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sentence-transformers/bert-base-nli-max-tokens sentence-transformers/bert-base-nli-max-tokens
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Sentence Transformers university
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How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/bert-base-nli-max-tokens") model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-max-tokens")

BERT base model (uncased) for Sentence Embeddings

This is the bert-base-nli-max-tokens model from the 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

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:

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 installed:

pip install -U sentence-transformers

Then you can use the model like this:

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:

@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",
}