Add files using upload-large-folder tool
Browse files- .gitattributes +2 -0
- README.md +1400 -3
- checkpoint-200/1_Pooling/config.json +10 -0
- checkpoint-200/README.md +1398 -0
- checkpoint-200/config.json +49 -0
- checkpoint-200/config_sentence_transformers.json +10 -0
- checkpoint-200/model.safetensors +3 -0
- checkpoint-200/modules.json +20 -0
- checkpoint-200/optimizer.pt +3 -0
- checkpoint-200/rng_state_0.pth +3 -0
- checkpoint-200/scaler.pt +3 -0
- checkpoint-200/scheduler.pt +3 -0
- checkpoint-200/sentence_bert_config.json +4 -0
- checkpoint-200/special_tokens_map.json +51 -0
- checkpoint-200/tokenizer.json +3 -0
- checkpoint-200/tokenizer_config.json +55 -0
- checkpoint-200/trainer_state.json +322 -0
- checkpoint-200/training_args.bin +3 -0
- checkpoint-400/1_Pooling/config.json +10 -0
- checkpoint-400/README.md +1400 -0
- checkpoint-400/config.json +49 -0
- checkpoint-400/config_sentence_transformers.json +10 -0
- checkpoint-400/model.safetensors +3 -0
- checkpoint-400/modules.json +20 -0
- checkpoint-400/optimizer.pt +3 -0
- checkpoint-400/rng_state_0.pth +3 -0
- checkpoint-400/scaler.pt +3 -0
- checkpoint-400/scheduler.pt +3 -0
- checkpoint-400/sentence_bert_config.json +4 -0
- checkpoint-400/special_tokens_map.json +51 -0
- checkpoint-400/tokenizer.json +3 -0
- checkpoint-400/tokenizer_config.json +55 -0
- checkpoint-400/trainer_state.json +603 -0
- checkpoint-400/training_args.bin +3 -0
- eval/Information-Retrieval_evaluation_full_de_results.csv +4 -0
- eval/Information-Retrieval_evaluation_full_en_results.csv +4 -0
- eval/Information-Retrieval_evaluation_full_es_results.csv +4 -0
- eval/Information-Retrieval_evaluation_full_zh_results.csv +4 -0
- eval/Information-Retrieval_evaluation_mix_de_results.csv +4 -0
- eval/Information-Retrieval_evaluation_mix_es_results.csv +4 -0
- eval/Information-Retrieval_evaluation_mix_zh_results.csv +4 -0
.gitattributes
CHANGED
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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
checkpoint-400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+
checkpoint-200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
CHANGED
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:124788
|
| 8 |
+
- loss:GISTEmbedLoss
|
| 9 |
+
base_model: Alibaba-NLP/gte-multilingual-base
|
| 10 |
+
widget:
|
| 11 |
+
- source_sentence: 其他机械、设备和有形货物租赁服务代表
|
| 12 |
+
sentences:
|
| 13 |
+
- 其他机械和设备租赁服务工作人员
|
| 14 |
+
- 电子和电信设备及零部件物流经理
|
| 15 |
+
- 工业主厨
|
| 16 |
+
- source_sentence: 公交车司机
|
| 17 |
+
sentences:
|
| 18 |
+
- 表演灯光设计师
|
| 19 |
+
- 乙烯基地板安装工
|
| 20 |
+
- 国际巴士司机
|
| 21 |
+
- source_sentence: online communication manager
|
| 22 |
+
sentences:
|
| 23 |
+
- trades union official
|
| 24 |
+
- social media manager
|
| 25 |
+
- budget manager
|
| 26 |
+
- source_sentence: Projektmanagerin
|
| 27 |
+
sentences:
|
| 28 |
+
- Projektmanager/Projektmanagerin
|
| 29 |
+
- Category-Manager
|
| 30 |
+
- Infanterist
|
| 31 |
+
- source_sentence: Volksvertreter
|
| 32 |
+
sentences:
|
| 33 |
+
- Parlamentarier
|
| 34 |
+
- Oberbürgermeister
|
| 35 |
+
- Konsul
|
| 36 |
+
pipeline_tag: sentence-similarity
|
| 37 |
+
library_name: sentence-transformers
|
| 38 |
+
metrics:
|
| 39 |
+
- cosine_accuracy@1
|
| 40 |
+
- cosine_accuracy@20
|
| 41 |
+
- cosine_accuracy@50
|
| 42 |
+
- cosine_accuracy@100
|
| 43 |
+
- cosine_accuracy@150
|
| 44 |
+
- cosine_accuracy@200
|
| 45 |
+
- cosine_precision@1
|
| 46 |
+
- cosine_precision@20
|
| 47 |
+
- cosine_precision@50
|
| 48 |
+
- cosine_precision@100
|
| 49 |
+
- cosine_precision@150
|
| 50 |
+
- cosine_precision@200
|
| 51 |
+
- cosine_recall@1
|
| 52 |
+
- cosine_recall@20
|
| 53 |
+
- cosine_recall@50
|
| 54 |
+
- cosine_recall@100
|
| 55 |
+
- cosine_recall@150
|
| 56 |
+
- cosine_recall@200
|
| 57 |
+
- cosine_ndcg@1
|
| 58 |
+
- cosine_ndcg@20
|
| 59 |
+
- cosine_ndcg@50
|
| 60 |
+
- cosine_ndcg@100
|
| 61 |
+
- cosine_ndcg@150
|
| 62 |
+
- cosine_ndcg@200
|
| 63 |
+
- cosine_mrr@1
|
| 64 |
+
- cosine_mrr@20
|
| 65 |
+
- cosine_mrr@50
|
| 66 |
+
- cosine_mrr@100
|
| 67 |
+
- cosine_mrr@150
|
| 68 |
+
- cosine_mrr@200
|
| 69 |
+
- cosine_map@1
|
| 70 |
+
- cosine_map@20
|
| 71 |
+
- cosine_map@50
|
| 72 |
+
- cosine_map@100
|
| 73 |
+
- cosine_map@150
|
| 74 |
+
- cosine_map@200
|
| 75 |
+
- cosine_map@500
|
| 76 |
+
model-index:
|
| 77 |
+
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
|
| 78 |
+
results:
|
| 79 |
+
- task:
|
| 80 |
+
type: information-retrieval
|
| 81 |
+
name: Information Retrieval
|
| 82 |
+
dataset:
|
| 83 |
+
name: full en
|
| 84 |
+
type: full_en
|
| 85 |
+
metrics:
|
| 86 |
+
- type: cosine_accuracy@1
|
| 87 |
+
value: 0.6666666666666666
|
| 88 |
+
name: Cosine Accuracy@1
|
| 89 |
+
- type: cosine_accuracy@20
|
| 90 |
+
value: 0.9904761904761905
|
| 91 |
+
name: Cosine Accuracy@20
|
| 92 |
+
- type: cosine_accuracy@50
|
| 93 |
+
value: 0.9904761904761905
|
| 94 |
+
name: Cosine Accuracy@50
|
| 95 |
+
- type: cosine_accuracy@100
|
| 96 |
+
value: 0.9904761904761905
|
| 97 |
+
name: Cosine Accuracy@100
|
| 98 |
+
- type: cosine_accuracy@150
|
| 99 |
+
value: 0.9904761904761905
|
| 100 |
+
name: Cosine Accuracy@150
|
| 101 |
+
- type: cosine_accuracy@200
|
| 102 |
+
value: 0.9904761904761905
|
| 103 |
+
name: Cosine Accuracy@200
|
| 104 |
+
- type: cosine_precision@1
|
| 105 |
+
value: 0.6666666666666666
|
| 106 |
+
name: Cosine Precision@1
|
| 107 |
+
- type: cosine_precision@20
|
| 108 |
+
value: 0.5147619047619048
|
| 109 |
+
name: Cosine Precision@20
|
| 110 |
+
- type: cosine_precision@50
|
| 111 |
+
value: 0.31999999999999995
|
| 112 |
+
name: Cosine Precision@50
|
| 113 |
+
- type: cosine_precision@100
|
| 114 |
+
value: 0.19047619047619047
|
| 115 |
+
name: Cosine Precision@100
|
| 116 |
+
- type: cosine_precision@150
|
| 117 |
+
value: 0.1361904761904762
|
| 118 |
+
name: Cosine Precision@150
|
| 119 |
+
- type: cosine_precision@200
|
| 120 |
+
value: 0.10542857142857143
|
| 121 |
+
name: Cosine Precision@200
|
| 122 |
+
- type: cosine_recall@1
|
| 123 |
+
value: 0.06854687410617222
|
| 124 |
+
name: Cosine Recall@1
|
| 125 |
+
- type: cosine_recall@20
|
| 126 |
+
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name: Cosine Ndcg@50
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| 857 |
+
- type: cosine_ndcg@100
|
| 858 |
+
value: 0.8167350090334409
|
| 859 |
+
name: Cosine Ndcg@100
|
| 860 |
+
- type: cosine_ndcg@150
|
| 861 |
+
value: 0.8181122471507385
|
| 862 |
+
name: Cosine Ndcg@150
|
| 863 |
+
- type: cosine_ndcg@200
|
| 864 |
+
value: 0.8186874070081017
|
| 865 |
+
name: Cosine Ndcg@200
|
| 866 |
+
- type: cosine_mrr@1
|
| 867 |
+
value: 0.7667014613778705
|
| 868 |
+
name: Cosine Mrr@1
|
| 869 |
+
- type: cosine_mrr@20
|
| 870 |
+
value: 0.8421752732824312
|
| 871 |
+
name: Cosine Mrr@20
|
| 872 |
+
- type: cosine_mrr@50
|
| 873 |
+
value: 0.8424954415974232
|
| 874 |
+
name: Cosine Mrr@50
|
| 875 |
+
- type: cosine_mrr@100
|
| 876 |
+
value: 0.8425358910333786
|
| 877 |
+
name: Cosine Mrr@100
|
| 878 |
+
- type: cosine_mrr@150
|
| 879 |
+
value: 0.8425483391786986
|
| 880 |
+
name: Cosine Mrr@150
|
| 881 |
+
- type: cosine_mrr@200
|
| 882 |
+
value: 0.8425515411459873
|
| 883 |
+
name: Cosine Mrr@200
|
| 884 |
+
- type: cosine_map@1
|
| 885 |
+
value: 0.7667014613778705
|
| 886 |
+
name: Cosine Map@1
|
| 887 |
+
- type: cosine_map@20
|
| 888 |
+
value: 0.7007206423896271
|
| 889 |
+
name: Cosine Map@20
|
| 890 |
+
- type: cosine_map@50
|
| 891 |
+
value: 0.7046277360194696
|
| 892 |
+
name: Cosine Map@50
|
| 893 |
+
- type: cosine_map@100
|
| 894 |
+
value: 0.7053668771050886
|
| 895 |
+
name: Cosine Map@100
|
| 896 |
+
- type: cosine_map@150
|
| 897 |
+
value: 0.7055166914145262
|
| 898 |
+
name: Cosine Map@150
|
| 899 |
+
- type: cosine_map@200
|
| 900 |
+
value: 0.7055658329670217
|
| 901 |
+
name: Cosine Map@200
|
| 902 |
+
- type: cosine_map@500
|
| 903 |
+
value: 0.7056512281794008
|
| 904 |
+
name: Cosine Map@500
|
| 905 |
+
---
|
| 906 |
+
|
| 907 |
+
# Job - Job matching Alibaba-NLP/gte-multilingual-base (v2)
|
| 908 |
+
|
| 909 |
+
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
|
| 910 |
+
|
| 911 |
+
## Model Details
|
| 912 |
+
|
| 913 |
+
### Model Description
|
| 914 |
+
- **Model Type:** Sentence Transformer
|
| 915 |
+
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
|
| 916 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 917 |
+
- **Output Dimensionality:** 768 dimensions
|
| 918 |
+
- **Similarity Function:** Cosine Similarity
|
| 919 |
+
- **Training Datasets:**
|
| 920 |
+
- full_en
|
| 921 |
+
- full_de
|
| 922 |
+
- full_es
|
| 923 |
+
- full_zh
|
| 924 |
+
- mix
|
| 925 |
+
<!-- - **Language:** Unknown -->
|
| 926 |
+
<!-- - **License:** Unknown -->
|
| 927 |
+
|
| 928 |
+
### Model Sources
|
| 929 |
+
|
| 930 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 931 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 932 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 933 |
+
|
| 934 |
+
### Full Model Architecture
|
| 935 |
+
|
| 936 |
+
```
|
| 937 |
+
SentenceTransformer(
|
| 938 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
|
| 939 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 940 |
+
(2): Normalize()
|
| 941 |
+
)
|
| 942 |
+
```
|
| 943 |
+
|
| 944 |
+
## Usage
|
| 945 |
+
|
| 946 |
+
### Direct Usage (Sentence Transformers)
|
| 947 |
+
|
| 948 |
+
First install the Sentence Transformers library:
|
| 949 |
+
|
| 950 |
+
```bash
|
| 951 |
+
pip install -U sentence-transformers
|
| 952 |
+
```
|
| 953 |
+
|
| 954 |
+
Then you can load this model and run inference.
|
| 955 |
+
```python
|
| 956 |
+
from sentence_transformers import SentenceTransformer
|
| 957 |
+
|
| 958 |
+
# Download from the 🤗 Hub
|
| 959 |
+
model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v2")
|
| 960 |
+
# Run inference
|
| 961 |
+
sentences = [
|
| 962 |
+
'Volksvertreter',
|
| 963 |
+
'Parlamentarier',
|
| 964 |
+
'Oberbürgermeister',
|
| 965 |
+
]
|
| 966 |
+
embeddings = model.encode(sentences)
|
| 967 |
+
print(embeddings.shape)
|
| 968 |
+
# [3, 768]
|
| 969 |
+
|
| 970 |
+
# Get the similarity scores for the embeddings
|
| 971 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 972 |
+
print(similarities.shape)
|
| 973 |
+
# [3, 3]
|
| 974 |
+
```
|
| 975 |
+
|
| 976 |
+
<!--
|
| 977 |
+
### Direct Usage (Transformers)
|
| 978 |
+
|
| 979 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 980 |
+
|
| 981 |
+
</details>
|
| 982 |
+
-->
|
| 983 |
+
|
| 984 |
+
<!--
|
| 985 |
+
### Downstream Usage (Sentence Transformers)
|
| 986 |
+
|
| 987 |
+
You can finetune this model on your own dataset.
