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tags: |
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- feature-extraction |
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- embeddings |
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--- |
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# LaBSE for English and Russian |
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This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google. |
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The current model has only English and Russian tokens left in the vocabulary. |
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Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings. |
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To get the sentence embeddings, you can use the following code: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE") |
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model = AutoModel.from_pretrained("sentence-transformers/LaBSE") |
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sentences = ["Hello World", "Hallo Welt"] |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt') |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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embeddings = model_output.pooler_output |
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embeddings = torch.nn.functional.normalize(embeddings) |
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print(embeddings) |
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## Reference: |
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Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020 |
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License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1) |
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