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--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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--- |
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# Sentence Embeddings Models trained on Paraphrases |
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This model is from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers)-repository. It was trained on millions of paraphrase sentences. 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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This model is the multilingual version of distilroberta-base-paraphrase-v1, 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 [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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``` |