|
| 988 |
+
|
| 989 |
+
<details><summary>Click to expand</summary>
|
| 990 |
+
|
| 991 |
+
</details>
|
| 992 |
+
-->
|
| 993 |
+
|
| 994 |
+
<!--
|
| 995 |
+
### Out-of-Scope Use
|
| 996 |
+
|
| 997 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 998 |
+
-->
|
| 999 |
+
|
| 1000 |
+
## Evaluation
|
| 1001 |
+
|
| 1002 |
+
### Metrics
|
| 1003 |
+
|
| 1004 |
+
#### Information Retrieval
|
| 1005 |
+
|
| 1006 |
+
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
|
| 1007 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 1008 |
+
|
| 1009 |
+
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|
| 1010 |
+
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
|
| 1011 |
+
| cosine_accuracy@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1012 |
+
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9754 | 0.9806 | 0.9553 | 0.9324 | 0.9843 |
|
| 1013 |
+
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9802 | 0.9683 | 0.9932 |
|
| 1014 |
+
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9849 | 0.9958 |
|
| 1015 |
+
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9938 | 0.9886 | 0.9974 |
|
| 1016 |
+
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9938 | 0.9979 |
|
| 1017 |
+
| cosine_precision@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1018 |
+
| cosine_precision@20 | 0.5148 | 0.5759 | 0.5103 | 0.4883 | 0.1216 | 0.1209 | 0.1387 |
|
| 1019 |
+
| cosine_precision@50 | 0.32 | 0.3923 | 0.3694 | 0.2963 | 0.0512 | 0.0514 | 0.0581 |
|
| 1020 |
+
| cosine_precision@100 | 0.1905 | 0.2566 | 0.2397 | 0.1788 | 0.0261 | 0.0265 | 0.0296 |
|
| 1021 |
+
| cosine_precision@150 | 0.1362 | 0.1928 | 0.1808 | 0.1278 | 0.0175 | 0.0179 | 0.0199 |
|
| 1022 |
+
| cosine_precision@200 | 0.1054 | 0.1528 | 0.1462 | 0.0999 | 0.0132 | 0.0135 | 0.0149 |
|
| 1023 |
+
| cosine_recall@1 | 0.0685 | 0.0036 | 0.0111 | 0.0693 | 0.2738 | 0.2436 | 0.2569 |
|
| 1024 |
+
| cosine_recall@20 | 0.5491 | 0.3853 | 0.3208 | 0.5251 | 0.899 | 0.8787 | 0.9157 |
|
| 1025 |
+
| cosine_recall@50 | 0.7554 | 0.566 | 0.5042 | 0.7083 | 0.9459 | 0.932 | 0.9583 |
|
| 1026 |
+
| cosine_recall@100 | 0.8503 | 0.6899 | 0.6173 | 0.8169 | 0.9651 | 0.9596 | 0.9765 |
|
| 1027 |
+
| cosine_recall@150 | 0.8995 | 0.754 | 0.6848 | 0.8613 | 0.9732 | 0.9718 | 0.9834 |
|
| 1028 |
+
| cosine_recall@200 | 0.9208 | 0.7858 | 0.7253 | 0.8898 | 0.9791 | 0.98 | 0.9865 |
|
| 1029 |
+
| cosine_ndcg@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1030 |
+
| cosine_ndcg@20 | 0.6952 | 0.6169 | 0.5378 | 0.6681 | 0.7815 | 0.7448 | 0.8002 |
|
| 1031 |
+
| cosine_ndcg@50 | 0.723 | 0.5914 | 0.5288 | 0.6857 | 0.7944 | 0.7595 | 0.8125 |
|
| 1032 |
+
| cosine_ndcg@100 | 0.7733 | 0.6235 | 0.5552 | 0.7379 | 0.7986 | 0.7657 | 0.8167 |
|
| 1033 |
+
| cosine_ndcg@150 | 0.7947 | 0.6557 | 0.5888 | 0.7577 | 0.8001 | 0.7682 | 0.8181 |
|
| 1034 |
+
| **cosine_ndcg@200** | **0.8039** | **0.6717** | **0.6092** | **0.7697** | **0.8012** | **0.7696** | **0.8187** |
|
| 1035 |
+
| cosine_mrr@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1036 |
+
| cosine_mrr@20 | 0.8183 | 0.5581 | 0.5165 | 0.8159 | 0.7804 | 0.7324 | 0.8422 |
|
| 1037 |
+
| cosine_mrr@50 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7813 | 0.7335 | 0.8425 |
|
| 1038 |
+
| cosine_mrr@100 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 |
|
| 1039 |
+
| cosine_mrr@150 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 |
|
| 1040 |
+
| cosine_mrr@200 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8426 |
|
| 1041 |
+
| cosine_map@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1042 |
+
| cosine_map@20 | 0.5566 | 0.4841 | 0.3984 | 0.5222 | 0.7071 | 0.6646 | 0.7007 |
|
| 1043 |
+
| cosine_map@50 | 0.5534 | 0.4304 | 0.3603 | 0.5083 | 0.7107 | 0.6684 | 0.7046 |
|
| 1044 |
+
| cosine_map@100 | 0.5852 | 0.4374 | 0.3632 | 0.5372 | 0.7113 | 0.6693 | 0.7054 |
|
| 1045 |
+
| cosine_map@150 | 0.5943 | 0.4527 | 0.3782 | 0.5454 | 0.7114 | 0.6695 | 0.7055 |
|
| 1046 |
+
| cosine_map@200 | 0.5976 | 0.4593 | 0.3863 | 0.5495 | 0.7115 | 0.6696 | 0.7056 |
|
| 1047 |
+
| cosine_map@500 | 0.6016 | 0.472 | 0.3992 | 0.5542 | 0.7116 | 0.6697 | 0.7057 |
|
| 1048 |
+
|
| 1049 |
+
<!--
|
| 1050 |
+
## Bias, Risks and Limitations
|
| 1051 |
+
|
| 1052 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 1053 |
+
-->
|
| 1054 |
+
|
| 1055 |
+
<!--
|
| 1056 |
+
### Recommendations
|
| 1057 |
+
|
| 1058 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 1059 |
+
-->
|
| 1060 |
+
|
| 1061 |
+
## Training Details
|
| 1062 |
+
|
| 1063 |
+
### Training Datasets
|
| 1064 |
+
<details><summary>full_en</summary>
|
| 1065 |
+
|
| 1066 |
+
#### full_en
|
| 1067 |
+
|
| 1068 |
+
* Dataset: full_en
|
| 1069 |
+
* Size: 28,880 training samples
|
| 1070 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1071 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1072 |
+
| | anchor | positive |
|
| 1073 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1074 |
+
| type | string | string |
|
| 1075 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
|
| 1076 |
+
* Samples:
|
| 1077 |
+
| anchor | positive |
|
| 1078 |
+
|:-----------------------------------------|:-----------------------------------------|
|
| 1079 |
+
| <code>air commodore</code> | <code>flight lieutenant</code> |
|
| 1080 |
+
| <code>command and control officer</code> | <code>flight officer</code> |
|
| 1081 |
+
| <code>air commodore</code> | <code>command and control officer</code> |
|
| 1082 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1083 |
+
```json
|
| 1084 |
+
{'guide': SentenceTransformer(
|
| 1085 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1086 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1087 |
+
(2): Normalize()
|
| 1088 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1089 |
+
```
|
| 1090 |
+
</details>
|
| 1091 |
+
<details><summary>full_de</summary>
|
| 1092 |
+
|
| 1093 |
+
#### full_de
|
| 1094 |
+
|
| 1095 |
+
* Dataset: full_de
|
| 1096 |
+
* Size: 23,023 training samples
|
| 1097 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1098 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1099 |
+
| | anchor | positive |
|
| 1100 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1101 |
+
| type | string | string |
|
| 1102 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
|
| 1103 |
+
* Samples:
|
| 1104 |
+
| anchor | positive |
|
| 1105 |
+
|:----------------------------------|:-----------------------------------------------------|
|
| 1106 |
+
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
|
| 1107 |
+
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
|
| 1108 |
+
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
|
| 1109 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1110 |
+
```json
|
| 1111 |
+
{'guide': SentenceTransformer(
|
| 1112 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1113 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1114 |
+
(2): Normalize()
|
| 1115 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1116 |
+
```
|
| 1117 |
+
</details>
|
| 1118 |
+
<details><summary>full_es</summary>
|
| 1119 |
+
|
| 1120 |
+
#### full_es
|
| 1121 |
+
|
| 1122 |
+
* Dataset: full_es
|
| 1123 |
+
* Size: 20,724 training samples
|
| 1124 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1125 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1126 |
+
| | anchor | positive |
|
| 1127 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1128 |
+
| type | string | string |
|
| 1129 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
|
| 1130 |
+
* Samples:
|
| 1131 |
+
| anchor | positive |
|
| 1132 |
+
|:------------------------------------|:-------------------------------------------|
|
| 1133 |
+
| <code>jefe de escuadrón</code> | <code>instructor</code> |
|
| 1134 |
+
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
|
| 1135 |
+
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
|
| 1136 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1137 |
+
```json
|
| 1138 |
+
{'guide': SentenceTransformer(
|
| 1139 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1140 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1141 |
+
(2): Normalize()
|
| 1142 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1143 |
+
```
|
| 1144 |
+
</details>
|
| 1145 |
+
<details><summary>full_zh</summary>
|
| 1146 |
+
|
| 1147 |
+
#### full_zh
|
| 1148 |
+
|
| 1149 |
+
* Dataset: full_zh
|
| 1150 |
+
* Size: 30,401 training samples
|
| 1151 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1152 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1153 |
+
| | anchor | positive |
|
| 1154 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1155 |
+
| type | string | string |
|
| 1156 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
|
| 1157 |
+
* Samples:
|
| 1158 |
+
| anchor | positive |
|
| 1159 |
+
|:------------------|:---------------------|
|
| 1160 |
+
| <code>技术总监</code> | <code>技术和运营总监</code> |
|
| 1161 |
+
| <code>技术总监</code> | <code>技术主管</code> |
|
| 1162 |
+
| <code>技术总监</code> | <code>技术艺术总监</code> |
|
| 1163 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1164 |
+
```json
|
| 1165 |
+
{'guide': SentenceTransformer(
|
| 1166 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1167 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1168 |
+
(2): Normalize()
|
| 1169 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1170 |
+
```
|
| 1171 |
+
</details>
|
| 1172 |
+
<details><summary>mix</summary>
|
| 1173 |
+
|
| 1174 |
+
#### mix
|
| 1175 |
+
|
| 1176 |
+
* Dataset: mix
|
| 1177 |
+
* Size: 21,760 training samples
|
| 1178 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1179 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1180 |
+
| | anchor | positive |
|
| 1181 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1182 |
+
| type | string | string |
|
| 1183 |
+
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
|
| 1184 |
+
* Samples:
|
| 1185 |
+
| anchor | positive |
|
| 1186 |
+
|:------------------------------------------|:----------------------------------------------------------------|
|
| 1187 |
+
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
|
| 1188 |
+
| <code>head of technical</code> | <code>directora técnica</code> |
|
| 1189 |
+
| <code>head of technical department</code> | <code>技术艺术总监</code> |
|
| 1190 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1191 |
+
```json
|
| 1192 |
+
{'guide': SentenceTransformer(
|
| 1193 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1194 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1195 |
+
(2): Normalize()
|
| 1196 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1197 |
+
```
|
| 1198 |
+
</details>
|
| 1199 |
+
|
| 1200 |
+
### Training Hyperparameters
|
| 1201 |
+
#### Non-Default Hyperparameters
|
| 1202 |
+
|
| 1203 |
+
- `eval_strategy`: steps
|
| 1204 |
+
- `per_device_train_batch_size`: 128
|
| 1205 |
+
- `per_device_eval_batch_size`: 128
|
| 1206 |
+
- `gradient_accumulation_steps`: 2
|
| 1207 |
+
- `num_train_epochs`: 5
|
| 1208 |
+
- `warmup_ratio`: 0.05
|
| 1209 |
+
- `log_on_each_node`: False
|
| 1210 |
+
- `fp16`: True
|
| 1211 |
+
- `dataloader_num_workers`: 4
|
| 1212 |
+
- `ddp_find_unused_parameters`: True
|
| 1213 |
+
- `batch_sampler`: no_duplicates
|
| 1214 |
+
|
| 1215 |
+
#### All Hyperparameters
|
| 1216 |
+
<details><summary>Click to expand</summary>
|
| 1217 |
+
|
| 1218 |
+
- `overwrite_output_dir`: False
|
| 1219 |
+
- `do_predict`: False
|
| 1220 |
+
- `eval_strategy`: steps
|
| 1221 |
+
- `prediction_loss_only`: True
|
| 1222 |
+
- `per_device_train_batch_size`: 128
|
| 1223 |
+
- `per_device_eval_batch_size`: 128
|
| 1224 |
+
- `per_gpu_train_batch_size`: None
|
| 1225 |
+
- `per_gpu_eval_batch_size`: None
|
| 1226 |
+
- `gradient_accumulation_steps`: 2
|
| 1227 |
+
- `eval_accumulation_steps`: None
|
| 1228 |
+
- `torch_empty_cache_steps`: None
|
| 1229 |
+
- `learning_rate`: 5e-05
|
| 1230 |
+
- `weight_decay`: 0.0
|
| 1231 |
+
- `adam_beta1`: 0.9
|
| 1232 |
+
- `adam_beta2`: 0.999
|
| 1233 |
+
- `adam_epsilon`: 1e-08
|
| 1234 |
+
- `max_grad_norm`: 1.0
|
| 1235 |
+
- `num_train_epochs`: 5
|
| 1236 |
+
- `max_steps`: -1
|
| 1237 |
+
- `lr_scheduler_type`: linear
|
| 1238 |
+
- `lr_scheduler_kwargs`: {}
|
| 1239 |
+
- `warmup_ratio`: 0.05
|
| 1240 |
+
- `warmup_steps`: 0
|
| 1241 |
+
- `log_level`: passive
|
| 1242 |
+
- `log_level_replica`: warning
|
| 1243 |
+
- `log_on_each_node`: False
|
| 1244 |
+
- `logging_nan_inf_filter`: True
|
| 1245 |
+
- `save_safetensors`: True
|
| 1246 |
+
- `save_on_each_node`: False
|
| 1247 |
+
- `save_only_model`: False
|
| 1248 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 1249 |
+
- `no_cuda`: False
|
| 1250 |
+
- `use_cpu`: False
|
| 1251 |
+
- `use_mps_device`: False
|
| 1252 |
+
- `seed`: 42
|
| 1253 |
+
- `data_seed`: None
|
| 1254 |
+
- `jit_mode_eval`: False
|
| 1255 |
+
- `use_ipex`: False
|
| 1256 |
+
- `bf16`: False
|
| 1257 |
+
- `fp16`: True
|
| 1258 |
+
- `fp16_opt_level`: O1
|
| 1259 |
+
- `half_precision_backend`: auto
|
| 1260 |
+
- `bf16_full_eval`: False
|
| 1261 |
+
- `fp16_full_eval`: False
|
| 1262 |
+
- `tf32`: None
|
| 1263 |
+
- `local_rank`: 0
|
| 1264 |
+
- `ddp_backend`: None
|
| 1265 |
+
- `tpu_num_cores`: None
|
| 1266 |
+
- `tpu_metrics_debug`: False
|
| 1267 |
+
- `debug`: []
|
| 1268 |
+
- `dataloader_drop_last`: True
|
| 1269 |
+
- `dataloader_num_workers`: 4
|
| 1270 |
+
- `dataloader_prefetch_factor`: None
|
| 1271 |
+
- `past_index`: -1
|
| 1272 |
+
- `disable_tqdm`: False
|
| 1273 |
+
- `remove_unused_columns`: True
|
| 1274 |
+
- `label_names`: None
|
| 1275 |
+
- `load_best_model_at_end`: False
|
| 1276 |
+
- `ignore_data_skip`: False
|
| 1277 |
+
- `fsdp`: []
|
| 1278 |
+
- `fsdp_min_num_params`: 0
|
| 1279 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 1280 |
+
- `tp_size`: 0
|
| 1281 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 1282 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 1283 |
+
- `deepspeed`: None
|
| 1284 |
+
- `label_smoothing_factor`: 0.0
|
| 1285 |
+
- `optim`: adamw_torch
|
| 1286 |
+
- `optim_args`: None
|
| 1287 |
+
- `adafactor`: False
|
| 1288 |
+
- `group_by_length`: False
|
| 1289 |
+
- `length_column_name`: length
|
| 1290 |
+
- `ddp_find_unused_parameters`: True
|
| 1291 |
+
- `ddp_bucket_cap_mb`: None
|
| 1292 |
+
- `ddp_broadcast_buffers`: False
|
| 1293 |
+
- `dataloader_pin_memory`: True
|
| 1294 |
+
- `dataloader_persistent_workers`: False
|
| 1295 |
+
- `skip_memory_metrics`: True
|
| 1296 |
+
- `use_legacy_prediction_loop`: False
|
| 1297 |
+
- `push_to_hub`: False
|
| 1298 |
+
- `resume_from_checkpoint`: None
|
| 1299 |
+
- `hub_model_id`: None
|
| 1300 |
+
- `hub_strategy`: every_save
|
| 1301 |
+
- `hub_private_repo`: None
|
| 1302 |
+
- `hub_always_push`: False
|
| 1303 |
+
- `gradient_checkpointing`: False
|
| 1304 |
+
- `gradient_checkpointing_kwargs`: None
|
| 1305 |
+
- `include_inputs_for_metrics`: False
|
| 1306 |
+
- `include_for_metrics`: []
|
| 1307 |
+
- `eval_do_concat_batches`: True
|
| 1308 |
+
- `fp16_backend`: auto
|
| 1309 |
+
- `push_to_hub_model_id`: None
|
| 1310 |
+
- `push_to_hub_organization`: None
|
| 1311 |
+
- `mp_parameters`:
|
| 1312 |
+
- `auto_find_batch_size`: False
|
| 1313 |
+
- `full_determinism`: False
|
| 1314 |
+
- `torchdynamo`: None
|
| 1315 |
+
- `ray_scope`: last
|
| 1316 |
+
- `ddp_timeout`: 1800
|
| 1317 |
+
- `torch_compile`: False
|
| 1318 |
+
- `torch_compile_backend`: None
|
| 1319 |
+
- `torch_compile_mode`: None
|
| 1320 |
+
- `include_tokens_per_second`: False
|
| 1321 |
+
- `include_num_input_tokens_seen`: False
|
| 1322 |
+
- `neftune_noise_alpha`: None
|
| 1323 |
+
- `optim_target_modules`: None
|
| 1324 |
+
- `batch_eval_metrics`: False
|
| 1325 |
+
- `eval_on_start`: False
|
| 1326 |
+
- `use_liger_kernel`: False
|
| 1327 |
+
- `eval_use_gather_object`: False
|
| 1328 |
+
- `average_tokens_across_devices`: False
|
| 1329 |
+
- `prompts`: None
|
| 1330 |
+
- `batch_sampler`: no_duplicates
|
| 1331 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 1332 |
+
|
| 1333 |
+
</details>
|
| 1334 |
+
|
| 1335 |
+
### Training Logs
|
| 1336 |
+
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|
| 1337 |
+
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
|
| 1338 |
+
| -1 | -1 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.7437 |
|
| 1339 |
+
| 0.0082 | 1 | 4.3088 | - | - | - | - | - | - | - |
|
| 1340 |
+
| 0.8230 | 100 | 1.9026 | - | - | - | - | - | - | - |
|
| 1341 |
+
| 1.6502 | 200 | 0.9336 | 0.8024 | 0.6703 | 0.6109 | 0.7695 | 0.7914 | 0.7594 | 0.8136 |
|
| 1342 |
+
| 2.4774 | 300 | 0.161 | - | - | - | - | - | - | - |
|
| 1343 |
+
| 3.3045 | 400 | 0.1398 | 0.8039 | 0.6717 | 0.6092 | 0.7697 | 0.8012 | 0.7696 | 0.8187 |
|
| 1344 |
+
|
| 1345 |
+
|
| 1346 |
+
### Framework Versions
|
| 1347 |
+
- Python: 3.11.11
|
| 1348 |
+
- Sentence Transformers: 4.1.0
|
| 1349 |
+
- Transformers: 4.51.2
|
| 1350 |
+
- PyTorch: 2.6.0+cu124
|
| 1351 |
+
- Accelerate: 1.6.0
|
| 1352 |
+
- Datasets: 3.5.0
|
| 1353 |
+
- Tokenizers: 0.21.1
|
| 1354 |
+
|
| 1355 |
+
## Citation
|
| 1356 |
+
|
| 1357 |
+
### BibTeX
|
| 1358 |
+
|
| 1359 |
+
#### Sentence Transformers
|
| 1360 |
+
```bibtex
|
| 1361 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 1362 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 1363 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 1364 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 1365 |
+
month = "11",
|
| 1366 |
+
year = "2019",
|
| 1367 |
+
publisher = "Association for Computational Linguistics",
|
| 1368 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 1369 |
+
}
|
| 1370 |
+
```
|
| 1371 |
+
|
| 1372 |
+
#### GISTEmbedLoss
|
| 1373 |
+
```bibtex
|
| 1374 |
+
@misc{solatorio2024gistembed,
|
| 1375 |
+
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
|
| 1376 |
+
author={Aivin V. Solatorio},
|
| 1377 |
+
year={2024},
|
| 1378 |
+
eprint={2402.16829},
|
| 1379 |
+
archivePrefix={arXiv},
|
| 1380 |
+
primaryClass={cs.LG}
|
| 1381 |
+
}
|
| 1382 |
+
```
|
| 1383 |
+
|
| 1384 |
+
<!--
|
| 1385 |
+
## Glossary
|
| 1386 |
+
|
| 1387 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 1388 |
+
-->
|
| 1389 |
+
|
| 1390 |
+
<!--
|
| 1391 |
+
## Model Card Authors
|
| 1392 |
+
|
| 1393 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 1394 |
+
-->
|
| 1395 |
+
|
| 1396 |
+
<!--
|
| 1397 |
+
## Model Card Contact
|
| 1398 |
+
|
| 1399 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 1400 |
+
-->
|
checkpoint-200/1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"word_embedding_dimension": 768,
|
| 3 |
+
"pooling_mode_cls_token": true,
|
| 4 |
+
"pooling_mode_mean_tokens": false,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": false,
|
| 9 |
+
"include_prompt": true
|
| 10 |
+
}
|
checkpoint-200/README.md
ADDED
|
@@ -0,0 +1,1398 @@
|
|
|
|
|
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:124788
|
| 8 |
+
- loss:GISTEmbedLoss
|
| 9 |
+
base_model: Alibaba-NLP/gte-multilingual-base
|
| 10 |
+
widget:
|
| 11 |
+
- source_sentence: 其他机械、设备和有形货物租赁服务代表
|
| 12 |
+
sentences:
|
| 13 |
+
- 其他机械和设备租赁服务工作人员
|
| 14 |
+
- 电子和电信设备及零部件物流经理
|
| 15 |
+
- 工业主厨
|
| 16 |
+
- source_sentence: 公交车司机
|
| 17 |
+
sentences:
|
| 18 |
+
- 表演灯光设计师
|
| 19 |
+
- 乙烯基地板安装工
|
| 20 |
+
- 国际巴士司机
|
| 21 |
+
- source_sentence: online communication manager
|
| 22 |
+
sentences:
|
| 23 |
+
- trades union official
|
| 24 |
+
- social media manager
|
| 25 |
+
- budget manager
|
| 26 |
+
- source_sentence: Projektmanagerin
|
| 27 |
+
sentences:
|
| 28 |
+
- Projektmanager/Projektmanagerin
|
| 29 |
+
- Category-Manager
|
| 30 |
+
- Infanterist
|
| 31 |
+
- source_sentence: Volksvertreter
|
| 32 |
+
sentences:
|
| 33 |
+
- Parlamentarier
|
| 34 |
+
- Oberbürgermeister
|
| 35 |
+
- Konsul
|
| 36 |
+
pipeline_tag: sentence-similarity
|
| 37 |
+
library_name: sentence-transformers
|
| 38 |
+
metrics:
|
| 39 |
+
- cosine_accuracy@1
|
| 40 |
+
- cosine_accuracy@20
|
| 41 |
+
- cosine_accuracy@50
|
| 42 |
+
- cosine_accuracy@100
|
| 43 |
+
- cosine_accuracy@150
|
| 44 |
+
- cosine_accuracy@200
|
| 45 |
+
- cosine_precision@1
|
| 46 |
+
- cosine_precision@20
|
| 47 |
+
- cosine_precision@50
|
| 48 |
+
- cosine_precision@100
|
| 49 |
+
- cosine_precision@150
|
| 50 |
+
- cosine_precision@200
|
| 51 |
+
- cosine_recall@1
|
| 52 |
+
- cosine_recall@20
|
| 53 |
+
- cosine_recall@50
|
| 54 |
+
- cosine_recall@100
|
| 55 |
+
- cosine_recall@150
|
| 56 |
+
- cosine_recall@200
|
| 57 |
+
- cosine_ndcg@1
|
| 58 |
+
- cosine_ndcg@20
|
| 59 |
+
- cosine_ndcg@50
|
| 60 |
+
- cosine_ndcg@100
|
| 61 |
+
- cosine_ndcg@150
|
| 62 |
+
- cosine_ndcg@200
|
| 63 |
+
- cosine_mrr@1
|
| 64 |
+
- cosine_mrr@20
|
| 65 |
+
- cosine_mrr@50
|
| 66 |
+
- cosine_mrr@100
|
| 67 |
+
- cosine_mrr@150
|
| 68 |
+
- cosine_mrr@200
|
| 69 |
+
- cosine_map@1
|
| 70 |
+
- cosine_map@20
|
| 71 |
+
- cosine_map@50
|
| 72 |
+
- cosine_map@100
|
| 73 |
+
- cosine_map@150
|
| 74 |
+
- cosine_map@200
|
| 75 |
+
- cosine_map@500
|
| 76 |
+
model-index:
|
| 77 |
+
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
|
| 78 |
+
results:
|
| 79 |
+
- task:
|
| 80 |
+
type: information-retrieval
|
| 81 |
+
name: Information Retrieval
|
| 82 |
+
dataset:
|
| 83 |
+
name: full en
|
| 84 |
+
type: full_en
|
| 85 |
+
metrics:
|
| 86 |
+
- type: cosine_accuracy@1
|
| 87 |
+
value: 0.6571428571428571
|
| 88 |
+
name: Cosine Accuracy@1
|
| 89 |
+
- type: cosine_accuracy@20
|
| 90 |
+
value: 0.9904761904761905
|
| 91 |
+
name: Cosine Accuracy@20
|
| 92 |
+
- type: cosine_accuracy@50
|
| 93 |
+
value: 0.9904761904761905
|
| 94 |
+
name: Cosine Accuracy@50
|
| 95 |
+
- type: cosine_accuracy@100
|
| 96 |
+
value: 0.9904761904761905
|
| 97 |
+
name: Cosine Accuracy@100
|
| 98 |
+
- type: cosine_accuracy@150
|
| 99 |
+
value: 0.9904761904761905
|
| 100 |
+
name: Cosine Accuracy@150
|
| 101 |
+
- type: cosine_accuracy@200
|
| 102 |
+
value: 0.9904761904761905
|
| 103 |
+
name: Cosine Accuracy@200
|
| 104 |
+
- type: cosine_precision@1
|
| 105 |
+
value: 0.6571428571428571
|
| 106 |
+
name: Cosine Precision@1
|
| 107 |
+
- type: cosine_precision@20
|
| 108 |
+
value: 0.5133333333333332
|
| 109 |
+
name: Cosine Precision@20
|
| 110 |
+
- type: cosine_precision@50
|
| 111 |
+
value: 0.3169523809523809
|
| 112 |
+
name: Cosine Precision@50
|
| 113 |
+
- type: cosine_precision@100
|
| 114 |
+
value: 0.18971428571428572
|
| 115 |
+
name: Cosine Precision@100
|
| 116 |
+
- type: cosine_precision@150
|
| 117 |
+
value: 0.13619047619047617
|
| 118 |
+
name: Cosine Precision@150
|
| 119 |
+
- type: cosine_precision@200
|
| 120 |
+
value: 0.10561904761904761
|
| 121 |
+
name: Cosine Precision@200
|
| 122 |
+
- type: cosine_recall@1
|
| 123 |
+
value: 0.06695957251887064
|
| 124 |
+
name: Cosine Recall@1
|
| 125 |
+
- type: cosine_recall@20
|
| 126 |
+
value: 0.5478306503729546
|
| 127 |
+
name: Cosine Recall@20
|
| 128 |
+
- type: cosine_recall@50
|
| 129 |
+
value: 0.7470276357469449
|
| 130 |
+
name: Cosine Recall@50
|
| 131 |
+
- type: cosine_recall@100
|
| 132 |
+
value: 0.8467073011936345
|
| 133 |
+
name: Cosine Recall@100
|
| 134 |
+
- type: cosine_recall@150
|
| 135 |
+
value: 0.9010846211520122
|
| 136 |
+
name: Cosine Recall@150
|
| 137 |
+
- type: cosine_recall@200
|
| 138 |
+
value: 0.9256595392715059
|
| 139 |
+
name: Cosine Recall@200
|
| 140 |
+
- type: cosine_ndcg@1
|
| 141 |
+
value: 0.6571428571428571
|
| 142 |
+
name: Cosine Ndcg@1
|
| 143 |
+
- type: cosine_ndcg@20
|
| 144 |
+
value: 0.6923506957704934
|
| 145 |
+
name: Cosine Ndcg@20
|
| 146 |
+
- type: cosine_ndcg@50
|
| 147 |
+
value: 0.7170311913169547
|
| 148 |
+
name: Cosine Ndcg@50
|
| 149 |
+
- type: cosine_ndcg@100
|
| 150 |
+
value: 0.7690946845916871
|
| 151 |
+
name: Cosine Ndcg@100
|
| 152 |
+
- type: cosine_ndcg@150
|
| 153 |
+
value: 0.7923061459636489
|
| 154 |
+
name: Cosine Ndcg@150
|
| 155 |
+
- type: cosine_ndcg@200
|
| 156 |
+
value: 0.8023952171736648
|
| 157 |
+
name: Cosine Ndcg@200
|
| 158 |
+
- type: cosine_mrr@1
|
| 159 |
+
value: 0.6571428571428571
|
| 160 |
+
name: Cosine Mrr@1
|
| 161 |
+
- type: cosine_mrr@20
|
| 162 |
+
value: 0.8111111111111111
|
| 163 |
+
name: Cosine Mrr@20
|
| 164 |
+
- type: cosine_mrr@50
|
| 165 |
+
value: 0.8111111111111111
|
| 166 |
+
name: Cosine Mrr@50
|
| 167 |
+
- type: cosine_mrr@100
|
| 168 |
+
value: 0.8111111111111111
|
| 169 |
+
name: Cosine Mrr@100
|
| 170 |
+
- type: cosine_mrr@150
|
| 171 |
+
value: 0.8111111111111111
|
| 172 |
+
name: Cosine Mrr@150
|
| 173 |
+
- type: cosine_mrr@200
|
| 174 |
+
value: 0.8111111111111111
|
| 175 |
+
name: Cosine Mrr@200
|
| 176 |
+
- type: cosine_map@1
|
| 177 |
+
value: 0.6571428571428571
|
| 178 |
+
name: Cosine Map@1
|
| 179 |
+
- type: cosine_map@20
|
| 180 |
+
value: 0.5516314386214587
|
| 181 |
+
name: Cosine Map@20
|
| 182 |
+
- type: cosine_map@50
|
| 183 |
+
value: 0.5474217433291914
|
| 184 |
+
name: Cosine Map@50
|
| 185 |
+
- type: cosine_map@100
|
| 186 |
+
value: 0.5799091076338031
|
| 187 |
+
name: Cosine Map@100
|
| 188 |
+
- type: cosine_map@150
|
| 189 |
+
value: 0.5895042547793764
|
| 190 |
+
name: Cosine Map@150
|
| 191 |
+
- type: cosine_map@200
|
| 192 |
+
value: 0.5930550248640567
|
| 193 |
+
name: Cosine Map@200
|
| 194 |
+
- type: cosine_map@500
|
| 195 |
+
value: 0.5967311945998978
|
| 196 |
+
name: Cosine Map@500
|
| 197 |
+
- task:
|
| 198 |
+
type: information-retrieval
|
| 199 |
+
name: Information Retrieval
|
| 200 |
+
dataset:
|
| 201 |
+
name: full es
|
| 202 |
+
type: full_es
|
| 203 |
+
metrics:
|
| 204 |
+
- type: cosine_accuracy@1
|
| 205 |
+
value: 0.11891891891891893
|
| 206 |
+
name: Cosine Accuracy@1
|
| 207 |
+
- type: cosine_accuracy@20
|
| 208 |
+
value: 1.0
|
| 209 |
+
name: Cosine Accuracy@20
|
| 210 |
+
- type: cosine_accuracy@50
|
| 211 |
+
value: 1.0
|
| 212 |
+
name: Cosine Accuracy@50
|
| 213 |
+
- type: cosine_accuracy@100
|
| 214 |
+
value: 1.0
|
| 215 |
+
name: Cosine Accuracy@100
|
| 216 |
+
- type: cosine_accuracy@150
|
| 217 |
+
value: 1.0
|
| 218 |
+
name: Cosine Accuracy@150
|
| 219 |
+
- type: cosine_accuracy@200
|
| 220 |
+
value: 1.0
|
| 221 |
+
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name: Cosine Precision@150
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| 591 |
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- type: cosine_precision@200
|
| 592 |
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value: 0.013198127925117008
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| 593 |
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name: Cosine Precision@200
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- type: cosine_recall@1
|
| 595 |
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value: 0.27067577941212884
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| 596 |
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name: Cosine Recall@1
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- type: cosine_recall@20
|
| 598 |
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value: 0.8850840700294678
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| 599 |
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name: Cosine Recall@20
|
| 600 |
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- type: cosine_recall@50
|
| 601 |
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value: 0.9390968972092216
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| 602 |
