Migrate model card from transformers-repo
Browse filesRead announcement at /static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fannouncement-all-model-cards-will-be-migrated-to-hf-co-model-repos%2F2755%3Cbr%2F%3EOriginal file history: https://github.com/huggingface/transformers/commits/master/model_cards/sentence-transformers/LaBSE/README.md
README.md
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# LaBSE Pytorch Version
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This is a pytorch port of the tensorflow version of [LaBSE](https://tfhub.dev/google/LaBSE/1).
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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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```
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When you have [sentence-transformers](https://www.sbert.net/) installed, 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 = ["Hello World", "Hallo Welt"]
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model = SentenceTransformer('LaBSE')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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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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