Bert Base 1024 Biencoder 6M Pairs

🚀 bert-base-1024-biencoder-6M-pairs

A long context biencoder based on MosaicML's BERT pretrained on 1024 sequence length. This model maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

🚀 Quick Start

Datasets

• sentence-transformers/embedding-training-data
• flax-sentence-embeddings/stackexchange_xml
• snli
• eli5
• search_qa
• multi_nli
• wikihow
• natural_questions
• trivia_qa
• ms_marco
• gooaq
• yahoo_answers_topics

Language

• en

Inference

• false

Pipeline Tag

• sentence-similarity

Task Categories

• sentence-similarity
• feature-extraction
• text-retrieval

Tags

• information retrieval
• ir
• documents retrieval
• passage retrieval
• beir
• benchmark
• sts
• semantic search
• sentence-transformers
• feature-extraction
• sentence-similarity
• transformers

📦 Installation

Download the model and related scripts

git clone https://huggingface.co/shreyansh26/bert-base-1024-biencoder-6M-pairs

💻 Usage Examples

Basic Usage

import torch
from torch import nn
from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline, AutoModel
from mosaic_bert import BertModel

# pip install triton==2.0.0.dev20221202 --no-deps if using Pytorch 2.0

class AutoModelForSentenceEmbedding(nn.Module):
    def __init__(self, model, tokenizer, normalize=True):
        super(AutoModelForSentenceEmbedding, self).__init__()

        self.model = model.to("cuda")
        self.normalize = normalize
        self.tokenizer = tokenizer

    def forward(self, **kwargs):
        model_output = self.model(**kwargs)
        embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
        if self.normalize:
            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)

        return embeddings

    def mean_pooling(self, 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)

model = AutoModel.from_pretrained("<path-to-model>", trust_remote_code=True).to("cuda")
model = AutoModelForSentenceEmbedding(model, tokenizer)
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

sentences = ["This is an example sentence", "Each sentence is converted"]

encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=1024, return_tensors='pt').to("cuda")
embeddings = model(**encoded_input)

print(embeddings)
print(embeddings.shape)

📚 Documentation

Training

This model has been trained on 6.4M randomly sampled pairs of sentences/paragraphs from the same training set that Sentence Transformers models use. Details of the training set here.

The training (along with hyperparameters), inference and data loading scripts can all be found in this Github repository.

Evaluations

We ran the model on a few retrieval based benchmarks (CQADupstackEnglishRetrieval, DBPedia, MSMARCO, QuoraRetrieval) and the results are here.