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name: Cosine Recall@50
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| 603 |
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- type: cosine_recall@100
|
| 604 |
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value: 0.9599497313225862
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| 605 |
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name: Cosine Recall@100
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- type: cosine_recall@150
|
| 607 |
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value: 0.9695527821112844
|
| 608 |
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name: Cosine Recall@150
|
| 609 |
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- type: cosine_recall@200
|
| 610 |
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value: 0.9758970358814353
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| 611 |
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name: Cosine Recall@200
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- type: cosine_ndcg@1
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| 613 |
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value: 0.7009880395215808
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| 614 |
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name: Cosine Ndcg@1
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- type: cosine_ndcg@20
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| 616 |
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value: 0.7690336236998598
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| 617 |
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name: Cosine Ndcg@20
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- type: cosine_ndcg@50
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| 619 |
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value: 0.7838732562697655
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| 620 |
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name: Cosine Ndcg@50
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- type: cosine_ndcg@100
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value: 0.7884317468596705
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| 623 |
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name: Cosine Ndcg@100
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- type: cosine_ndcg@150
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| 625 |
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value: 0.7902844804245556
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| 626 |
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name: Cosine Ndcg@150
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- type: cosine_ndcg@200
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value: 0.7913994944724545
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name: Cosine Ndcg@200
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- type: cosine_mrr@1
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value: 0.7009880395215808
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name: Cosine Mrr@1
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- type: cosine_mrr@20
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| 634 |
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value: 0.7712491671812917
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| 635 |
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name: Cosine Mrr@20
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value: 0.7722842539435679
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name: Cosine Mrr@50
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value: 0.7724347923967887
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name: Cosine Mrr@100
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value: 0.7724644404043258
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name: Cosine Mrr@150
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value: 0.7724705526191206
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name: Cosine Mrr@200
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- type: cosine_map@1
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value: 0.7009880395215808
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| 650 |
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name: Cosine Map@1
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- type: cosine_map@20
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value: 0.6938173897965141
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name: Cosine Map@20
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value: 0.6978248868009254
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name: Cosine Map@50
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value: 0.6984889579958145
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name: Cosine Map@100
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value: 0.6986621032108891
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name: Cosine Map@150
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value: 0.6987465392575996
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name: Cosine Map@200
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value: 0.6988876342368443
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name: Cosine Map@500
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- task:
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| 670 |
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type: information-retrieval
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| 671 |
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name: Information Retrieval
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| 672 |
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dataset:
|
| 673 |
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name: mix de
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| 674 |
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type: mix_de
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| 675 |
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metrics:
|
| 676 |
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- type: cosine_accuracy@1
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value: 0.642225689027561
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name: Cosine Accuracy@1
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- type: cosine_accuracy@20
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value: 0.9240769630785232
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name: Cosine Accuracy@20
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- type: cosine_accuracy@50
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value: 0.9635985439417577
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name: Cosine Accuracy@50
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value: 0.9771190847633905
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name: Cosine Accuracy@100
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value: 0.9901196047841914
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name: Cosine Accuracy@200
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value: 0.642225689027561
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name: Cosine Precision@1
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value: 0.11911076443057722
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name: Cosine Precision@20
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value: 0.05086843473738951
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name: Cosine Precision@50
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value: 0.0261622464898596
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name: Cosine Precision@100
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- type: cosine_precision@150
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value: 0.01770844167100017
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name: Cosine Precision@150
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- type: cosine_precision@200
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value: 0.013424336973478942
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name: Cosine Precision@200
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- type: cosine_recall@1
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value: 0.2405616224648986
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| 714 |
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name: Cosine Recall@1
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- type: cosine_recall@20
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value: 0.8650459351707401
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name: Cosine Recall@20
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- type: cosine_recall@50
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value: 0.9226295718495406
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name: Cosine Recall@50
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- type: cosine_recall@100
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value: 0.9480412549835328
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name: Cosine Recall@100
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- type: cosine_recall@150
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value: 0.9618651412723176
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name: Cosine Recall@150
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- type: cosine_recall@200
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| 728 |
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value: 0.9720922170220142
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| 729 |
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name: Cosine Recall@200
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- type: cosine_ndcg@1
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| 731 |
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value: 0.642225689027561
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name: Cosine Ndcg@1
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value: 0.7332013199323174
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name: Cosine Ndcg@20
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- type: cosine_ndcg@50
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value: 0.7490333180034867
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name: Cosine Ndcg@50
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- type: cosine_ndcg@100
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value: 0.7547612967303503
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name: Cosine Ndcg@100
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- type: cosine_ndcg@150
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value: 0.7575184392863841
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name: Cosine Ndcg@150
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- type: cosine_ndcg@200
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value: 0.7593986816807992
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| 747 |
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name: Cosine Ndcg@200
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- type: cosine_mrr@1
|
| 749 |
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value: 0.642225689027561
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| 750 |
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name: Cosine Mrr@1
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| 751 |
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- type: cosine_mrr@20
|
| 752 |
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value: 0.7246816496840639
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| 753 |
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name: Cosine Mrr@20
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- type: cosine_mrr@50
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| 755 |
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value: 0.7260235454700952
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name: Cosine Mrr@50
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- type: cosine_mrr@100
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value: 0.7262168772880452
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name: Cosine Mrr@100
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- type: cosine_mrr@150
|
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value: 0.7262822017289415
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name: Cosine Mrr@150
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- type: cosine_mrr@200
|
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value: 0.7263128860080087
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name: Cosine Mrr@200
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value: 0.642225689027561
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name: Cosine Map@1
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- type: cosine_map@20
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value: 0.6521189972338849
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name: Cosine Map@20
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- type: cosine_map@50
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value: 0.6561813596290409
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name: Cosine Map@50
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- type: cosine_map@100
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value: 0.6570111325791598
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name: Cosine Map@100
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- type: cosine_map@150
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value: 0.6572712744212402
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name: Cosine Map@150
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- type: cosine_map@200
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value: 0.6574012324541948
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name: Cosine Map@200
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- type: cosine_map@500
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| 785 |
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value: 0.6575399010277455
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| 786 |
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name: Cosine Map@500
|
| 787 |
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- task:
|
| 788 |
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type: information-retrieval
|
| 789 |
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name: Information Retrieval
|
| 790 |
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dataset:
|
| 791 |
+
name: mix zh
|
| 792 |
+
type: mix_zh
|
| 793 |
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metrics:
|
| 794 |
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- type: cosine_accuracy@1
|
| 795 |
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value: 0.7713987473903967
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| 796 |
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name: Cosine Accuracy@1
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- type: cosine_accuracy@20
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| 798 |
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value: 0.9806889352818372
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| 799 |
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name: Cosine Accuracy@20
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| 800 |
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- type: cosine_accuracy@50
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| 801 |
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value: 0.9916492693110647
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| 802 |
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name: Cosine Accuracy@50
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- type: cosine_accuracy@100
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| 804 |
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value: 0.9947807933194155
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name: Cosine Accuracy@100
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- type: cosine_accuracy@150
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| 807 |
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value: 0.9963465553235908
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| 808 |
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name: Cosine Accuracy@150
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- type: cosine_accuracy@200
|
| 810 |
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value: 0.9973903966597077
|
| 811 |
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name: Cosine Accuracy@200
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- type: cosine_precision@1
|
| 813 |
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value: 0.7713987473903967
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| 814 |
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name: Cosine Precision@1
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- type: cosine_precision@20
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| 816 |
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value: 0.13656054279749477
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| 817 |
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name: Cosine Precision@20
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| 818 |
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- type: cosine_precision@50
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| 819 |
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value: 0.05762004175365346
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| 820 |
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name: Cosine Precision@50
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| 821 |
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- type: cosine_precision@100
|
| 822 |
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value: 0.02944676409185805
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| 823 |
+
name: Cosine Precision@100
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| 824 |
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- type: cosine_precision@150
|
| 825 |
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value: 0.0197633959638135
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| 826 |
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name: Cosine Precision@150
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| 827 |
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- type: cosine_precision@200
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| 828 |
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value: 0.014877348643006268
|
| 829 |
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name: Cosine Precision@200
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| 830 |
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- type: cosine_recall@1
|
| 831 |
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value: 0.2585731683069888
|
| 832 |
+
name: Cosine Recall@1
|
| 833 |
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- type: cosine_recall@20
|
| 834 |
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value: 0.9014352818371607
|
| 835 |
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name: Cosine Recall@20
|
| 836 |
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- type: cosine_recall@50
|
| 837 |
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value: 0.950347947112039
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| 838 |
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name: Cosine Recall@50
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| 839 |
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- type: cosine_recall@100
|
| 840 |
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value: 0.9715031315240084
|
| 841 |
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name: Cosine Recall@100
|
| 842 |
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- type: cosine_recall@150
|
| 843 |
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value: 0.9781576200417537
|
| 844 |
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name: Cosine Recall@150
|
| 845 |
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- type: cosine_recall@200
|
| 846 |
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value: 0.9818110647181629
|
| 847 |
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name: Cosine Recall@200
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- type: cosine_ndcg@1
|
| 849 |
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value: 0.7713987473903967
|
| 850 |
+
name: Cosine Ndcg@1
|
| 851 |
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- type: cosine_ndcg@20
|
| 852 |
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value: 0.7926986810043013
|
| 853 |
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name: Cosine Ndcg@20
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| 854 |
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- type: cosine_ndcg@50
|
| 855 |
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value: 0.8066848794942646
|
| 856 |
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name: Cosine Ndcg@50
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| 857 |
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- type: cosine_ndcg@100
|
| 858 |
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value: 0.8115576206060865
|
| 859 |
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name: Cosine Ndcg@100
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- type: cosine_ndcg@150
|
| 861 |
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value: 0.8129087269558002
|
| 862 |
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name: Cosine Ndcg@150
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- type: cosine_ndcg@200
|
| 864 |
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value: 0.8135973837485255
|
| 865 |
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name: Cosine Ndcg@200
|
| 866 |
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- type: cosine_mrr@1
|
| 867 |
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value: 0.7713987473903967
|
| 868 |
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name: Cosine Mrr@1
|
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- type: cosine_mrr@20
|
| 870 |
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value: 0.8432785828350186
|
| 871 |
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name: Cosine Mrr@20
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- type: cosine_mrr@50
|
| 873 |
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value: 0.8436385628906108
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| 874 |
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name: Cosine Mrr@50
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- type: cosine_mrr@100
|
| 876 |
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value: 0.8436803457907981
|
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name: Cosine Mrr@100
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- type: cosine_mrr@150
|
| 879 |
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value: 0.8436922193976949
|
| 880 |
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name: Cosine Mrr@150
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- type: cosine_mrr@200
|
| 882 |
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value: 0.8436986631082636
|
| 883 |
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name: Cosine Mrr@200
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- type: cosine_map@1
|
| 885 |
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value: 0.7713987473903967
|
| 886 |
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name: Cosine Map@1
|
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- type: cosine_map@20
|
| 888 |
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value: 0.6929147114308428
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name: Cosine Map@20
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- type: cosine_map@50
|
| 891 |
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value: 0.6972607407491801
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name: Cosine Map@50
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- type: cosine_map@100
|
| 894 |
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value: 0.6981100717727863
|
| 895 |
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name: Cosine Map@100
|
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- type: cosine_map@150
|
| 897 |
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value: 0.6982601227257159
|
| 898 |
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name: Cosine Map@150
|
| 899 |
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- type: cosine_map@200
|
| 900 |
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value: 0.6983171494463136
|
| 901 |
+
name: Cosine Map@200
|
| 902 |
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- type: cosine_map@500
|
| 903 |
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value: 0.6984116893552017
|
| 904 |
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name: Cosine Map@500
|
| 905 |
+
---
|
| 906 |
+
|
| 907 |
+
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
|
| 908 |
+
|
| 909 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 910 |
+
|
| 911 |
+
## Model Details
|
| 912 |
+
|
| 913 |
+
### Model Description
|
| 914 |
+
- **Model Type:** Sentence Transformer
|
| 915 |
+
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
|
| 916 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 917 |
+
- **Output Dimensionality:** 768 dimensions
|
| 918 |
+
- **Similarity Function:** Cosine Similarity
|
| 919 |
+
- **Training Datasets:**
|
| 920 |
+
- full_en
|
| 921 |
+
- full_de
|
| 922 |
+
- full_es
|
| 923 |
+
- full_zh
|
| 924 |
+
- mix
|
| 925 |
+
<!-- - **Language:** Unknown -->
|
| 926 |
+
<!-- - **License:** Unknown -->
|
| 927 |
+
|
| 928 |
+
### Model Sources
|
| 929 |
+
|
| 930 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 931 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 932 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 933 |
+
|
| 934 |
+
### Full Model Architecture
|
| 935 |
+
|
| 936 |
+
```
|
| 937 |
+
SentenceTransformer(
|
| 938 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
|
| 939 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 940 |
+
(2): Normalize()
|
| 941 |
+
)
|
| 942 |
+
```
|
| 943 |
+
|
| 944 |
+
## Usage
|
| 945 |
+
|
| 946 |
+
### Direct Usage (Sentence Transformers)
|
| 947 |
+
|
| 948 |
+
First install the Sentence Transformers library:
|
| 949 |
+
|
| 950 |
+
```bash
|
| 951 |
+
pip install -U sentence-transformers
|
| 952 |
+
```
|
| 953 |
+
|
| 954 |
+
Then you can load this model and run inference.
