|
--- |
|
pipeline_tag: sentence-similarity |
|
license: apache-2.0 |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- transformers |
|
--- |
|
|
|
# sentence-transformers/paraphrase-MiniLM-L12-v2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
|
|
|
|
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["This is an example sentence", "Each sentence is converted"] |
|
|
|
model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L12-v2') |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
|
|
|
|
## Usage (HuggingFace Transformers) |
|
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import torch |
|
|
|
|
|
#Mean Pooling - Take attention mask into account for correct averaging |
|
def mean_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() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
|
|
# Sentences we want sentence embeddings for |
|
sentences = ['This is an example sentence', 'Each sentence is converted'] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L12-v2') |
|
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L12-v2') |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
# Perform pooling. In this case, max pooling. |
|
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
|
|
|
print("Sentence embeddings:") |
|
print(sentence_embeddings) |
|
``` |
|
|
|
|
|
|
|
## Evaluation Results |
|
|
|
|
|
|
|
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L12-v2) |
|
|
|
|
|
|
|
## Full Model Architecture |
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
|
) |
|
``` |
|
|
|
## Citing & Authors |
|
|
|
This model was trained by [sentence-transformers](https://www.sbert.net/). |
|
|
|
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): |
|
```bibtex |
|
@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", |
|
} |
|
``` |
