Contriever Msmarco

🚀 Feature Extraction Model

This model is a fine - tuned version of the pre - trained contriever model, which can be used for feature extraction.

🚀 Quick Start

This model is the finetuned version of the pre - trained contriever model available at https://huggingface.co/facebook/contriever, following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available at https://github.com/facebookresearch/contriever.

💻 Usage Examples

Basic Usage

Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.

import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')
model = AutoModel.from_pretrained('facebook/contriever-msmarco')

sentences = [
    "Where was Marie Curie born?",
    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]

# Apply tokenizer
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
outputs = model(**inputs)

# Mean pooling
def mean_pooling(token_embeddings, mask):
    token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
    sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
    return sentence_embeddings
embeddings = mean_pooling(outputs[0], inputs['attention_mask'])