pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
bowdpr_wiki_triviaft
This is a fine-tuned retriever on the TriviaQA Task (without distillation). We introduce a novel pre-training paradigm, Bag-of-Word Prediction, for dense retrieval. This retriever is initialized from a base-sized pre-trained model, bowdpr/bowdpr_wiki. Please refer to our paper for detailed pre-training and fine-tuning settings.
Finetuning on QA datasets involves a two-stage pipeline
- s1: BM25 negs
- s2: BM25 negs + Mined negatives from s1
Usage (Sentence-Transformers)
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('bowdpr/bowdpr_wiki_triviaft')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, 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.
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('bowdpr/bowdpr_wiki_triviaft')
model = AutoModel.from_pretrained('bowdpr/bowdpr_wiki_triviaft')
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Full Model Architecture
SentenceTransformerforCL(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
Citing & Authors
If you are interested in our work, please consider citing our paper.
@misc{ma2024bow_pred,
title={Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval},
author={Guangyuan Ma and Xing Wu and Zijia Lin and Songlin Hu},
year={2024},
eprint={2401.11248},
archivePrefix={arXiv},
primaryClass={cs.IR}
}