Create README.md - Model card (#5658)

Model card for sentence-transformers/bert-base-nli-max-tokens

model_cards/sentence-transformers/bert-base-nli-max-tokens/README.md

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+---
+language: english
+tags:
+- exbert
+license: apache-2.0
+datasets:
+- SNLI
+- MultiNLI
+---
+
+# BERT base model (uncased) for Sentence Embeddings
+This is the `bert-base-nli-max-tokens` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers)-repository. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings. 
+The model is described in  the paper  [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
+
+## Usage (HuggingFace Models Repository)
+
+You can use the model directly from the model repository to compute sentence embeddings. It uses max pooling to generate a fixed sized sentence embedding:
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Max Pooling - Take the max value over time for every dimension
+def max_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
+    max_over_time = torch.max(token_embeddings, 1)[0]
+    return max_over_time
+
+
+#Sentences we want sentence embeddings for
+sentences = ['This framework generates embeddings for each input sentence',
+             'Sentences are passed as a list of string.',
+             'The quick brown fox jumps over the lazy dog.']
+
+#Load AutoModel from huggingface model repository
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/bert-base-nli-max-tokens")
+model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-max-tokens")
+
+#Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+
+#Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+#Perform pooling. In this case, max pooling
+sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask'])
+
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Usage (Sentence-Transformers)
+Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('bert-base-nli-max-tokens')
+sentences = ['This framework generates embeddings for each input sentence',
+    'Sentences are passed as a list of string.', 
+    'The quick brown fox jumps over the lazy dog.']
+sentence_embeddings = model.encode(sentences)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Citing & Authors
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+``` 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```