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---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- snli
- multi_nli
---
# BERT base model (uncased) for Sentence Embeddings
This is the `bert-base-nli-cls-token` 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. The CLS token of each input represents the sentence embedding:
```python
from transformers import AutoTokenizer, AutoModel
import torch
#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-cls-token")
model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-cls-token")
#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)
sentence_embeddings = model_output[0][:,0] #Take the first token ([CLS]) from each sentence
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-cls-token')
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",
}
```
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