All Datasets V4 Mpnet Base

🚀 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

This 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. The sentence vector can be used for information retrieval, clustering, or sentence similarity tasks.

✨ Features

• Self - supervised Contrastive Learning: Trained on large - scale sentence - level datasets using self - supervised contrastive learning, enabling the model to capture rich semantic information.
• Fine - tuned on 1B Sentence Pairs: Fine - tuned on a dataset of 1 billion sentence pairs, enhancing the model's generalization ability.
• Efficient Hardware Utilization: Trained using 7 TPUs v3 - 8, with support from Google's Flax, JAX, and Cloud teams.

💻 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_v4_mpnet-base')
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 ['mpnet - base'](https://huggingface.co/microsoft/mpnet - base) 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 semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks.

Training procedure

Pre - training

We use the pretrained ['mpnet - base'](https://huggingface.co/microsoft/mpnet - base). 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 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 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, the configuration of which is detailed in the data_config.json file.