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--- |
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pipeline_tag: feature-extraction |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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--- |
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# Sentence Embeddings with `bert-zwnj-wnli-mean-tokens` |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) 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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sentences = [ |
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'اولین حکمران شهر بابل کی بود؟', |
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'در فصل زمستان چه اتفاقی افتاد؟', |
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'میراث کوروش' |
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] |
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model = SentenceTransformer('m3hrdadfi/bert-zwnj-wnli-mean-tokens') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word 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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# Max Pooling - Take the max value over time for every dimension. |
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def max_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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token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value |
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return torch.mean(token_embeddings, 1)[0] |
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# Sentences we want sentence embeddings for |
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sentences = [ |
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'اولین حکمران شهر بابل کی بود؟', |
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'در فصل زمستان چه اتفاقی افتاد؟', |
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'میراث کوروش' |
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] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/bert-zwnj-wnli-mean-tokens') |
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model = AutoModel.from_pretrained('m3hrdadfi/bert-zwnj-wnli-mean-tokens') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, 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, max pooling. |
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sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Questions? |
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Post a Github issue from [HERE](https://github.com/m3hrdadfi/sentence-transformers). |
