Model Card (#7752)

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Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/sentence-transformers/LaBSE/README.md

+# LaBSE Pytorch Version 
+This is a pytorch port of the tensorflow version of [LaBSE](https://tfhub.dev/google/LaBSE/1).
+
+To get the sentence embeddings, you can  use the following code:
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
+model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
+
+sentences = ["Hello World", "Hallo Welt"]
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')
+
+with torch.no_grad():
+    model_output = model(**encoded_input, return_dict=True)
+
+embeddings = model_output.pooler_output
+embeddings = torch.nn.functional.normalize(embeddings)
+print(embeddings)
+```
+
+
+When you have [sentence-transformers](https://www.sbert.net/) installed, you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["Hello World", "Hallo Welt"]
+
+model = SentenceTransformer('LaBSE')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Reference:
+Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020
+
+License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1)