File size: 3,453 Bytes
924bf7d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
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-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",
}
```
|