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@@ -33,3 +33,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+model_b1_s256.rknn filter=lfs diff=lfs merge=lfs -text
+model_b4_s256.rknn filter=lfs diff=lfs merge=lfs -text
+model_b4_s512.rknn filter=lfs diff=lfs merge=lfs -text
+rknn/model_o1.rknn filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,161 @@
+---
+license: apache-2.0
+datasets:
+- sentence-transformers/natural-questions
+language:
+- en
+base_model: cross-encoder/ms-marco-MiniLM-L12-v2
+pipeline_tag: text-ranking
+library_name: rk-transformers
+tags:
+- transformers
+- rknn
+- rockchip
+- npu
+- rk-transformers
+- rk3588
+model_name: ms-marco-MiniLM-L12-v2
+---
+# ms-marco-MiniLM-L12-v2 (RKNN2)
+
+> This is an RKNN-compatible version of the [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) model. It has been optimized for Rockchip NPUs using the [rk-transformers](https://github.com/emapco/rk-transformers) library.
+
+<details><summary>Click to see the RKNN model details and usage examples</summary>
+
+## Model Details
+
+- **Original Model:** [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2)
+- **Target Platform:** rk3588
+- **rknn-toolkit2 Version:** 2.3.2
+- **rk-transformers Version:** 0.1.0
+
+### Available Model Files
+
+| Model File | Optimization Level | Quantization | File Size |
+| :--------- | :----------------- | :----------- | :-------- |
+| [model.rknn](./model.rknn) | 0 | float16 | 68.8 MB |
+| [model_b1_s256.rknn](./model_b1_s256.rknn) | 0 | float16 | 67.0 MB |
+| [model_b4_s256.rknn](./model_b4_s256.rknn) | 0 | float16 | 75.1 MB |
+| [model_b4_s512.rknn](./model_b4_s512.rknn) | 0 | float16 | 81.7 MB |
+| [rknn/model_o1.rknn](./rknn/model_o1.rknn) | 1 | float16 | 68.8 MB |
+| [rknn/model_o2.rknn](./rknn/model_o2.rknn) | 2 | float16 | 68.8 MB |
+| [rknn/model_o3.rknn](./rknn/model_o3.rknn) | 3 | float16 | 68.8 MB |
+| [rknn/model_w8a8.rknn](./rknn/model_w8a8.rknn) | 0 | w8a8 | 36.5 MB |
+
+## Usage
+
+### Installation
+
+Install `rk-transformers` to use this model:
+
+```bash
+pip install rk-transformers
+```
+
+#### RKTransformers API
+
+```python
+from rktransformers import RKRTModelForSequenceClassification
+from transformers import AutoTokenizer
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("rk-transformers/ms-marco-MiniLM-L12-v2")
+model = RKRTModelForSequenceClassification.from_pretrained(
+    "rk-transformers/ms-marco-MiniLM-L12-v2",
+    platform="rk3588",
+    core_mask="auto",
+)
+
+# Tokenize and run inference
+inputs = tokenizer(
+    ["Sample text for encoding"],
+    padding="max_length",
+    max_length=512,
+    truncation=True,
+    return_tensors="np"
+)
+
+outputs = model(**inputs)
+print(outputs.shape)
+
+# Load specific optimized/quantized model file
+model = RKRTModelForSequenceClassification.from_pretrained(
+    "rk-transformers/ms-marco-MiniLM-L12-v2",
+    platform="rk3588",
+    file_name="rknn/model_w8a8.rknn"
+)
+```
+
+## Configuration
+
+The full configuration for all exported RKNN models is available in the [rknn.json](./rknn.json) file.
+
+</details>
+# Cross-Encoder for MS Marco
+
+This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
+
+The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/cross_encoder/training/ms_marco)
+
+
+## Usage with SentenceTransformers
+
+The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then you can use the pre-trained models like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L12-v2')
+scores = model.predict([
+    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
+    ("How many people live in Berlin?", "Berlin is well known for its museums."),
+])
+print(scores)
+# [ 9.218911  -4.0780287]
+```
+
+
+## Usage with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L12-v2')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L12-v2')
+
+features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    print(scores)
+```
+
+
+
+## Performance
+In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. 
+
+
+| Model-Name        | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev)  | Docs / Sec |
+| ------------- |:-------------| -----| --- | 
+| **Version 2 models** | | | 
+| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000
+| cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100
+| cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500
+| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800
+| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960
+| **Version 1 models** | | | 
+| cross-encoder/ms-marco-TinyBERT-L2  | 67.43 | 30.15  | 9000
+| cross-encoder/ms-marco-TinyBERT-L4  | 68.09 | 34.50  | 2900
+| cross-encoder/ms-marco-TinyBERT-L6 |  69.57 | 36.13  | 680
+| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
+| **Other models** | | | 
+| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 
+| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 
+| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 
+| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 
+| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 
+| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
+ 
+ Note: Runtime was computed on a V100 GPU.

config.json

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+    "BertForSequenceClassification"
+  ],
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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rknn/model_w8a8.rknn

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5f39839582190c0b2e6f0c260994f946c8ea973ccc456635111e721f1e6e6843
+size 38286411

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}