All Datasets V3 MiniLM L6

🚀 Flax Sentence Embeddings Model

This project focuses on training sentence embedding models on large sentence-level datasets using self-supervised contrastive learning. The model can output semantic vectors for sentences, which are useful for various NLP tasks.

🚀 Quick Start

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-L6-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, 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 member about efficient deep learning frameworks.

✨ Features

• Sentence Encoding: 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.
• Versatile Applications: The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

💻 Usage Examples

Basic Usage

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

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L6')
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)

🔧 Technical Details

Pre-training

We use the pretrained 'MiniLM-L6-H384-uncased' which is a 6 layer version of 'microsoft/MiniLM-L12-H384-uncased' by keeping only every second layer. 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.

Hyper parameters

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.