All Datasets V4 MiniLM L12

🚀 Flax Sentence Embedding Model

This project trains sentence embedding models on large sentence-level datasets using self-supervised contrastive learning, aiming to provide high-quality sentence vectors for various NLP tasks.

🚀 Quick Start

Our model is designed to be used as a sentence encoder. Given an input sentence, it outputs a vector that captures the sentence's semantic information. This vector can be used for information retrieval, clustering, or sentence similarity tasks.

Here's how to use this model to get the features of a given text using the SentenceTransformers library:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L12')
text = "Replace me by any text you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514,  0.04046123,  0.1317083 ,  0.00085931,  0.04585106,
#        -0.05607086,  0.0138078 ,  0.03569756,  0.01420381,  0.04266302 ...],
#        dtype=float32)

✨ Features

• Large-scale Training: Trained on a dataset of over 1 billion sentence pairs, enabling the model to capture rich semantic information.
• Contrastive Learning: Uses a contrastive learning objective to enhance the model's ability to distinguish similar and dissimilar sentences.
• Efficient Infrastructure: Benefited from efficient hardware infrastructure (7 TPUs v3 - 8) and guidance from Google's Flax, JAX, and Cloud team members.

📦 Installation

The installation of the model mainly involves installing the SentenceTransformers library. You can use the following command to install it:

pip install sentence-transformers

📚 Documentation

Model description

The project aims to train sentence embedding models on very large sentence - level datasets using a self - supervised contrastive learning objective. We used the pretrained ['MiniLM - L12'](https://huggingface.co/microsoft/MiniLM - L12 - H384 - uncased) model and fine - tuned it on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences was actually paired with it in our dataset.

We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open - to - the - community - community - week - using - jax - flax - for - nlp - cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train - the - best - sentence - embedding - model - ever - with - 1b - training - pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3 - 8, as well as intervention from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.

Intended uses

Our model is intended to be used as a sentence encoder. Given an input sentence, it outputs a vector which captures the sentence's semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks.

🔧 Technical Details

Training procedure

Pre - training

We use the pretrained ['MiniLM - L12'](https://huggingface.co/microsoft/MiniLM - L12 - H384 - uncased). Please refer to the model card for more detailed information about the pre - training procedure.

Fine - tuning

We fine - tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pair from the batch. We then apply the cross - entropy loss by comparing with true pairs.

Hyper parameters

We trained our model on a TPU v3 - 8. We trained the model for 540k steps using a batch size of 1024 (128 per TPU core). We used a learning rate warm - up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e - 5 learning rate. The full training script is accessible in this current repository.

Training data

We use the concatenation of multiple datasets to fine - tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability, and the configuration is detailed in the data_config.json file.