AhmedZaky1/DIMI-embedding-v2

SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base. 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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Alibaba-NLP/gte-multilingual-base
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'لا أعتقد ذلك',
    'أخشى لا يا سيدي',
    'رجل واحد في قميص برتقالي يرتدي خوذة بيضاء يركب دراجة.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Dataset: arabic-sts17
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8113
spearman_cosine	0.8156

Training Details

Training Dataset

Unnamed Dataset

• Size: 498,670 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 4 tokens mean: 19.59 tokens max: 82 tokens	min: 4 tokens mean: 13.98 tokens max: 69 tokens

• Samples:

  sentence_0	sentence_1
  ولد صغير يرتدي ملابس زرقاء يرتدي حذاء	الصبي الصغير يرتدي ملابسه
  كيف يتم بناء كاميرات المراقبة؟	ما هي كاميرا المراقبة؟
  لماذا الطاقة الإجمالية للكون صفر؟	إذا كان إجمالي الطاقة في الكون صفر، فهل يعني ذلك أن هناك طريقة لـ "صنع" المادة/الطاقة من خلال صنع نوع من النظير؟

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          384,
          128
      ],
      "matryoshka_weights": [
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 24
• per_device_eval_batch_size: 24
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 24
• per_device_eval_batch_size: 24
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	arabic-sts17_spearman_cosine
0.0481	500	1.6592	-
0.0963	1000	1.177	-
0.1444	1500	1.0053	-
0.1925	2000	0.9125	0.8135
0.2406	2500	0.8212	-
0.2888	3000	0.8204	-
0.3369	3500	0.7696	-
0.3850	4000	0.7501	0.8089
0.4332	4500	0.7118	-
0.4813	5000	0.7073	-
0.5294	5500	0.6772	-
0.5775	6000	0.6637	0.8085
0.6257	6500	0.6507	-
0.6738	7000	0.605	-
0.7219	7500	0.6076	-
0.7700	8000	0.6076	0.8060
0.8182	8500	0.5594	-
0.8663	9000	0.5928	-
0.9144	9500	0.5587	-
0.9626	10000	0.5736	0.8099
1.0	10389	-	0.8122
1.0107	10500	0.555	-
1.0588	11000	0.5233	-
1.1069	11500	0.5216	-
1.1551	12000	0.5176	0.8015
1.2032	12500	0.4865	-
1.2513	13000	0.4907	-
1.2995	13500	0.5079	-
1.3476	14000	0.4991	0.8027
1.3957	14500	0.4834	-
1.4438	15000	0.4626	-
1.4920	15500	0.4442	-
1.5401	16000	0.4768	0.8079
1.5882	16500	0.4459	-
1.6363	17000	0.4409	-
1.6845	17500	0.4434	-
1.7326	18000	0.4264	0.8041
1.7807	18500	0.4341	-
1.8289	19000	0.4143	-
1.8770	19500	0.4304	-
1.9251	20000	0.4314	0.8133
1.9732	20500	0.448	-
2.0	20778	-	0.8116
2.0214	21000	0.3985	-
2.0695	21500	0.3854	-
2.1176	22000	0.3875	0.8095
2.1658	22500	0.4139	-
2.2139	23000	0.3956	-
2.2620	23500	0.3856	-
2.3101	24000	0.3816	0.8110
2.3583	24500	0.3732	-
2.4064	25000	0.3662	-
2.4545	25500	0.3773	-
2.5026	26000	0.3703	0.8058
2.5508	26500	0.3666	-
2.5989	27000	0.369	-
2.6470	27500	0.3612	-
2.6952	28000	0.3444	0.8135
2.7433	28500	0.3667	-
2.7914	29000	0.3707	-
2.8395	29500	0.3698	-
2.8877	30000	0.3658	0.8156

Framework Versions

• Python: 3.12.7
• Sentence Transformers: 3.3.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}