|
| 955 |
+
```python
|
| 956 |
+
from sentence_transformers import SentenceTransformer
|
| 957 |
+
|
| 958 |
+
# Download from the 🤗 Hub
|
| 959 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
| 960 |
+
# Run inference
|
| 961 |
+
sentences = [
|
| 962 |
+
'Volksvertreter',
|
| 963 |
+
'Parlamentarier',
|
| 964 |
+
'Oberbürgermeister',
|
| 965 |
+
]
|
| 966 |
+
embeddings = model.encode(sentences)
|
| 967 |
+
print(embeddings.shape)
|
| 968 |
+
# [3, 768]
|
| 969 |
+
|
| 970 |
+
# Get the similarity scores for the embeddings
|
| 971 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 972 |
+
print(similarities.shape)
|
| 973 |
+
# [3, 3]
|
| 974 |
+
```
|
| 975 |
+
|
| 976 |
+
<!--
|
| 977 |
+
### Direct Usage (Transformers)
|
| 978 |
+
|
| 979 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 980 |
+
|
| 981 |
+
</details>
|
| 982 |
+
-->
|
| 983 |
+
|
| 984 |
+
<!--
|
| 985 |
+
### Downstream Usage (Sentence Transformers)
|
| 986 |
+
|
| 987 |
+
You can finetune this model on your own dataset.
|
| 988 |
+
|
| 989 |
+
<details><summary>Click to expand</summary>
|
| 990 |
+
|
| 991 |
+
</details>
|
| 992 |
+
-->
|
| 993 |
+
|
| 994 |
+
<!--
|
| 995 |
+
### Out-of-Scope Use
|
| 996 |
+
|
| 997 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 998 |
+
-->
|
| 999 |
+
|
| 1000 |
+
## Evaluation
|
| 1001 |
+
|
| 1002 |
+
### Metrics
|
| 1003 |
+
|
| 1004 |
+
#### Information Retrieval
|
| 1005 |
+
|
| 1006 |
+
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
|
| 1007 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 1008 |
+
|
| 1009 |
+
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|
| 1010 |
+
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
|
| 1011 |
+
| cosine_accuracy@1 | 0.6571 | 0.1189 | 0.2956 | 0.6602 | 0.701 | 0.6422 | 0.7714 |
|
| 1012 |
+
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9475 | 0.9241 | 0.9807 |
|
| 1013 |
+
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9776 | 0.9636 | 0.9916 |
|
| 1014 |
+
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9886 | 0.9771 | 0.9948 |
|
| 1015 |
+
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9922 | 0.9849 | 0.9963 |
|
| 1016 |
+
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9932 | 0.9901 | 0.9974 |
|
| 1017 |
+
| cosine_precision@1 | 0.6571 | 0.1189 | 0.2956 | 0.6602 | 0.701 | 0.6422 | 0.7714 |
|
| 1018 |
+
| cosine_precision@20 | 0.5133 | 0.5768 | 0.5074 | 0.4869 | 0.1197 | 0.1191 | 0.1366 |
|
| 1019 |
+
| cosine_precision@50 | 0.317 | 0.3907 | 0.3682 | 0.2959 | 0.0509 | 0.0509 | 0.0576 |
|
| 1020 |
+
| cosine_precision@100 | 0.1897 | 0.2542 | 0.2418 | 0.179 | 0.026 | 0.0262 | 0.0294 |
|
| 1021 |
+
| cosine_precision@150 | 0.1362 | 0.1923 | 0.1819 | 0.1291 | 0.0175 | 0.0177 | 0.0198 |
|
| 1022 |
+
| cosine_precision@200 | 0.1056 | 0.1526 | 0.1471 | 0.1006 | 0.0132 | 0.0134 | 0.0149 |
|
| 1023 |
+
| cosine_recall@1 | 0.067 | 0.0035 | 0.0111 | 0.0667 | 0.2707 | 0.2406 | 0.2586 |
|
| 1024 |
+
| cosine_recall@20 | 0.5478 | 0.3862 | 0.3189 | 0.5204 | 0.8851 | 0.865 | 0.9014 |
|
| 1025 |
+
| cosine_recall@50 | 0.747 | 0.5626 | 0.5004 | 0.7067 | 0.9391 | 0.9226 | 0.9503 |
|
| 1026 |
+
| cosine_recall@100 | 0.8467 | 0.6836 | 0.6244 | 0.8139 | 0.9599 | 0.948 | 0.9715 |
|
| 1027 |
+
| cosine_recall@150 | 0.9011 | 0.7497 | 0.6875 | 0.8684 | 0.9696 | 0.9619 | 0.9782 |
|
| 1028 |
+
| cosine_recall@200 | 0.9257 | 0.7853 | 0.7335 | 0.8964 | 0.9759 | 0.9721 | 0.9818 |
|
| 1029 |
+
| cosine_ndcg@1 | 0.6571 | 0.1189 | 0.2956 | 0.6602 | 0.701 | 0.6422 | 0.7714 |
|
| 1030 |
+
| cosine_ndcg@20 | 0.6924 | 0.6159 | 0.5343 | 0.663 | 0.769 | 0.7332 | 0.7927 |
|
| 1031 |
+
| cosine_ndcg@50 | 0.717 | 0.5887 | 0.5252 | 0.6822 | 0.7839 | 0.749 | 0.8067 |
|
| 1032 |
+
| cosine_ndcg@100 | 0.7691 | 0.6196 | 0.5569 | 0.7344 | 0.7884 | 0.7548 | 0.8116 |
|
| 1033 |
+
| cosine_ndcg@150 | 0.7923 | 0.6531 | 0.5892 | 0.758 | 0.7903 | 0.7575 | 0.8129 |
|
| 1034 |
+
| **cosine_ndcg@200** | **0.8024** | **0.6703** | **0.6109** | **0.7695** | **0.7914** | **0.7594** | **0.8136** |
|
| 1035 |
+
| cosine_mrr@1 | 0.6571 | 0.1189 | 0.2956 | 0.6602 | 0.701 | 0.6422 | 0.7714 |
|
| 1036 |
+
| cosine_mrr@20 | 0.8111 | 0.5554 | 0.5141 | 0.8068 | 0.7712 | 0.7247 | 0.8433 |
|
| 1037 |
+
| cosine_mrr@50 | 0.8111 | 0.5554 | 0.5141 | 0.8068 | 0.7723 | 0.726 | 0.8436 |
|
| 1038 |
+
| cosine_mrr@100 | 0.8111 | 0.5554 | 0.5142 | 0.8068 | 0.7724 | 0.7262 | 0.8437 |
|
| 1039 |
+
| cosine_mrr@150 | 0.8111 | 0.5554 | 0.5142 | 0.8068 | 0.7725 | 0.7263 | 0.8437 |
|
| 1040 |
+
| cosine_mrr@200 | 0.8111 | 0.5554 | 0.5142 | 0.8068 | 0.7725 | 0.7263 | 0.8437 |
|
| 1041 |
+
| cosine_map@1 | 0.6571 | 0.1189 | 0.2956 | 0.6602 | 0.701 | 0.6422 | 0.7714 |
|
| 1042 |
+
| cosine_map@20 | 0.5516 | 0.484 | 0.3953 | 0.5177 | 0.6938 | 0.6521 | 0.6929 |
|
| 1043 |
+
| cosine_map@50 | 0.5474 | 0.4288 | 0.3566 | 0.5051 | 0.6978 | 0.6562 | 0.6973 |
|
| 1044 |
+
| cosine_map@100 | 0.5799 | 0.4352 | 0.3618 | 0.5346 | 0.6985 | 0.657 | 0.6981 |
|
| 1045 |
+
| cosine_map@150 | 0.5895 | 0.4511 | 0.3767 | 0.5441 | 0.6987 | 0.6573 | 0.6983 |
|
| 1046 |
+
| cosine_map@200 | 0.5931 | 0.458 | 0.385 | 0.5478 | 0.6987 | 0.6574 | 0.6983 |
|
| 1047 |
+
| cosine_map@500 | 0.5967 | 0.4708 | 0.3976 | 0.5525 | 0.6989 | 0.6575 | 0.6984 |
|
| 1048 |
+
|
| 1049 |
+
<!--
|
| 1050 |
+
## Bias, Risks and Limitations
|
| 1051 |
+
|
| 1052 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 1053 |
+
-->
|
| 1054 |
+
|
| 1055 |
+
<!--
|
| 1056 |
+
### Recommendations
|
| 1057 |
+
|
| 1058 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 1059 |
+
-->
|
| 1060 |
+
|
| 1061 |
+
## Training Details
|
| 1062 |
+
|
| 1063 |
+
### Training Datasets
|
| 1064 |
+
<details><summary>full_en</summary>
|
| 1065 |
+
|
| 1066 |
+
#### full_en
|
| 1067 |
+
|
| 1068 |
+
* Dataset: full_en
|
| 1069 |
+
* Size: 28,880 training samples
|
| 1070 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1071 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1072 |
+
| | anchor | positive |
|
| 1073 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1074 |
+
| type | string | string |
|
| 1075 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
|
| 1076 |
+
* Samples:
|
| 1077 |
+
| anchor | positive |
|
| 1078 |
+
|:-----------------------------------------|:-----------------------------------------|
|
| 1079 |
+
| <code>air commodore</code> | <code>flight lieutenant</code> |
|
| 1080 |
+
| <code>command and control officer</code> | <code>flight officer</code> |
|
| 1081 |
+
| <code>air commodore</code> | <code>command and control officer</code> |
|
| 1082 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1083 |
+
```json
|
| 1084 |
+
{'guide': SentenceTransformer(
|
| 1085 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1086 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1087 |
+
(2): Normalize()
|
| 1088 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1089 |
+
```
|
| 1090 |
+
</details>
|
| 1091 |
+
<details><summary>full_de</summary>
|
| 1092 |
+
|
| 1093 |
+
#### full_de
|
| 1094 |
+
|
| 1095 |
+
* Dataset: full_de
|
| 1096 |
+
* Size: 23,023 training samples
|
| 1097 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1098 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1099 |
+
| | anchor | positive |
|
| 1100 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1101 |
+
| type | string | string |
|
| 1102 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
|
| 1103 |
+
* Samples:
|
| 1104 |
+
| anchor | positive |
|
| 1105 |
+
|:----------------------------------|:-----------------------------------------------------|
|
| 1106 |
+
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
|
| 1107 |
+
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
|
| 1108 |
+
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
|
| 1109 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1110 |
+
```json
|
| 1111 |
+
{'guide': SentenceTransformer(
|
| 1112 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1113 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1114 |
+
(2): Normalize()
|
| 1115 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1116 |
+
```
|
| 1117 |
+
</details>
|
| 1118 |
+
<details><summary>full_es</summary>
|
| 1119 |
+
|
| 1120 |
+
#### full_es
|
| 1121 |
+
|
| 1122 |
+
* Dataset: full_es
|
| 1123 |
+
* Size: 20,724 training samples
|
| 1124 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1125 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1126 |
+
| | anchor | positive |
|
| 1127 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1128 |
+
| type | string | string |
|
| 1129 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
|
| 1130 |
+
* Samples:
|
| 1131 |
+
| anchor | positive |
|
| 1132 |
+
|:------------------------------------|:-------------------------------------------|
|
| 1133 |
+
| <code>jefe de escuadrón</code> | <code>instructor</code> |
|
| 1134 |
+
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
|
| 1135 |
+
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
|
| 1136 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1137 |
+
```json
|
| 1138 |
+
{'guide': SentenceTransformer(
|
| 1139 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1140 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1141 |
+
(2): Normalize()
|
| 1142 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1143 |
+
```
|
| 1144 |
+
</details>
|
| 1145 |
+
<details><summary>full_zh</summary>
|
| 1146 |
+
|
| 1147 |
+
#### full_zh
|
| 1148 |
+
|
| 1149 |
+
* Dataset: full_zh
|
| 1150 |
+
* Size: 30,401 training samples
|
| 1151 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1152 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1153 |
+
| | anchor | positive |
|
| 1154 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1155 |
+
| type | string | string |
|
| 1156 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
|
| 1157 |
+
* Samples:
|
| 1158 |
+
| anchor | positive |
|
| 1159 |
+
|:------------------|:---------------------|
|
| 1160 |
+
| <code>技术总监</code> | <code>技术和运营总监</code> |
|
| 1161 |
+
| <code>技术总监</code> | <code>技术主管</code> |
|
| 1162 |
+
| <code>技术总监</code> | <code>技术艺术总监</code> |
|
| 1163 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1164 |
+
```json
|
| 1165 |
+
{'guide': SentenceTransformer(
|
| 1166 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1167 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1168 |
+
(2): Normalize()
|
| 1169 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1170 |
+
```
|
| 1171 |
+
</details>
|
| 1172 |
+
<details><summary>mix</summary>
|
| 1173 |
+
|
| 1174 |
+
#### mix
|
| 1175 |
+
|
| 1176 |
+
* Dataset: mix
|
| 1177 |
+
* Size: 21,760 training samples
|
| 1178 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1179 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1180 |
+
| | anchor | positive |
|
| 1181 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1182 |
+
| type | string | string |
|
| 1183 |
+
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
|
| 1184 |
+
* Samples:
|
| 1185 |
+
| anchor | positive |
|
| 1186 |
+
|:------------------------------------------|:----------------------------------------------------------------|
|
| 1187 |
+
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
|
| 1188 |
+
| <code>head of technical</code> | <code>directora técnica</code> |
|
| 1189 |
+
| <code>head of technical department</code> | <code>技术艺术总监</code> |
|
| 1190 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1191 |
+
```json
|
| 1192 |
+
{'guide': SentenceTransformer(
|
| 1193 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1194 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1195 |
+
(2): Normalize()
|
| 1196 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1197 |
+
```
|
| 1198 |
+
</details>
|
| 1199 |
+
|
| 1200 |
+
### Training Hyperparameters
|
| 1201 |
+
#### Non-Default Hyperparameters
|
| 1202 |
+
|
| 1203 |
+
- `eval_strategy`: steps
|
| 1204 |
+
- `per_device_train_batch_size`: 128
|
| 1205 |
+
- `per_device_eval_batch_size`: 128
|
| 1206 |
+
- `gradient_accumulation_steps`: 2
|
| 1207 |
+
- `num_train_epochs`: 5
|
| 1208 |
+
- `warmup_ratio`: 0.05
|
| 1209 |
+
- `log_on_each_node`: False
|
| 1210 |
+
- `fp16`: True
|
| 1211 |
+
- `dataloader_num_workers`: 4
|
| 1212 |
+
- `ddp_find_unused_parameters`: True
|
| 1213 |
+
- `batch_sampler`: no_duplicates
|
| 1214 |
+
|
| 1215 |
+
#### All Hyperparameters
|
| 1216 |
+
<details><summary>Click to expand</summary>
|
| 1217 |
+
|
| 1218 |
+
- `overwrite_output_dir`: False
|
| 1219 |
+
- `do_predict`: False
|
| 1220 |
+
- `eval_strategy`: steps
|
| 1221 |
+
- `prediction_loss_only`: True
|
| 1222 |
+
- `per_device_train_batch_size`: 128
|
| 1223 |
+
- `per_device_eval_batch_size`: 128
|
| 1224 |
+
- `per_gpu_train_batch_size`: None
|
| 1225 |
+
- `per_gpu_eval_batch_size`: None
|
| 1226 |
+
- `gradient_accumulation_steps`: 2
|
| 1227 |
+
- `eval_accumulation_steps`: None
|
| 1228 |
+
- `torch_empty_cache_steps`: None
|
| 1229 |
+
- `learning_rate`: 5e-05
|
| 1230 |
+
- `weight_decay`: 0.0
|
| 1231 |
+
- `adam_beta1`: 0.9
|
| 1232 |
+
- `adam_beta2`: 0.999
|
| 1233 |
+
- `adam_epsilon`: 1e-08
|
| 1234 |
+
- `max_grad_norm`: 1.0
|
| 1235 |
+
- `num_train_epochs`: 5
|
| 1236 |
+
- `max_steps`: -1
|
| 1237 |
+
- `lr_scheduler_type`: linear
|
| 1238 |
+
- `lr_scheduler_kwargs`: {}
|
| 1239 |
+
- `warmup_ratio`: 0.05
|
| 1240 |
+
- `warmup_steps`: 0
|
| 1241 |
+
- `log_level`: passive
|
| 1242 |
+
- `log_level_replica`: warning
|
| 1243 |
+
- `log_on_each_node`: False
|
| 1244 |
+
- `logging_nan_inf_filter`: True
|
| 1245 |
+
- `save_safetensors`: True
|
| 1246 |
+
- `save_on_each_node`: False
|
| 1247 |
+
- `save_only_model`: False
|
| 1248 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 1249 |
+
- `no_cuda`: False
|
| 1250 |
+
- `use_cpu`: False
|
| 1251 |
+
- `use_mps_device`: False
|
| 1252 |
+
- `seed`: 42
|
| 1253 |
+
- `data_seed`: None
|
| 1254 |
+
- `jit_mode_eval`: False
|
| 1255 |
+
- `use_ipex`: False
|
| 1256 |
+
- `bf16`: False
|
| 1257 |
+
- `fp16`: True
|
| 1258 |
+
- `fp16_opt_level`: O1
|
| 1259 |
+
- `half_precision_backend`: auto
|
| 1260 |
+
- `bf16_full_eval`: False
|
| 1261 |
+
- `fp16_full_eval`: False
|
| 1262 |
+
- `tf32`: None
|
| 1263 |
+
- `local_rank`: 0
|
| 1264 |
+
- `ddp_backend`: None
|
| 1265 |
+
- `tpu_num_cores`: None
|
| 1266 |
+
- `tpu_metrics_debug`: False
|
| 1267 |
+
- `debug`: []
|
| 1268 |
+
- `dataloader_drop_last`: True
|
| 1269 |
+
- `dataloader_num_workers`: 4
|
| 1270 |
+
- `dataloader_prefetch_factor`: None
|
| 1271 |
+
- `past_index`: -1
|
| 1272 |
+
- `disable_tqdm`: False
|
| 1273 |
+
- `remove_unused_columns`: True
|
| 1274 |
+
- `label_names`: None
|
| 1275 |
+
- `load_best_model_at_end`: False
|
| 1276 |
+
- `ignore_data_skip`: False
|
| 1277 |
+
- `fsdp`: []
|
| 1278 |
+
- `fsdp_min_num_params`: 0
|
| 1279 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 1280 |
+
- `tp_size`: 0
|
| 1281 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 1282 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 1283 |
+
- `deepspeed`: None
|
| 1284 |
+
- `label_smoothing_factor`: 0.0
|
| 1285 |
+
- `optim`: adamw_torch
|
| 1286 |
+
- `optim_args`: None
|
| 1287 |
+
- `adafactor`: False
|
| 1288 |
+
- `group_by_length`: False
|
| 1289 |
+
- `length_column_name`: length
|
| 1290 |
+
- `ddp_find_unused_parameters`: True
|
| 1291 |
+
- `ddp_bucket_cap_mb`: None
|
| 1292 |
+
- `ddp_broadcast_buffers`: False
|
| 1293 |
+
- `dataloader_pin_memory`: True
|
| 1294 |
+
- `dataloader_persistent_workers`: False
|
| 1295 |
+
- `skip_memory_metrics`: True
|
| 1296 |
+
- `use_legacy_prediction_loop`: False
|
| 1297 |
+
- `push_to_hub`: False
|
| 1298 |
+
- `resume_from_checkpoint`: None
|
| 1299 |
+
- `hub_model_id`: None
|
| 1300 |
+
- `hub_strategy`: every_save
|
| 1301 |
+
- `hub_private_repo`: None
|
| 1302 |
+
- `hub_always_push`: False
|
| 1303 |
+
- `gradient_checkpointing`: False
|
| 1304 |
+
- `gradient_checkpointing_kwargs`: None
|
| 1305 |
+
- `include_inputs_for_metrics`: False
|
| 1306 |
+
- `include_for_metrics`: []
|
| 1307 |
+
- `eval_do_concat_batches`: True
|
| 1308 |
+
- `fp16_backend`: auto
|
| 1309 |
+
- `push_to_hub_model_id`: None
|
| 1310 |
+
- `push_to_hub_organization`: None
|
| 1311 |
+
- `mp_parameters`:
|
| 1312 |
+
- `auto_find_batch_size`: False
|
| 1313 |
+
- `full_determinism`: False
|
| 1314 |
+
- `torchdynamo`: None
|
| 1315 |
+
- `ray_scope`: last
|
| 1316 |
+
- `ddp_timeout`: 1800
|
| 1317 |
+
- `torch_compile`: False
|
| 1318 |
+
- `torch_compile_backend`: None
|
| 1319 |
+
- `torch_compile_mode`: None
|
| 1320 |
+
- `include_tokens_per_second`: False
|
| 1321 |
+
- `include_num_input_tokens_seen`: False
|
| 1322 |
+
- `neftune_noise_alpha`: None
|
| 1323 |
+
- `optim_target_modules`: None
|
| 1324 |
+
- `batch_eval_metrics`: False
|
| 1325 |
+
- `eval_on_start`: False
|
| 1326 |
+
- `use_liger_kernel`: False
|
| 1327 |
+
- `eval_use_gather_object`: False
|
| 1328 |
+
- `average_tokens_across_devices`: False
|
| 1329 |
+
- `prompts`: None
|
| 1330 |
+
- `batch_sampler`: no_duplicates
|
