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Browse files- README.md +92 -0
- config.json +22 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
README.md
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# Sentence Embeddings Models trained on Duplicate Questions
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This model is from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers)-repository. It was trained on the [Quora Duplicate Questions dataset](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs). Further details on SBERT can be found in the paper: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
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For more details, see: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)
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This model is the multilingual version of quora-distilbert-base, trained on parallel data for 50+ languages.
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## Usage (HuggingFace Models Repository)
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You can use the model directly from the model repository to compute sentence embeddings:
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
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sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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return sum_embeddings / sum_mask
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#Sentences we want sentence embeddings for
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sentences = ['This framework generates embeddings for each input sentence',
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'Sentences are passed as a list of string.',
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'The quick brown fox jumps over the lazy dog.']
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#Load AutoModel from huggingface model repository
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tokenizer = AutoTokenizer.from_pretrained("model_name")
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model = AutoModel.from_pretrained("model_name")
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#Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
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#Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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#Perform pooling. In this case, mean pooling
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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```
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## Usage (Sentence-Transformers)
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Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('model_name')
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sentences = ['This framework generates embeddings for each input sentence',
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'Sentences are passed as a list of string.',
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'The quick brown fox jumps over the lazy dog.']
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sentence_embeddings = model.encode(sentences)
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Citing & Authors
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If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
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```
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "http://arxiv.org/abs/1908.10084",
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}
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```
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and for the multilingual models:
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If you find this model helpful, feel free to cite our publication [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813):
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```
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@inproceedings{reimers-2020-multilingual-sentence-bert,
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title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2020",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/2004.09813",
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}
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```
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config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertModel"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"output_past": true,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"vocab_size": 119547
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:66863c27df8fe2c66905a1c19be5848e4963f8b8d9193577b21e45b9449e453b
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size 538975987
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sentence_bert_config.json
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{
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"max_seq_length": 128
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}
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer_config.json
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{"do_lower_case": false, "model_max_length": 512}
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vocab.txt
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