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README.md

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+---
+language: en
+license: mit
+pipeline_tag: text-classification
+tags:
+- sentence-transformers
+---
+
+# Cross-Encoder for MS Marco
+
+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/training/ms_marco)
+
+## Training Data
+This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
+
+## Usage
+The usage becomes easier 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('model_name', max_length=512)
+scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')])
+```
+
+## 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-L-2-v2 | 69.84 | 32.56 | 9000
+| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
+| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
+| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
+| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
+| **Version 1 models** | | | 
+| cross-encoder/ms-marco-TinyBERT-L-2  | 67.43 | 30.15  | 9000
+| cross-encoder/ms-marco-TinyBERT-L-4  | 68.09 | 34.50  | 2900
+| cross-encoder/ms-marco-TinyBERT-L-6 |  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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+{
+  "_name_or_path": "cross-encoder/ms-marco-MiniLM-L-12-v2",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
+  "transformers_version": "4.4.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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special_tokens_map.json

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+    "mask_token": "[MASK]",
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tokenizer_config.json

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+    "cls_token": "[CLS]",
+    "do_basic_tokenize": true,
+    "do_lower_case": true,
+    "mask_token": "[MASK]",
+    "model_max_length": 512,
+    "name_or_path": "microsoft/MiniLM-L12-H384-uncased",
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+    "tokenize_chinese_chars": true,
+    "unk_token": "[UNK]"
+}