| 1331 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 1332 |
+
|
| 1333 |
+
</details>
|
| 1334 |
+
|
| 1335 |
+
### Training Logs
|
| 1336 |
+
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|
| 1337 |
+
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
|
| 1338 |
+
| -1 | -1 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.7437 |
|
| 1339 |
+
| 0.0082 | 1 | 4.3088 | - | - | - | - | - | - | - |
|
| 1340 |
+
| 0.8230 | 100 | 1.9026 | - | - | - | - | - | - | - |
|
| 1341 |
+
| 1.6502 | 200 | 0.9336 | 0.8024 | 0.6703 | 0.6109 | 0.7695 | 0.7914 | 0.7594 | 0.8136 |
|
| 1342 |
+
|
| 1343 |
+
|
| 1344 |
+
### Framework Versions
|
| 1345 |
+
- Python: 3.11.11
|
| 1346 |
+
- Sentence Transformers: 4.1.0
|
| 1347 |
+
- Transformers: 4.51.2
|
| 1348 |
+
- PyTorch: 2.6.0+cu124
|
| 1349 |
+
- Accelerate: 1.6.0
|
| 1350 |
+
- Datasets: 3.5.0
|
| 1351 |
+
- Tokenizers: 0.21.1
|
| 1352 |
+
|
| 1353 |
+
## Citation
|
| 1354 |
+
|
| 1355 |
+
### BibTeX
|
| 1356 |
+
|
| 1357 |
+
#### Sentence Transformers
|
| 1358 |
+
```bibtex
|
| 1359 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 1360 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 1361 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 1362 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 1363 |
+
month = "11",
|
| 1364 |
+
year = "2019",
|
| 1365 |
+
publisher = "Association for Computational Linguistics",
|
| 1366 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 1367 |
+
}
|
| 1368 |
+
```
|
| 1369 |
+
|
| 1370 |
+
#### GISTEmbedLoss
|
| 1371 |
+
```bibtex
|
| 1372 |
+
@misc{solatorio2024gistembed,
|
| 1373 |
+
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
|
| 1374 |
+
author={Aivin V. Solatorio},
|
| 1375 |
+
year={2024},
|
| 1376 |
+
eprint={2402.16829},
|
| 1377 |
+
archivePrefix={arXiv},
|
| 1378 |
+
primaryClass={cs.LG}
|
| 1379 |
+
}
|
| 1380 |
+
```
|
| 1381 |
+
|
| 1382 |
+
<!--
|
| 1383 |
+
## Glossary
|
| 1384 |
+
|
| 1385 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 1386 |
+
-->
|
| 1387 |
+
|
| 1388 |
+
<!--
|
| 1389 |
+
## Model Card Authors
|
| 1390 |
+
|
| 1391 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 1392 |
+
-->
|
| 1393 |
+
|
| 1394 |
+
<!--
|
| 1395 |
+
## Model Card Contact
|
| 1396 |
+
|
| 1397 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 1398 |
+
-->
|
checkpoint-200/config.json
ADDED
|
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"NewModel"
|
| 4 |
+
],
|
| 5 |
+
"attention_probs_dropout_prob": 0.0,
|
| 6 |
+
"auto_map": {
|
| 7 |
+
"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
|
| 8 |
+
"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
|
| 9 |
+
"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
|
| 10 |
+
"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
|
| 11 |
+
"AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
|
| 12 |
+
"AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
|
| 13 |
+
"AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
|
| 14 |
+
},
|
| 15 |
+
"classifier_dropout": 0.0,
|
| 16 |
+
"hidden_act": "gelu",
|
| 17 |
+
"hidden_dropout_prob": 0.1,
|
| 18 |
+
"hidden_size": 768,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "LABEL_0"
|
| 21 |
+
},
|
| 22 |
+
"initializer_range": 0.02,
|
| 23 |
+
"intermediate_size": 3072,
|
| 24 |
+
"label2id": {
|
| 25 |
+
"LABEL_0": 0
|
| 26 |
+
},
|
| 27 |
+
"layer_norm_eps": 1e-12,
|
| 28 |
+
"layer_norm_type": "layer_norm",
|
| 29 |
+
"logn_attention_clip1": false,
|
| 30 |
+
"logn_attention_scale": false,
|
| 31 |
+
"max_position_embeddings": 8192,
|
| 32 |
+
"model_type": "new",
|
| 33 |
+
"num_attention_heads": 12,
|
| 34 |
+
"num_hidden_layers": 12,
|
| 35 |
+
"pack_qkv": true,
|
| 36 |
+
"pad_token_id": 1,
|
| 37 |
+
"position_embedding_type": "rope",
|
| 38 |
+
"rope_scaling": {
|
| 39 |
+
"factor": 8.0,
|
| 40 |
+
"type": "ntk"
|
| 41 |
+
},
|
| 42 |
+
"rope_theta": 20000,
|
| 43 |
+
"torch_dtype": "float32",
|
| 44 |
+
"transformers_version": "4.51.2",
|
| 45 |
+
"type_vocab_size": 1,
|
| 46 |
+
"unpad_inputs": false,
|
| 47 |
+
"use_memory_efficient_attention": false,
|
| 48 |
+
"vocab_size": 250048
|
| 49 |
+
}
|
checkpoint-200/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "4.1.0",
|
| 4 |
+
"transformers": "4.51.2",
|
| 5 |
+
"pytorch": "2.6.0+cu124"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
checkpoint-200/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b5739aacbad9a359e1ff72bd23d43eda3a3610e34ac3061d3c0d19c24042df52
|
| 3 |
+
size 1221487872
|
checkpoint-200/modules.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"idx": 2,
|
| 16 |
+
"name": "2",
|
| 17 |
+
"path": "2_Normalize",
|
| 18 |
+
"type": "sentence_transformers.models.Normalize"
|
| 19 |
+
}
|
| 20 |
+
]
|
checkpoint-200/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4490d323ccfa091660768e94e1be37784f3171607a6403582274d38e52ff32f7
|
| 3 |
+
size 2443060986
|
checkpoint-200/rng_state_0.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e8d62bb1f8508718b92a5fecab4fb7d55821121383ff7cb094985aaff6fccbb8
|
| 3 |
+
size 15984
|
checkpoint-200/scaler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:986f83da3f32a0b13555fd4d7fcd98b07983eae746b7dafddcf4aff0739363a0
|
| 3 |
+
size 988
|
checkpoint-200/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5fea9571d262cb96d9a56307ab5a0db02609a153cb4165e4f3766dad960e60bd
|
| 3 |
+
size 1064
|
checkpoint-200/sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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| 1 |
+
{
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| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
checkpoint-200/special_tokens_map.json
ADDED
|
@@ -0,0 +1,51 @@
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| 1 |
+
{
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| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<s>",
|
| 4 |
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"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
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"rstrip": false,
|
| 7 |
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"single_word": false
|
| 8 |
+
},
|
| 9 |
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"cls_token": {
|
| 10 |
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"content": "<s>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
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"normalized": false,
|
| 13 |
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"rstrip": false,
|
| 14 |
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"single_word": false
|
| 15 |
+
},
|
| 16 |
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"eos_token": {
|
| 17 |
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"content": "</s>",
|
| 18 |
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"lstrip": false,
|
| 19 |
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"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
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},
|
| 23 |
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"mask_token": {
|
| 24 |
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"content": "<mask>",
|
| 25 |
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"lstrip": true,
|
| 26 |
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"normalized": false,
|
| 27 |
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"rstrip": false,
|
| 28 |
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"single_word": false
|
| 29 |
+
},
|
| 30 |
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"pad_token": {
|
| 31 |
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"content": "<pad>",
|
| 32 |
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"lstrip": false,
|
| 33 |
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"normalized": false,
|
| 34 |
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"rstrip": false,
|
| 35 |
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"single_word": false
|
| 36 |
+
},
|
| 37 |
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"sep_token": {
|
| 38 |
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"content": "</s>",
|
| 39 |
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"lstrip": false,
|
| 40 |
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"normalized": false,
|
| 41 |
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"rstrip": false,
|
| 42 |
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"single_word": false
|
| 43 |
+
},
|
| 44 |
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"unk_token": {
|
| 45 |
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"content": "<unk>",
|
| 46 |
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"lstrip": false,
|
| 47 |
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"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false
|
| 50 |
+
}
|
| 51 |
+
}
|
checkpoint-200/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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| 3 |
+
size 17082987
|
checkpoint-200/tokenizer_config.json
ADDED
|
@@ -0,0 +1,55 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<s>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "<pad>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "</s>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"250001": {
|
| 36 |
+
"content": "<mask>",
|
| 37 |
+
"lstrip": true,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"bos_token": "<s>",
|
| 45 |
+
"clean_up_tokenization_spaces": true,
|
| 46 |
+
"cls_token": "<s>",
|
| 47 |
+
"eos_token": "</s>",
|
| 48 |
+
"extra_special_tokens": {},
|
| 49 |
+
"mask_token": "<mask>",
|
| 50 |
+
"model_max_length": 8192,
|
| 51 |
+
"pad_token": "<pad>",
|
| 52 |
+
"sep_token": "</s>",
|
| 53 |
+
"tokenizer_class": "XLMRobertaTokenizerFast",
|
| 54 |
+
"unk_token": "<unk>"
|
| 55 |
+
}
|
checkpoint-200/trainer_state.json
ADDED
|
@@ -0,0 +1,322 @@
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| 1 |
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{
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"is_world_process_zero": true,
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"log_history": [
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{
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{
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},
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{
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| 32 |
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},
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| 33 |
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{
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| 34 |
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"eval_mix_zh_cosine_precision@200": 0.014877348643006268,
|
| 287 |
+
"eval_mix_zh_cosine_precision@50": 0.05762004175365346,
|
| 288 |
+
"eval_mix_zh_cosine_recall@1": 0.2585731683069888,
|
| 289 |
+
"eval_mix_zh_cosine_recall@100": 0.9715031315240084,
|
| 290 |
+
"eval_mix_zh_cosine_recall@150": 0.9781576200417537,
|
| 291 |
+
"eval_mix_zh_cosine_recall@20": 0.9014352818371607,
|
| 292 |
+
"eval_mix_zh_cosine_recall@200": 0.9818110647181629,
|
| 293 |
+
"eval_mix_zh_cosine_recall@50": 0.950347947112039,
|
| 294 |
+
"eval_runtime": 11.3241,
|
| 295 |
+
"eval_samples_per_second": 0.0,
|
| 296 |
+
"eval_sequential_score": 0.8135973837485255,
|
| 297 |
+
"eval_steps_per_second": 0.0,
|
| 298 |
+
"step": 200
|
| 299 |
+
}
|
| 300 |
+
],
|
| 301 |
+
"logging_steps": 100,
|
| 302 |
+
"max_steps": 605,
|
| 303 |
+
"num_input_tokens_seen": 0,
|
| 304 |
+
"num_train_epochs": 5,
|
| 305 |
+
"save_steps": 200,
|
| 306 |
+
"stateful_callbacks": {
|
| 307 |
+
"TrainerControl": {
|
| 308 |
+
"args": {
|
| 309 |
+
"should_epoch_stop": false,
|
| 310 |
+
"should_evaluate": false,
|
| 311 |
+
"should_log": false,
|
| 312 |
+
"should_save": true,
|
| 313 |
+
"should_training_stop": false
|
| 314 |
+
},
|
| 315 |
+
"attributes": {}
|
| 316 |
+
}
|
| 317 |
+
},
|
| 318 |
+
"total_flos": 4.5625697398559293e+18,
|
| 319 |
+
"train_batch_size": 128,
|
| 320 |
+
"trial_name": null,
|
| 321 |
+
"trial_params": null
|
| 322 |
+
}
|
checkpoint-200/training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bf4d5229cda7218c285b125b9db305e5b03ef7807e7bcdbf89afc0ac543dd982
|
| 3 |
+
size 5624
|
checkpoint-400/1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"word_embedding_dimension": 768,
|
| 3 |
+
"pooling_mode_cls_token": true,
|
| 4 |
+
"pooling_mode_mean_tokens": false,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": false,
|
| 9 |
+
"include_prompt": true
|
| 10 |
+
}
|
checkpoint-400/README.md
ADDED
|
@@ -0,0 +1,1400 @@
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:124788
|
| 8 |
+
- loss:GISTEmbedLoss
|
| 9 |
+
base_model: Alibaba-NLP/gte-multilingual-base
|
| 10 |
+
widget:
|
| 11 |
+
- source_sentence: 其他机械、设备和有形货物租赁服务代表
|
| 12 |
+
sentences:
|
| 13 |
+
- 其他机械和设备租赁服务工作人员
|
| 14 |
+
- 电子和电信设备及零部件物流经理
|
| 15 |
+
- 工业主厨
|
| 16 |
+
- source_sentence: 公交车司机
|
| 17 |
+
sentences:
|
| 18 |
+
- 表演灯光设计师
|
| 19 |
+
- 乙烯基地板安装工
|
| 20 |
+
- 国际巴士司机
|
| 21 |
+
- source_sentence: online communication manager
|
| 22 |
+
sentences:
|
| 23 |
+
- trades union official
|
| 24 |
+
- social media manager
|
| 25 |
+
- budget manager
|
| 26 |
+
- source_sentence: Projektmanagerin
|
| 27 |
+
sentences:
|
| 28 |
+
- Projektmanager/Projektmanagerin
|
| 29 |
+
- Category-Manager
|
| 30 |
+
- Infanterist
|
| 31 |
+
- source_sentence: Volksvertreter
|
| 32 |
+
sentences:
|
| 33 |
+
- Parlamentarier
|
| 34 |
+
- Oberbürgermeister
|
| 35 |
+
- Konsul
|
| 36 |
+
pipeline_tag: sentence-similarity
|
| 37 |
+
library_name: sentence-transformers
|
| 38 |
+
metrics:
|
| 39 |
+
- cosine_accuracy@1
|
| 40 |
+
- cosine_accuracy@20
|
| 41 |
+
- cosine_accuracy@50
|
| 42 |
+
- cosine_accuracy@100
|
| 43 |
+
- cosine_accuracy@150
|
| 44 |
+
- cosine_accuracy@200
|
| 45 |
+
- cosine_precision@1
|
| 46 |
+
- cosine_precision@20
|
| 47 |
+
- cosine_precision@50
|
| 48 |
+
- cosine_precision@100
|
| 49 |
+
- cosine_precision@150
|
| 50 |
+
- cosine_precision@200
|
| 51 |
+
- cosine_recall@1
|
| 52 |
+
- cosine_recall@20
|
| 53 |
+
- cosine_recall@50
|
| 54 |
+
- cosine_recall@100
|
| 55 |
+
- cosine_recall@150
|
| 56 |
+
- cosine_recall@200
|
| 57 |
+
- cosine_ndcg@1
|
| 58 |
+
- cosine_ndcg@20
|
| 59 |
+
- cosine_ndcg@50
|
| 60 |
+
- cosine_ndcg@100
|
| 61 |
+
- cosine_ndcg@150
|
| 62 |
+
- cosine_ndcg@200
|
| 63 |
+
- cosine_mrr@1
|
| 64 |
+
- cosine_mrr@20
|
| 65 |
+
- cosine_mrr@50
|
| 66 |
+
- cosine_mrr@100
|
| 67 |
+
- cosine_mrr@150
|
| 68 |
+
- cosine_mrr@200
|
| 69 |
+
- cosine_map@1
|
| 70 |
+
- cosine_map@20
|
| 71 |
+
- cosine_map@50
|
| 72 |
+
- cosine_map@100
|
| 73 |
+
- cosine_map@150
|
| 74 |
+
- cosine_map@200
|
| 75 |
+
- cosine_map@500
|
| 76 |
+
model-index:
|
| 77 |
+
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
|
| 78 |
+
results:
|
| 79 |
+
- task:
|
| 80 |
+
type: information-retrieval
|
| 81 |
+
name: Information Retrieval
|
| 82 |
+
dataset:
|
| 83 |
+
name: full en
|
| 84 |
+
type: full_en
|
| 85 |
+
metrics:
|
| 86 |
+
- type: cosine_accuracy@1
|
| 87 |
+
value: 0.6666666666666666
|
| 88 |
+
name: Cosine Accuracy@1
|
| 89 |
+
- type: cosine_accuracy@20
|
| 90 |
+
value: 0.9904761904761905
|
| 91 |
+
name: Cosine Accuracy@20
|
| 92 |
+
- type: cosine_accuracy@50
|
| 93 |
+
value: 0.9904761904761905
|
| 94 |
+
name: Cosine Accuracy@50
|
| 95 |
+
- type: cosine_accuracy@100
|
| 96 |
+
value: 0.9904761904761905
|
| 97 |
+
name: Cosine Accuracy@100
|
| 98 |
+
- type: cosine_accuracy@150
|
| 99 |
+
value: 0.9904761904761905
|
| 100 |
+
name: Cosine Accuracy@150
|
| 101 |
+
- type: cosine_accuracy@200
|
| 102 |
+
value: 0.9904761904761905
|
| 103 |
+
name: Cosine Accuracy@200
|
| 104 |
+
- type: cosine_precision@1
|
| 105 |
+
value: 0.6666666666666666
|
| 106 |
+
name: Cosine Precision@1
|
| 107 |
+
- type: cosine_precision@20
|
| 108 |
+
value: 0.5147619047619048
|
| 109 |
+
name: Cosine Precision@20
|
| 110 |
+
- type: cosine_precision@50
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| 842 |
+
- type: cosine_recall@150
|
| 843 |
+
value: 0.9833768267223383
|
| 844 |
+
name: Cosine Recall@150
|
| 845 |
+
- type: cosine_recall@200
|
| 846 |
+
value: 0.986464857341684
|
| 847 |
+
name: Cosine Recall@200
|
| 848 |
+
- type: cosine_ndcg@1
|
| 849 |
+
value: 0.7667014613778705
|
| 850 |
+
name: Cosine Ndcg@1
|
| 851 |
+
- type: cosine_ndcg@20
|
| 852 |
+
value: 0.8002168358295473
|
| 853 |
+
name: Cosine Ndcg@20
|
| 854 |
+
- type: cosine_ndcg@50
|
| 855 |
+
value: 0.8125113081884888
|
| 856 |
+
name: Cosine Ndcg@50
|
| 857 |
+
- type: cosine_ndcg@100
|
| 858 |
+
value: 0.8167350090334409
|
| 859 |
+
name: Cosine Ndcg@100
|
| 860 |
+
- type: cosine_ndcg@150
|
| 861 |
+
value: 0.8181122471507385
|
| 862 |
+
name: Cosine Ndcg@150
|
| 863 |
+
- type: cosine_ndcg@200
|
| 864 |
+
value: 0.8186874070081017
|
| 865 |
+
name: Cosine Ndcg@200
|
| 866 |
+
- type: cosine_mrr@1
|
| 867 |
+
value: 0.7667014613778705
|
| 868 |
+
name: Cosine Mrr@1
|
| 869 |
+
- type: cosine_mrr@20
