All Datasets V3 MiniLM L12

🚀 Flax Sentence Embedding Model

This project focuses on training sentence embedding models on large-scale sentence-level datasets using self-supervised contrastive learning. The model can generate semantic vectors for sentences, useful in information retrieval, clustering, and similarity 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 semantic information of the sentence. This sentence vector can be used for information retrieval, clustering, or sentence similarity tasks.

✨ Features

• Self-supervised Contrastive Learning: Trained on a large sentence pairs dataset using a self-supervised contrastive learning objective.
• Fine-tuned MiniLM-L12: Based on the pretrained MiniLM-L12 model and fine-tuned on over 1 billion sentence pairs.
• Versatile Applications: Suitable for various NLP tasks such as information retrieval, clustering, and sentence similarity.

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_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)

📚 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 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, organized by Hugging Face. We developed this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. 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 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. 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 pairs from the batch. We then apply the cross - entropy loss by comparing with true pairs.

Hyperparameters

We trained our model on a TPU v3 - 8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use 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 from 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 which configuration is detailed in the data_config.json file.