Add new CrossEncoder model
Browse files- .gitattributes +1 -0
- README.md +62 -62
- config.json +36 -34
- onnx/model.onnx +3 -0
- special_tokens_map.json +51 -1
- tokenizer.json +2 -2
- tokenizer_config.json +55 -1
.gitattributes
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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language:
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- ru
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- es
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- multilingual
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datasets:
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- unicamp-dl/mmarco
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base_model:
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- nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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tags:
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- transformers
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---
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# Cross-Encoder for multilingual MS Marco
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This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.
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As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.
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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)
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## Usage with SentenceTransformers
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The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder('model_name')
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scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
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```
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## Usage with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model = AutoModelForSequenceClassification.from_pretrained('model_name')
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tokenizer = AutoTokenizer.from_pretrained('model_name')
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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")
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model.eval()
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with torch.no_grad():
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scores = model(**features).logits
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print(scores)
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```
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---
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license: apache-2.0
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language:
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- en
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- ar
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- zh
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- nl
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- fr
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- de
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- hi
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- in
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- it
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- ja
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- pt
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+
- ru
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+
- es
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+
- vi
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- multilingual
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datasets:
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- unicamp-dl/mmarco
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base_model:
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- nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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tags:
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- transformers
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---
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# Cross-Encoder for multilingual MS Marco
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This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.
|
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+
|
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As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.
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+
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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)
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+
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## Usage with SentenceTransformers
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37 |
+
|
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+
The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
|
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder('model_name')
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scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
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```
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## Usage with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model = AutoModelForSequenceClassification.from_pretrained('model_name')
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tokenizer = AutoTokenizer.from_pretrained('model_name')
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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")
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model.eval()
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with torch.no_grad():
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scores = model(**features).logits
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print(scores)
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```
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config.json
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{
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{
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"sentence_transformers": {
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"activation_fn": "torch.nn.modules.linear.Identity",
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"version": "4.1.0.dev0"
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},
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"torch_dtype": "float32",
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"transformers_version": "4.52.0.dev0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e9a03ed1e966f7c5288dd4230e3d6a9bf5e3a170a06f1f4241c5bca12c6487c
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size 470883696
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special_tokens_map.json
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"rstrip": false,
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"single_word": false
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"pad_token": {
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"content": "<pad>",
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"sep_token": {
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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{
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"special": true
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"bos_token": "<s>",
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"eos_token": "</s>",
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"extra_special_tokens": {},
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "XLMRobertaTokenizer",
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"unk_token": "<unk>"
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}
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