|
| 870 |
+
value: 0.8421752732824312
|
| 871 |
+
name: Cosine Mrr@20
|
| 872 |
+
- type: cosine_mrr@50
|
| 873 |
+
value: 0.8424954415974232
|
| 874 |
+
name: Cosine Mrr@50
|
| 875 |
+
- type: cosine_mrr@100
|
| 876 |
+
value: 0.8425358910333786
|
| 877 |
+
name: Cosine Mrr@100
|
| 878 |
+
- type: cosine_mrr@150
|
| 879 |
+
value: 0.8425483391786986
|
| 880 |
+
name: Cosine Mrr@150
|
| 881 |
+
- type: cosine_mrr@200
|
| 882 |
+
value: 0.8425515411459873
|
| 883 |
+
name: Cosine Mrr@200
|
| 884 |
+
- type: cosine_map@1
|
| 885 |
+
value: 0.7667014613778705
|
| 886 |
+
name: Cosine Map@1
|
| 887 |
+
- type: cosine_map@20
|
| 888 |
+
value: 0.7007206423896271
|
| 889 |
+
name: Cosine Map@20
|
| 890 |
+
- type: cosine_map@50
|
| 891 |
+
value: 0.7046277360194696
|
| 892 |
+
name: Cosine Map@50
|
| 893 |
+
- type: cosine_map@100
|
| 894 |
+
value: 0.7053668771050886
|
| 895 |
+
name: Cosine Map@100
|
| 896 |
+
- type: cosine_map@150
|
| 897 |
+
value: 0.7055166914145262
|
| 898 |
+
name: Cosine Map@150
|
| 899 |
+
- type: cosine_map@200
|
| 900 |
+
value: 0.7055658329670217
|
| 901 |
+
name: Cosine Map@200
|
| 902 |
+
- type: cosine_map@500
|
| 903 |
+
value: 0.7056512281794008
|
| 904 |
+
name: Cosine Map@500
|
| 905 |
+
---
|
| 906 |
+
|
| 907 |
+
# Job - Job matching Alibaba-NLP/gte-multilingual-base (v2)
|
| 908 |
+
|
| 909 |
+
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
|
| 910 |
+
|
| 911 |
+
## Model Details
|
| 912 |
+
|
| 913 |
+
### Model Description
|
| 914 |
+
- **Model Type:** Sentence Transformer
|
| 915 |
+
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
|
| 916 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 917 |
+
- **Output Dimensionality:** 768 dimensions
|
| 918 |
+
- **Similarity Function:** Cosine Similarity
|
| 919 |
+
- **Training Datasets:**
|
| 920 |
+
- full_en
|
| 921 |
+
- full_de
|
| 922 |
+
- full_es
|
| 923 |
+
- full_zh
|
| 924 |
+
- mix
|
| 925 |
+
<!-- - **Language:** Unknown -->
|
| 926 |
+
<!-- - **License:** Unknown -->
|
| 927 |
+
|
| 928 |
+
### Model Sources
|
| 929 |
+
|
| 930 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 931 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 932 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 933 |
+
|
| 934 |
+
### Full Model Architecture
|
| 935 |
+
|
| 936 |
+
```
|
| 937 |
+
SentenceTransformer(
|
| 938 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
|
| 939 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 940 |
+
(2): Normalize()
|
| 941 |
+
)
|
| 942 |
+
```
|
| 943 |
+
|
| 944 |
+
## Usage
|
| 945 |
+
|
| 946 |
+
### Direct Usage (Sentence Transformers)
|
| 947 |
+
|
| 948 |
+
First install the Sentence Transformers library:
|
| 949 |
+
|
| 950 |
+
```bash
|
| 951 |
+
pip install -U sentence-transformers
|
| 952 |
+
```
|
| 953 |
+
|
| 954 |
+
Then you can load this model and run inference.
|
| 955 |
+
```python
|
| 956 |
+
from sentence_transformers import SentenceTransformer
|
| 957 |
+
|
| 958 |
+
# Download from the 🤗 Hub
|
| 959 |
+
model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v2")
|
| 960 |
+
# Run inference
|
| 961 |
+
sentences = [
|
| 962 |
+
'Volksvertreter',
|
| 963 |
+
'Parlamentarier',
|
| 964 |
+
'Oberbürgermeister',
|
| 965 |
+
]
|
| 966 |
+
embeddings = model.encode(sentences)
|
| 967 |
+
print(embeddings.shape)
|
| 968 |
+
# [3, 768]
|
| 969 |
+
|
| 970 |
+
# Get the similarity scores for the embeddings
|
| 971 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 972 |
+
print(similarities.shape)
|
| 973 |
+
# [3, 3]
|
| 974 |
+
```
|
| 975 |
+
|
| 976 |
+
<!--
|
| 977 |
+
### Direct Usage (Transformers)
|
| 978 |
+
|
| 979 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 980 |
+
|
| 981 |
+
</details>
|
| 982 |
+
-->
|
| 983 |
+
|
| 984 |
+
<!--
|
| 985 |
+
### Downstream Usage (Sentence Transformers)
|
| 986 |
+
|
| 987 |
+
You can finetune this model on your own dataset.
|
| 988 |
+
|
| 989 |
+
<details><summary>Click to expand</summary>
|
| 990 |
+
|
| 991 |
+
</details>
|
| 992 |
+
-->
|
| 993 |
+
|
| 994 |
+
<!--
|
| 995 |
+
### Out-of-Scope Use
|
| 996 |
+
|
| 997 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 998 |
+
-->
|
| 999 |
+
|
| 1000 |
+
## Evaluation
|
| 1001 |
+
|
| 1002 |
+
### Metrics
|
| 1003 |
+
|
| 1004 |
+
#### Information Retrieval
|
| 1005 |
+
|
| 1006 |
+
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
|
| 1007 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 1008 |
+
|
| 1009 |
+
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|
| 1010 |
+
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
|
| 1011 |
+
| cosine_accuracy@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1012 |
+
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9754 | 0.9806 | 0.9553 | 0.9324 | 0.9843 |
|
| 1013 |
+
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9802 | 0.9683 | 0.9932 |
|
| 1014 |
+
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9849 | 0.9958 |
|
| 1015 |
+
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9938 | 0.9886 | 0.9974 |
|
| 1016 |
+
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9938 | 0.9979 |
|
| 1017 |
+
| cosine_precision@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1018 |
+
| cosine_precision@20 | 0.5148 | 0.5759 | 0.5103 | 0.4883 | 0.1216 | 0.1209 | 0.1387 |
|
| 1019 |
+
| cosine_precision@50 | 0.32 | 0.3923 | 0.3694 | 0.2963 | 0.0512 | 0.0514 | 0.0581 |
|
| 1020 |
+
| cosine_precision@100 | 0.1905 | 0.2566 | 0.2397 | 0.1788 | 0.0261 | 0.0265 | 0.0296 |
|
| 1021 |
+
| cosine_precision@150 | 0.1362 | 0.1928 | 0.1808 | 0.1278 | 0.0175 | 0.0179 | 0.0199 |
|
| 1022 |
+
| cosine_precision@200 | 0.1054 | 0.1528 | 0.1462 | 0.0999 | 0.0132 | 0.0135 | 0.0149 |
|
| 1023 |
+
| cosine_recall@1 | 0.0685 | 0.0036 | 0.0111 | 0.0693 | 0.2738 | 0.2436 | 0.2569 |
|
| 1024 |
+
| cosine_recall@20 | 0.5491 | 0.3853 | 0.3208 | 0.5251 | 0.899 | 0.8787 | 0.9157 |
|
| 1025 |
+
| cosine_recall@50 | 0.7554 | 0.566 | 0.5042 | 0.7083 | 0.9459 | 0.932 | 0.9583 |
|
| 1026 |
+
| cosine_recall@100 | 0.8503 | 0.6899 | 0.6173 | 0.8169 | 0.9651 | 0.9596 | 0.9765 |
|
| 1027 |
+
| cosine_recall@150 | 0.8995 | 0.754 | 0.6848 | 0.8613 | 0.9732 | 0.9718 | 0.9834 |
|
| 1028 |
+
| cosine_recall@200 | 0.9208 | 0.7858 | 0.7253 | 0.8898 | 0.9791 | 0.98 | 0.9865 |
|
| 1029 |
+
| cosine_ndcg@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1030 |
+
| cosine_ndcg@20 | 0.6952 | 0.6169 | 0.5378 | 0.6681 | 0.7815 | 0.7448 | 0.8002 |
|
| 1031 |
+
| cosine_ndcg@50 | 0.723 | 0.5914 | 0.5288 | 0.6857 | 0.7944 | 0.7595 | 0.8125 |
|
| 1032 |
+
| cosine_ndcg@100 | 0.7733 | 0.6235 | 0.5552 | 0.7379 | 0.7986 | 0.7657 | 0.8167 |
|
| 1033 |
+
| cosine_ndcg@150 | 0.7947 | 0.6557 | 0.5888 | 0.7577 | 0.8001 | 0.7682 | 0.8181 |
|
| 1034 |
+
| **cosine_ndcg@200** | **0.8039** | **0.6717** | **0.6092** | **0.7697** | **0.8012** | **0.7696** | **0.8187** |
|
| 1035 |
+
| cosine_mrr@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1036 |
+
| cosine_mrr@20 | 0.8183 | 0.5581 | 0.5165 | 0.8159 | 0.7804 | 0.7324 | 0.8422 |
|
| 1037 |
+
| cosine_mrr@50 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7813 | 0.7335 | 0.8425 |
|
| 1038 |
+
| cosine_mrr@100 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 |
|
| 1039 |
+
| cosine_mrr@150 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 |
|
| 1040 |
+
| cosine_mrr@200 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8426 |
|
| 1041 |
+
| cosine_map@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 |
|
| 1042 |
+
| cosine_map@20 | 0.5566 | 0.4841 | 0.3984 | 0.5222 | 0.7071 | 0.6646 | 0.7007 |
|
| 1043 |
+
| cosine_map@50 | 0.5534 | 0.4304 | 0.3603 | 0.5083 | 0.7107 | 0.6684 | 0.7046 |
|
| 1044 |
+
| cosine_map@100 | 0.5852 | 0.4374 | 0.3632 | 0.5372 | 0.7113 | 0.6693 | 0.7054 |
|
| 1045 |
+
| cosine_map@150 | 0.5943 | 0.4527 | 0.3782 | 0.5454 | 0.7114 | 0.6695 | 0.7055 |
|
| 1046 |
+
| cosine_map@200 | 0.5976 | 0.4593 | 0.3863 | 0.5495 | 0.7115 | 0.6696 | 0.7056 |
|
| 1047 |
+
| cosine_map@500 | 0.6016 | 0.472 | 0.3992 | 0.5542 | 0.7116 | 0.6697 | 0.7057 |
|
| 1048 |
+
|
| 1049 |
+
<!--
|
| 1050 |
+
## Bias, Risks and Limitations
|
| 1051 |
+
|
| 1052 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 1053 |
+
-->
|
| 1054 |
+
|
| 1055 |
+
<!--
|
| 1056 |
+
### Recommendations
|
| 1057 |
+
|
| 1058 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 1059 |
+
-->
|
| 1060 |
+
|
| 1061 |
+
## Training Details
|
| 1062 |
+
|
| 1063 |
+
### Training Datasets
|
| 1064 |
+
<details><summary>full_en</summary>
|
| 1065 |
+
|
| 1066 |
+
#### full_en
|
| 1067 |
+
|
| 1068 |
+
* Dataset: full_en
|
| 1069 |
+
* Size: 28,880 training samples
|
| 1070 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1071 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1072 |
+
| | anchor | positive |
|
| 1073 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1074 |
+
| type | string | string |
|
| 1075 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
|
| 1076 |
+
* Samples:
|
| 1077 |
+
| anchor | positive |
|
| 1078 |
+
|:-----------------------------------------|:-----------------------------------------|
|
| 1079 |
+
| <code>air commodore</code> | <code>flight lieutenant</code> |
|
| 1080 |
+
| <code>command and control officer</code> | <code>flight officer</code> |
|
| 1081 |
+
| <code>air commodore</code> | <code>command and control officer</code> |
|
| 1082 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1083 |
+
```json
|
| 1084 |
+
{'guide': SentenceTransformer(
|
| 1085 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1086 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1087 |
+
(2): Normalize()
|
| 1088 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1089 |
+
```
|
| 1090 |
+
</details>
|
| 1091 |
+
<details><summary>full_de</summary>
|
| 1092 |
+
|
| 1093 |
+
#### full_de
|
| 1094 |
+
|
| 1095 |
+
* Dataset: full_de
|
| 1096 |
+
* Size: 23,023 training samples
|
| 1097 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1098 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1099 |
+
| | anchor | positive |
|
| 1100 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1101 |
+
| type | string | string |
|
| 1102 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
|
| 1103 |
+
* Samples:
|
| 1104 |
+
| anchor | positive |
|
| 1105 |
+
|:----------------------------------|:-----------------------------------------------------|
|
| 1106 |
+
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
|
| 1107 |
+
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
|
| 1108 |
+
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
|
| 1109 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1110 |
+
```json
|
| 1111 |
+
{'guide': SentenceTransformer(
|
| 1112 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1113 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1114 |
+
(2): Normalize()
|
| 1115 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1116 |
+
```
|
| 1117 |
+
</details>
|
| 1118 |
+
<details><summary>full_es</summary>
|
| 1119 |
+
|
| 1120 |
+
#### full_es
|
| 1121 |
+
|
| 1122 |
+
* Dataset: full_es
|
| 1123 |
+
* Size: 20,724 training samples
|
| 1124 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1125 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1126 |
+
| | anchor | positive |
|
| 1127 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1128 |
+
| type | string | string |
|
| 1129 |
+
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
|
| 1130 |
+
* Samples:
|
| 1131 |
+
| anchor | positive |
|
| 1132 |
+
|:------------------------------------|:-------------------------------------------|
|
| 1133 |
+
| <code>jefe de escuadrón</code> | <code>instructor</code> |
|
| 1134 |
+
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
|
| 1135 |
+
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
|
| 1136 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1137 |
+
```json
|
| 1138 |
+
{'guide': SentenceTransformer(
|
| 1139 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1140 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1141 |
+
(2): Normalize()
|
| 1142 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1143 |
+
```
|
| 1144 |
+
</details>
|
| 1145 |
+
<details><summary>full_zh</summary>
|
| 1146 |
+
|
| 1147 |
+
#### full_zh
|
| 1148 |
+
|
| 1149 |
+
* Dataset: full_zh
|
| 1150 |
+
* Size: 30,401 training samples
|
| 1151 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1152 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1153 |
+
| | anchor | positive |
|
| 1154 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1155 |
+
| type | string | string |
|
| 1156 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
|
| 1157 |
+
* Samples:
|
| 1158 |
+
| anchor | positive |
|
| 1159 |
+
|:------------------|:---------------------|
|
| 1160 |
+
| <code>技术总监</code> | <code>技术和运营总监</code> |
|
| 1161 |
+
| <code>技术总监</code> | <code>技术主管</code> |
|
| 1162 |
+
| <code>技术总监</code> | <code>技术艺术总监</code> |
|
| 1163 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1164 |
+
```json
|
| 1165 |
+
{'guide': SentenceTransformer(
|
| 1166 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1167 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1168 |
+
(2): Normalize()
|
| 1169 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1170 |
+
```
|
| 1171 |
+
</details>
|
| 1172 |
+
<details><summary>mix</summary>
|
| 1173 |
+
|
| 1174 |
+
#### mix
|
| 1175 |
+
|
| 1176 |
+
* Dataset: mix
|
| 1177 |
+
* Size: 21,760 training samples
|
| 1178 |
+
* Columns: <code>anchor</code> and <code>positive</code>
|
| 1179 |
+
* Approximate statistics based on the first 1000 samples:
|
| 1180 |
+
| | anchor | positive |
|
| 1181 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
| 1182 |
+
| type | string | string |
|
| 1183 |
+
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
|
| 1184 |
+
* Samples:
|
| 1185 |
+
| anchor | positive |
|
| 1186 |
+
|:------------------------------------------|:----------------------------------------------------------------|
|
| 1187 |
+
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
|
| 1188 |
+
| <code>head of technical</code> | <code>directora técnica</code> |
|
| 1189 |
+
| <code>head of technical department</code> | <code>技术艺术总监</code> |
|
| 1190 |
+
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
|
| 1191 |
+
```json
|
| 1192 |
+
{'guide': SentenceTransformer(
|
| 1193 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| 1194 |
+
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 1195 |
+
(2): Normalize()
|
| 1196 |
+
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
|
| 1197 |
+
```
|
| 1198 |
+
</details>
|
| 1199 |
+
|
| 1200 |
+
### Training Hyperparameters
|
| 1201 |
+
#### Non-Default Hyperparameters
|
| 1202 |
+
|
| 1203 |
+
- `eval_strategy`: steps
|
| 1204 |
+
- `per_device_train_batch_size`: 128
|
| 1205 |
+
- `per_device_eval_batch_size`: 128
|
| 1206 |
+
- `gradient_accumulation_steps`: 2
|
| 1207 |
+
- `num_train_epochs`: 5
|
| 1208 |
+
- `warmup_ratio`: 0.05
|
| 1209 |
+
- `log_on_each_node`: False
|
| 1210 |
+
- `fp16`: True
|
| 1211 |
+
- `dataloader_num_workers`: 4
|
| 1212 |
+
- `ddp_find_unused_parameters`: True
|
| 1213 |
+
- `batch_sampler`: no_duplicates
|
| 1214 |
+
|
| 1215 |
+
#### All Hyperparameters
|
| 1216 |
+
<details><summary>Click to expand</summary>
|
| 1217 |
+
|
| 1218 |
+
- `overwrite_output_dir`: False
|
| 1219 |
+
- `do_predict`: False
|
| 1220 |
+
- `eval_strategy`: steps
|
| 1221 |
+
- `prediction_loss_only`: True
|
| 1222 |
+
- `per_device_train_batch_size`: 128
|
| 1223 |
+
- `per_device_eval_batch_size`: 128
|
| 1224 |
+
- `per_gpu_train_batch_size`: None
|
| 1225 |
+
- `per_gpu_eval_batch_size`: None
|
| 1226 |
+
- `gradient_accumulation_steps`: 2
|
| 1227 |
+
- `eval_accumulation_steps`: None
|
| 1228 |
+
- `torch_empty_cache_steps`: None
|
| 1229 |
+
- `learning_rate`: 5e-05
|
| 1230 |
+
- `weight_decay`: 0.0
|
| 1231 |
+
- `adam_beta1`: 0.9
|
| 1232 |
+
- `adam_beta2`: 0.999
|
| 1233 |
+
- `adam_epsilon`: 1e-08
|
| 1234 |
+
- `max_grad_norm`: 1.0
|
| 1235 |
+
- `num_train_epochs`: 5
|
| 1236 |
+
- `max_steps`: -1
|
| 1237 |
+
- `lr_scheduler_type`: linear
|
| 1238 |
+
- `lr_scheduler_kwargs`: {}
|
| 1239 |
+
- `warmup_ratio`: 0.05
|
| 1240 |
+
- `warmup_steps`: 0
|
| 1241 |
+
- `log_level`: passive
|
| 1242 |
+
- `log_level_replica`: warning
|
| 1243 |
+
- `log_on_each_node`: False
|
| 1244 |
+
- `logging_nan_inf_filter`: True
|
| 1245 |
+
- `save_safetensors`: True
|
| 1246 |
+
- `save_on_each_node`: False
|
| 1247 |
+
- `save_only_model`: False
|
| 1248 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 1249 |
+
- `no_cuda`: False
|
| 1250 |
+
- `use_cpu`: False
|
| 1251 |
+
- `use_mps_device`: False
|
| 1252 |
+
- `seed`: 42
|
| 1253 |
+
- `data_seed`: None
|
| 1254 |
+
- `jit_mode_eval`: False
|
| 1255 |
+
- `use_ipex`: False
|
| 1256 |
+
- `bf16`: False
|
| 1257 |
+
- `fp16`: True
|
| 1258 |
+
- `fp16_opt_level`: O1
|
| 1259 |
+
- `half_precision_backend`: auto
|
| 1260 |
+
- `bf16_full_eval`: False
|
| 1261 |
+
- `fp16_full_eval`: False
|
| 1262 |
+
- `tf32`: None
|
| 1263 |
+
- `local_rank`: 0
|
| 1264 |
+
- `ddp_backend`: None
|
| 1265 |
+
- `tpu_num_cores`: None
|
| 1266 |
+
- `tpu_metrics_debug`: False
|
| 1267 |
+
- `debug`: []
|
| 1268 |
+
- `dataloader_drop_last`: True
|
| 1269 |
+
- `dataloader_num_workers`: 4
|
| 1270 |
+
- `dataloader_prefetch_factor`: None
|
| 1271 |
+
- `past_index`: -1
|
| 1272 |
+
- `disable_tqdm`: False
|
| 1273 |
+
- `remove_unused_columns`: True
|
| 1274 |
+
- `label_names`: None
|
| 1275 |
+
- `load_best_model_at_end`: False
|
| 1276 |
+
- `ignore_data_skip`: False
|
| 1277 |
+
- `fsdp`: []
|
| 1278 |
+
- `fsdp_min_num_params`: 0
|
| 1279 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 1280 |
+
- `tp_size`: 0
|
| 1281 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 1282 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 1283 |
+
- `deepspeed`: None
|
| 1284 |
+
- `label_smoothing_factor`: 0.0
|
| 1285 |
+
- `optim`: adamw_torch
|
| 1286 |
+
- `optim_args`: None
|
| 1287 |
+
- `adafactor`: False
|
| 1288 |
+
- `group_by_length`: False
|
| 1289 |
+
- `length_column_name`: length
|
| 1290 |
+
- `ddp_find_unused_parameters`: True
|
| 1291 |
+
- `ddp_bucket_cap_mb`: None
|
| 1292 |
+
- `ddp_broadcast_buffers`: False
|
| 1293 |
+
- `dataloader_pin_memory`: True
|
| 1294 |
+
- `dataloader_persistent_workers`: False
|
| 1295 |
+
- `skip_memory_metrics`: True
|
| 1296 |
+
- `use_legacy_prediction_loop`: False
|
| 1297 |
+
- `push_to_hub`: False
|
| 1298 |
+
- `resume_from_checkpoint`: None
|
| 1299 |
+
- `hub_model_id`: None
|
| 1300 |
+
- `hub_strategy`: every_save
|
| 1301 |
+
- `hub_private_repo`: None
|
| 1302 |
+
- `hub_always_push`: False
|
| 1303 |
+
- `gradient_checkpointing`: False
|
| 1304 |
+
- `gradient_checkpointing_kwargs`: None
|
| 1305 |
+
- `include_inputs_for_metrics`: False
|
| 1306 |
+
- `include_for_metrics`: []
|
| 1307 |
+
- `eval_do_concat_batches`: True
|
| 1308 |
+
- `fp16_backend`: auto
|
| 1309 |
+
- `push_to_hub_model_id`: None
|
| 1310 |
+
- `push_to_hub_organization`: None
|
| 1311 |
+
- `mp_parameters`:
|
| 1312 |
+
- `auto_find_batch_size`: False
|
| 1313 |
+
- `full_determinism`: False
|
| 1314 |
+
- `torchdynamo`: None
|
| 1315 |
+
- `ray_scope`: last
|
| 1316 |
+
- `ddp_timeout`: 1800
|
| 1317 |
+
- `torch_compile`: False
|
| 1318 |
+
- `torch_compile_backend`: None
|
| 1319 |
+
- `torch_compile_mode`: None
|
| 1320 |
+
- `include_tokens_per_second`: False
|
| 1321 |
+
- `include_num_input_tokens_seen`: False
|
| 1322 |
+
- `neftune_noise_alpha`: None
|
| 1323 |
+
- `optim_target_modules`: None
|
| 1324 |
+
- `batch_eval_metrics`: False
|
| 1325 |
+
- `eval_on_start`: False
|
| 1326 |
+
- `use_liger_kernel`: False
|
| 1327 |
+
- `eval_use_gather_object`: False
|
| 1328 |
+
- `average_tokens_across_devices`: False
|
| 1329 |
+
- `prompts`: None
|
| 1330 |
+
- `batch_sampler`: no_duplicates
|
| 1331 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 1332 |
+
|
| 1333 |
+
</details>
|
| 1334 |
+
|
| 1335 |
+
### Training Logs
|
| 1336 |
+
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|
| 1337 |
+
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
|
| 1338 |
+
| -1 | -1 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.7437 |
|
| 1339 |
+
| 0.0082 | 1 | 4.3088 | - | - | - | - | - | - | - |
|
| 1340 |
+
| 0.8230 | 100 | 1.9026 | - | - | - | - | - | - | - |
|
| 1341 |
+
| 1.6502 | 200 | 0.9336 | 0.8024 | 0.6703 | 0.6109 | 0.7695 | 0.7914 | 0.7594 | 0.8136 |
|
| 1342 |
+
| 2.4774 | 300 | 0.161 | - | - | - | - | - | - | - |
|
| 1343 |
+
| 3.3045 | 400 | 0.1398 | 0.8039 | 0.6717 | 0.6092 | 0.7697 | 0.8012 | 0.7696 | 0.8187 |
|
| 1344 |
+
|
| 1345 |
+
|
| 1346 |
+
### Framework Versions
|
| 1347 |
+
- Python: 3.11.11
|
| 1348 |
+
- Sentence Transformers: 4.1.0
|
| 1349 |
+
- Transformers: 4.51.2
|
| 1350 |
+
- PyTorch: 2.6.0+cu124
|
| 1351 |
+
- Accelerate: 1.6.0
|
| 1352 |
+
- Datasets: 3.5.0
|
| 1353 |
+
- Tokenizers: 0.21.1
|
| 1354 |
+
|
| 1355 |
+
## Citation
|
| 1356 |
+
|
| 1357 |
+
### BibTeX
|
| 1358 |
+
|
| 1359 |
+
#### Sentence Transformers
|
| 1360 |
+
```bibtex
|
| 1361 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 1362 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 1363 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 1364 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 1365 |
+
month = "11",
|
| 1366 |
+
year = "2019",
|
| 1367 |
+
publisher = "Association for Computational Linguistics",
|
| 1368 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 1369 |
+
}
|
| 1370 |
+
```
|
| 1371 |
+
|
| 1372 |
+
#### GISTEmbedLoss
|
| 1373 |
+
```bibtex
|
| 1374 |
+
@misc{solatorio2024gistembed,
|
| 1375 |
+
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
|
| 1376 |
+
author={Aivin V. Solatorio},
|
| 1377 |
+
year={2024},
|
| 1378 |
+
eprint={2402.16829},
|
| 1379 |
+
archivePrefix={arXiv},
|
| 1380 |
+
primaryClass={cs.LG}
|
| 1381 |
+
}
|
| 1382 |
+
```
|
| 1383 |
+
|
| 1384 |
+
<!--
|
| 1385 |
+
## Glossary
|
| 1386 |
+
|
| 1387 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 1388 |
+
-->
|
| 1389 |
+
|
| 1390 |
+
<!--
|
| 1391 |
+
## Model Card Authors
|
| 1392 |
+
|
| 1393 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 1394 |
+
-->
|
| 1395 |
+
|
| 1396 |
+
<!--
|
| 1397 |
+
## Model Card Contact
|
| 1398 |
+
|
| 1399 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 1400 |
+
-->
|
checkpoint-400/config.json
ADDED
|
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"NewModel"
|
| 4 |
+
],
|
| 5 |
+
"attention_probs_dropout_prob": 0.0,
|
| 6 |
+
"auto_map": {
|
| 7 |
+
"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
|
| 8 |
+
"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
|
| 9 |
+
"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
|
| 10 |
+
"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
|
| 11 |
+
"AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
|
| 12 |
+
"AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
|
| 13 |
+
"AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
|
| 14 |
+
},
|
| 15 |
+
"classifier_dropout": 0.0,
|
| 16 |
+
"hidden_act": "gelu",
|
| 17 |
+
"hidden_dropout_prob": 0.1,
|
| 18 |
+
"hidden_size": 768,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "LABEL_0"
|
| 21 |
+
},
|
| 22 |
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|
| 23 |
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"intermediate_size": 3072,
|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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"logn_attention_clip1": false,
|
| 30 |
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"logn_attention_scale": false,
|
| 31 |
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"max_position_embeddings": 8192,
|
| 32 |
+
"model_type": "new",
|
| 33 |
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"num_attention_heads": 12,
|
| 34 |
+
"num_hidden_layers": 12,
|
| 35 |
+
"pack_qkv": true,
|
| 36 |
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"pad_token_id": 1,
|
| 37 |
+
"position_embedding_type": "rope",
|
| 38 |
+
"rope_scaling": {
|
| 39 |
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"factor": 8.0,
|
| 40 |
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"type": "ntk"
|
| 41 |
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},
|
| 42 |
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"rope_theta": 20000,
|
| 43 |
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"torch_dtype": "float32",
|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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"use_memory_efficient_attention": false,
|
| 48 |
+
"vocab_size": 250048
|
| 49 |
+
}
|
checkpoint-400/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
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|
|
| 1 |
+
{
|
| 2 |
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"__version__": {
|
| 3 |
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"sentence_transformers": "4.1.0",
|
| 4 |
+
"transformers": "4.51.2",
|
| 5 |
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"pytorch": "2.6.0+cu124"
|
| 6 |
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},
|
| 7 |
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"prompts": {},
|
| 8 |
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"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
checkpoint-400/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 1221487872
|
checkpoint-400/modules.json
ADDED
|
@@ -0,0 +1,20 @@
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|
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|
|
|
|
|
|
| 1 |
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[
|
| 2 |
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{
|
| 3 |
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"idx": 0,
|
| 4 |
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"name": "0",
|
| 5 |
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"path": "",
|
| 6 |
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"type": "sentence_transformers.models.Transformer"
|
| 7 |
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},
|
| 8 |
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{
|
| 9 |
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"idx": 1,
|
| 10 |
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"name": "1",
|
| 11 |
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"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
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},
|
| 14 |
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{
|
| 15 |
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"idx": 2,
|
| 16 |
+
"name": "2",
|
| 17 |
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"path": "2_Normalize",
|
| 18 |
+
"type": "sentence_transformers.models.Normalize"
|
| 19 |
+
}
|
| 20 |
+
]
|
checkpoint-400/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 2443060986
|
checkpoint-400/rng_state_0.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:0344e55875506625799735a316cda354931058883283953c51ab59ea9e0f9513
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| 3 |
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size 15984
|
checkpoint-400/scaler.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 988
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checkpoint-400/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 1064
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checkpoint-400/sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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|
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|
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|
| 1 |
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{
|
| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
checkpoint-400/special_tokens_map.json
ADDED
|
@@ -0,0 +1,51 @@
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|
| 1 |
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{
|
| 2 |
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"bos_token": {
|
| 3 |
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"content": "<s>",
|
| 4 |
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"lstrip": false,
|
| 5 |
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"normalized": false,
|
| 6 |
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"rstrip": false,
|
| 7 |
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"single_word": false
|
| 8 |
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},
|
| 9 |
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"cls_token": {
|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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"eos_token": {
|
| 17 |
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"content": "</s>",
|
| 18 |
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"lstrip": false,
|
| 19 |
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"normalized": false,
|
| 20 |
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"rstrip": false,
|
| 21 |
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"single_word": false
|
| 22 |
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},
|
| 23 |
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"mask_token": {
|
| 24 |
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"content": "<mask>",
|
| 25 |
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"lstrip": true,
|
| 26 |
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"normalized": false,
|
| 27 |
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"rstrip": false,
|
| 28 |
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"single_word": false
|
| 29 |
+
},
|
| 30 |
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"pad_token": {
|
| 31 |
+
"content": "<pad>",
|
| 32 |
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"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
},
|
| 37 |
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"sep_token": {
|
| 38 |
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"content": "</s>",
|
| 39 |
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"lstrip": false,
|
| 40 |
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"normalized": false,
|
| 41 |
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"rstrip": false,
|
| 42 |
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"single_word": false
|
| 43 |
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},
|
| 44 |
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"unk_token": {
|
| 45 |
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"content": "<unk>",
|
| 46 |
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|
| 47 |
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"normalized": false,
|
| 48 |
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"rstrip": false,
|
| 49 |
+
"single_word": false
|
| 50 |
+
}
|
| 51 |
+
}
|
checkpoint-400/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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| 3 |
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size 17082987
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checkpoint-400/tokenizer_config.json
ADDED
|
@@ -0,0 +1,55 @@
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|
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|
| 1 |
+
{
|
| 2 |
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"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<s>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
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"1": {
|
| 12 |
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"content": "<pad>",
|
| 13 |
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"lstrip": false,
|
| 14 |
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"normalized": false,
|
| 15 |
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"rstrip": false,
|
| 16 |
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"single_word": false,
|
| 17 |
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"special": true
|
| 18 |
+
},
|
| 19 |
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"2": {
|
| 20 |
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"content": "</s>",
|
| 21 |
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"lstrip": false,
|
| 22 |
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"normalized": false,
|
| 23 |
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"rstrip": false,
|
| 24 |
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"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
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"3": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
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"normalized": false,
|
| 31 |
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"rstrip": false,
|
| 32 |
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"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"250001": {
|
| 36 |
+
"content": "<mask>",
|
| 37 |
+
"lstrip": true,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"bos_token": "<s>",
|
| 45 |
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"clean_up_tokenization_spaces": true,
|
| 46 |
+
"cls_token": "<s>",
|
| 47 |
+
"eos_token": "</s>",
|
| 48 |
+
"extra_special_tokens": {},
|
| 49 |
+
"mask_token": "<mask>",
|
| 50 |
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"model_max_length": 8192,
|
| 51 |
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"pad_token": "<pad>",
|
| 52 |
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"sep_token": "</s>",
|
| 53 |
+
"tokenizer_class": "XLMRobertaTokenizerFast",
|
| 54 |
+
"unk_token": "<unk>"
|
| 55 |
+
}
|
checkpoint-400/trainer_state.json
ADDED
|
@@ -0,0 +1,603 @@
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epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
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1.6502057613168724,200,0.2955665024630542,0.9852216748768473,0.9852216748768473,0.9901477832512315,0.9901477832512315,0.9901477832512315,0.2955665024630542,0.01108543831680986,0.5073891625615764,0.31887986522832873,0.3681773399014779,0.5004342335550164,0.24177339901477832,0.6244233924508789,0.18187192118226603,0.687486468465792,0.1470689655172414,0.7334854348170513,0.2955665024630542,0.5140785596450613,0.5140785596450613,0.5141580130059386,0.5141580130059386,0.5141580130059386,0.2955665024630542,0.5342874353496432,0.5251712513704461,0.5568512483442096,0.5891923427833955,0.6108910915140433,0.2955665024630542,0.3952556219642319,0.3565895386599598,0.3618162919366333,0.37673093284239206,0.3850375691141728,0.3976475131909832
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| 3 |
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3.3045267489711936,400,0.2955665024630542,0.9753694581280788,0.9852216748768473,0.9901477832512315,0.9901477832512315,0.9901477832512315,0.2955665024630542,0.01108543831680986,0.5103448275862069,0.3207974783481294,0.36935960591133016,0.5042046446720455,0.23965517241379314,0.6172666777909689,0.1807881773399015,0.6848138831682932,0.1461576354679803,0.7253195006357535,0.2955665024630542,0.5164773875147672,0.5167647438366063,0.5168213657719442,0.5168213657719442,0.5168213657719442,0.2955665024630542,0.537849085734973,0.5288037060639387,0.5551941695921919,0.5887611959940118,0.6092219717029682,0.2955665024630542,0.398398563122481,0.36032758502543594,0.3632259128424842,0.37822275477623696,0.3863148456840816,0.399227009561676
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| 4 |
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4.954732510288066,600,0.2955665024630542,0.9753694581280788,0.9802955665024631,0.9901477832512315,0.9901477832512315,0.9901477832512315,0.2955665024630542,0.01108543831680986,0.512807881773399,0.3237955971074792,0.36807881773399015,0.5030929796037713,0.24029556650246306,0.6172851336056435,0.18111658456486043,0.6855584980130183,0.14665024630541873,0.7279125666778575,0.2955665024630542,0.5145867542419259,0.5147235905856588,0.5148895627540307,0.5148895627540307,0.5148895627540307,0.2955665024630542,0.5399334191840455,0.5279243511614918,0.5555133187133253,0.5891570214465959,0.6103222576369508,0.2955665024630542,0.40047661522515754,0.3595621891952358,0.3631770989458169,0.37785921428477304,0.38624613510168,0.3990985450532001
|
eval/Information-Retrieval_evaluation_full_en_results.csv
ADDED
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@@ -0,0 +1,4 @@
|
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|
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|
|
|
|
|
|
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| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.6571428571428571,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6571428571428571,0.06695957251887064,0.5133333333333332,0.5478306503729546,0.3169523809523809,0.7470276357469449,0.18971428571428572,0.8467073011936345,0.13619047619047617,0.9010846211520122,0.10561904761904761,0.9256595392715059,0.6571428571428571,0.8111111111111111,0.8111111111111111,0.8111111111111111,0.8111111111111111,0.8111111111111111,0.6571428571428571,0.6923506957704934,0.7170311913169547,0.7690946845916871,0.7923061459636489,0.8023952171736648,0.6571428571428571,0.5516314386214587,0.5474217433291914,0.5799091076338031,0.5895042547793764,0.5930550248640567,0.5967311945998978
|
| 3 |
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3.3045267489711936,400,0.6666666666666666,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6666666666666666,0.06854687410617222,0.5147619047619048,0.5491240579458434,0.31999999999999995,0.7553654907661455,0.19047619047619047,0.8503209224897438,0.1361904761904762,0.8994749092946579,0.10542857142857143,0.9207884118691805,0.6666666666666666,0.8182539682539683,0.8182539682539683,0.8182539682539683,0.8182539682539683,0.8182539682539683,0.6666666666666666,0.6952098522285352,0.7229572913271685,0.7732532874348539,0.7947334799125039,0.8038564389556094,0.6666666666666666,0.5566401101002375,0.55344017265156,0.5852249415484134,0.5943042662925763,0.5975837437975446,0.6015742986218369
|
| 4 |
+
4.954732510288066,600,0.6666666666666666,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.9904761904761905,0.6666666666666666,0.06854687410617222,0.5147619047619048,0.5470067005308932,0.3182857142857143,0.7505056727432228,0.18971428571428572,0.8478262642391493,0.1354920634920635,0.8922827077083945,0.10542857142857143,0.9231956217242554,0.6666666666666666,0.8193650793650795,0.8193650793650795,0.8193650793650795,0.8193650793650795,0.8193650793650795,0.6666666666666666,0.6953331094276901,0.7205492972737558,0.7716799081442828,0.7920452317168161,0.8038291002924913,0.6666666666666666,0.5564682301725326,0.5511565436923715,0.5830965448117339,0.5920235672028571,0.5958093906842231,0.5997006503367213
|
eval/Information-Retrieval_evaluation_full_es_results.csv
ADDED
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@@ -0,0 +1,4 @@
|
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|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.11891891891891893,1.0,1.0,1.0,1.0,1.0,0.11891891891891893,0.0035436931012884127,0.5767567567567567,0.3862419782331355,0.3907027027027027,0.5625768407738393,0.2541621621621622,0.6836436316189977,0.19225225225225226,0.7496865406970199,0.15264864864864866,0.7852629043380305,0.11891891891891893,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.11891891891891893,0.6158554243812342,0.5886857089260162,0.6196114606257926,0.6530674955405338,0.670287400819268,0.11891891891891893,0.4839539531842883,0.4288206349412292,0.43522297182400527,0.4511056582755023,0.45802493743471273,0.47075604946048677
|
| 3 |
+
3.3045267489711936,400,0.12432432432432433,1.0,1.0,1.0,1.0,1.0,0.12432432432432433,0.0036138931714884822,0.575945945945946,0.3852888120551914,0.3923243243243244,0.5659574514538841,0.2565945945945946,0.6898678629281393,0.19282882882882882,0.7540209165372845,0.1527837837837838,0.7858170054407897,0.12432432432432433,0.5581081081081081,0.5581081081081081,0.5581081081081081,0.5581081081081081,0.5581081081081081,0.12432432432432433,0.6168674053047035,0.5913690595071309,0.62350509928888,0.6556716735369459,0.6716557949894583,0.12432432432432433,0.48407152706202555,0.43043374125481026,0.43735327570764515,0.45269435912524697,0.45930097680668164,0.47204219228541466
|
| 4 |
+
4.954732510288066,600,0.11891891891891893,1.0,1.0,1.0,1.0,1.0,0.11891891891891893,0.0035747235671014874,0.5716216216216217,0.38238488508523605,0.3897297297297297,0.5589774933986379,0.25486486486486487,0.6809046903877798,0.19196396396396398,0.7492085330846164,0.15216216216216216,0.7841796161358201,0.11891891891891893,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.5554054054054054,0.11891891891891893,0.6129390001663928,0.5871887749995743,0.6186776604605951,0.6521363774137173,0.6690500870831251,0.11891891891891893,0.48004412760250637,0.42709096639640703,0.43383015161725486,0.4492558751565027,0.4560059854501064,0.4687334978259114
|
eval/Information-Retrieval_evaluation_full_zh_results.csv
ADDED
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@@ -0,0 +1,4 @@
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| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.6601941747572816,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6601941747572816,0.06669332811942774,0.4868932038834952,0.52040897323663,0.2959223300970874,0.7067236634036261,0.17902912621359218,0.813864097315397,0.12912621359223303,0.8683619147921042,0.10063106796116503,0.8964210248615742,0.6601941747572816,0.8068423485899215,0.8068423485899215,0.8068423485899215,0.8068423485899215,0.8068423485899215,0.6601941747572816,0.6629898844211244,0.682216395408567,0.7344118850318737,0.7580048379992059,0.769464510105362,0.6601941747572816,0.5176817014415404,0.5050961591489588,0.5346277197767966,0.5441006347287816,0.547804939644668,0.5524877228701637
|
| 3 |
+
3.3045267489711936,400,0.6796116504854369,0.9805825242718447,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6796116504854369,0.06931865009287731,0.488349514563107,0.5250914458143515,0.29631067961165053,0.7082715439925011,0.17883495145631062,0.8169166539243944,0.12776699029126212,0.8613232254521018,0.09990291262135924,0.8898175710074696,0.6796116504854369,0.8158576051779936,0.816279724215562,0.816279724215562,0.816279724215562,0.816279724215562,0.6796116504854369,0.6680745295820606,0.6856578240865067,0.7378907298421352,0.7576651805692517,0.7696718049970358,0.6796116504854369,0.522177160195635,0.5082601209392789,0.5371705298206915,0.5454012672534121,0.5494570875591636,0.5542116087189223
|
| 4 |
+
4.954732510288066,600,0.6699029126213593,0.9805825242718447,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.9902912621359223,0.6699029126213593,0.0668914656268579,0.48446601941747586,0.5216552728717982,0.29300970873786414,0.6977673292298668,0.17815533980582518,0.8121070659747489,0.12724919093851134,0.8591188085882469,0.09990291262135924,0.8883403241892006,0.6699029126213593,0.8110032362459547,0.811465557096625,0.811465557096625,0.811465557096625,0.811465557096625,0.6699029126213593,0.6644952763622808,0.6798029503509618,0.7344694576412091,0.7548761299269686,0.7675810647559893,0.6699029126213593,0.5195778690550232,0.5053363422108585,0.5348207175322741,0.5430669760209089,0.5474770834562246,0.5521257951989472
|
eval/Information-Retrieval_evaluation_mix_de_results.csv
ADDED
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@@ -0,0 +1,4 @@
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| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.642225689027561,0.9240769630785232,0.9635985439417577,0.9771190847633905,0.984919396775871,0.9901196047841914,0.642225689027561,0.2405616224648986,0.11911076443057722,0.8650459351707401,0.05086843473738951,0.9226295718495406,0.0261622464898596,0.9480412549835328,0.01770844167100017,0.9618651412723176,0.013424336973478942,0.9720922170220142,0.642225689027561,0.7246816496840639,0.7260235454700952,0.7262168772880452,0.7262822017289415,0.7263128860080087,0.642225689027561,0.7332013199323174,0.7490333180034867,0.7547612967303503,0.7575184392863841,0.7593986816807992,0.642225689027561,0.6521189972338849,0.6561813596290409,0.6570111325791598,0.6572712744212402,0.6574012324541948,0.6575399010277455
|
| 3 |
+
3.3045267489711936,400,0.6484659386375455,0.9323972958918356,0.968278731149246,0.984919396775871,0.9885595423816953,0.9937597503900156,0.6484659386375455,0.2435517420696828,0.12093083723348932,0.87873114924597,0.05140925637025482,0.9319899462645173,0.02647425897035882,0.9596117178020455,0.017892182353960822,0.9718322066215982,0.013530941237649509,0.9799791991679667,0.6484659386375455,0.7323691045739125,0.733538875120878,0.733776247038599,0.7338087409764548,0.7338398642058079,0.6484659386375455,0.7448150588358,0.7595232400510039,0.7656851368194345,0.7681576326024331,0.7696474672652458,0.6484659386375455,0.6646138211839377,0.6683657128313888,0.6692634410264182,0.669518875077899,0.6696171599377958,0.6697127210085475
|
| 4 |
+
4.954732510288066,600,0.6526261050442018,0.9381175247009881,0.968278731149246,0.984399375975039,0.9890795631825273,0.9937597503900156,0.6526261050442018,0.24476512393829086,0.12171086843473738,0.8842780377881783,0.051513260530421226,0.9338100190674293,0.026557462298491943,0.9620384815392615,0.017919916796671865,0.9734789391575663,0.01352834113364535,0.9799791991679667,0.6526261050442018,0.7361257905522858,0.7371463016806762,0.7373955457270671,0.7374354870723953,0.7374620339191081,0.6526261050442018,0.7490629318893474,0.7628601222905265,0.769213497844745,0.7714755020563181,0.7726559015412698,0.6526261050442018,0.6683439802461194,0.672011814394583,0.6729253835478308,0.6731548906218254,0.6732301986902165,0.6733271347787819
|
eval/Information-Retrieval_evaluation_mix_es_results.csv
ADDED
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@@ -0,0 +1,4 @@
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| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.7009880395215808,0.9474778991159646,0.9776391055642226,0.9885595423816953,0.9921996879875195,0.9932397295891836,0.7009880395215808,0.27067577941212884,0.11968278731149247,0.8850840700294678,0.05085803432137287,0.9390968972092216,0.02598543941757671,0.9599497313225862,0.017493499739989597,0.9695527821112844,0.013198127925117008,0.9758970358814353,0.7009880395215808,0.7712491671812917,0.7722842539435679,0.7724347923967887,0.7724644404043258,0.7724705526191206,0.7009880395215808,0.7690336236998598,0.7838732562697655,0.7884317468596705,0.7902844804245556,0.7913994944724545,0.7009880395215808,0.6938173897965141,0.6978248868009254,0.6984889579958145,0.6986621032108891,0.6987465392575996,0.6988876342368443
|
| 3 |
+
3.3045267489711936,400,0.7087883515340614,0.9552782111284451,0.9802392095683827,0.9901196047841914,0.9937597503900156,0.9958398335933437,0.7087883515340614,0.2737959042171211,0.12158086323452937,0.8990032934650719,0.05122204888195529,0.9459438377535101,0.026125845033801356,0.9650979372508233,0.017548968625411682,0.9731582596637198,0.013239729589183572,0.979086496793205,0.7087883515340614,0.7804158804359833,0.7812547046826683,0.7813961782842836,0.7814280971923943,0.7814392363829243,0.7087883515340614,0.7814741332820433,0.7944033394497885,0.7986024294603647,0.8001222520801115,0.801183843730514,0.7087883515340614,0.7070596364024803,0.7106867578203881,0.7112928928384499,0.7114314004578745,0.711504950521157,0.7116431478000537
|
| 4 |
+
4.954732510288066,600,0.7113884555382215,0.9578783151326054,0.9802392095683827,0.9911596463858554,0.9942797711908476,0.9963598543941757,0.7113884555382215,0.2743592600846891,0.12225689027561103,0.9034494713121858,0.05128445137805513,0.9468365401282718,0.026136245449817998,0.9653579476512393,0.017562835846767204,0.973678280464552,0.013244929797191891,0.979259837060149,0.7113884555382215,0.7831032164843245,0.7838272775216667,0.7839849724965707,0.7840120488977712,0.7840241592348755,0.7113884555382215,0.7855908925287227,0.7974527919158213,0.8015109259174463,0.8030947148671981,0.8040944464945255,0.7113884555382215,0.711471856133734,0.7147402910660883,0.7153107216129952,0.715461327633746,0.7155310997514029,0.7156626841865296
|
eval/Information-Retrieval_evaluation_mix_zh_results.csv
ADDED
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@@ -0,0 +1,4 @@
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| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@20,cosine-Accuracy@50,cosine-Accuracy@100,cosine-Accuracy@150,cosine-Accuracy@200,cosine-Precision@1,cosine-Recall@1,cosine-Precision@20,cosine-Recall@20,cosine-Precision@50,cosine-Recall@50,cosine-Precision@100,cosine-Recall@100,cosine-Precision@150,cosine-Recall@150,cosine-Precision@200,cosine-Recall@200,cosine-MRR@1,cosine-MRR@20,cosine-MRR@50,cosine-MRR@100,cosine-MRR@150,cosine-MRR@200,cosine-NDCG@1,cosine-NDCG@20,cosine-NDCG@50,cosine-NDCG@100,cosine-NDCG@150,cosine-NDCG@200,cosine-MAP@1,cosine-MAP@20,cosine-MAP@50,cosine-MAP@100,cosine-MAP@150,cosine-MAP@200,cosine-MAP@500
|
| 2 |
+
1.6502057613168724,200,0.7713987473903967,0.9806889352818372,0.9916492693110647,0.9947807933194155,0.9963465553235908,0.9973903966597077,0.7713987473903967,0.2585731683069888,0.13656054279749477,0.9014352818371607,0.05762004175365346,0.950347947112039,0.02944676409185805,0.9715031315240084,0.0197633959638135,0.9781576200417537,0.014877348643006268,0.9818110647181629,0.7713987473903967,0.8432785828350186,0.8436385628906108,0.8436803457907981,0.8436922193976949,0.8436986631082636,0.7713987473903967,0.7926986810043013,0.8066848794942646,0.8115576206060865,0.8129087269558002,0.8135973837485255,0.7713987473903967,0.6929147114308428,0.6972607407491801,0.6981100717727863,0.6982601227257159,0.6983171494463136,0.6984116893552017
|
| 3 |
+
3.3045267489711936,400,0.7667014613778705,0.9843423799582464,0.9932150313152401,0.9958246346555324,0.9973903966597077,0.9979123173277662,0.7667014613778705,0.25692041952480366,0.13870041753653445,0.9156576200417536,0.05810020876826725,0.9582637439109255,0.029598121085595,0.9765483646485734,0.01986778009742519,0.9833768267223383,0.014945198329853866,0.986464857341684,0.7667014613778705,0.8421752732824312,0.8424954415974232,0.8425358910333786,0.8425483391786986,0.8425515411459873,0.7667014613778705,0.8002168358295473,0.8125113081884888,0.8167350090334409,0.8181122471507385,0.8186874070081017,0.7667014613778705,0.7007206423896271,0.7046277360194696,0.7053668771050886,0.7055166914145262,0.7055658329670217,0.7056512281794008
|
| 4 |
+
4.954732510288066,600,0.7713987473903967,0.9853862212943633,0.994258872651357,0.9968684759916493,0.9973903966597077,0.9984342379958246,0.7713987473903967,0.25851227756238193,0.1394832985386221,0.9209203201113431,0.0581837160751566,0.9596555323590814,0.0296659707724426,0.9788100208768268,0.019888656924147527,0.9843771746694502,0.014968684759916497,0.988204592901879,0.7713987473903967,0.8448394509501435,0.8451478929977786,0.845185992264558,0.845190415321067,0.8451967261265305,0.7713987473903967,0.8040681543125745,0.8152493764658302,0.8196701648084599,0.8208022806585027,0.8214985185492638,0.7713987473903967,0.7044865019071646,0.7080890700816939,0.7088637949529534,0.7089946233102989,0.7090485452746494,0.709122690469693
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