Reddit Single Context 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 be used for information retrieval, clustering, and sentence similarity tasks.

🚀 Quick Start

The model serves 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 utilized for information retrieval, clustering, or sentence similarity tasks.

✨ Features

• Trained on very large sentence level datasets using self - supervised contrastive learning.
• Fine - tuned on a 700M sentence pairs dataset.
• Developed during the Hugging Face Community week with the support of efficient hardware and deep - learning frameworks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('flax-sentence-embeddings/reddit_single-context_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 700M 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 member 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 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 700M sentences. We sampled each dataset given a weighted probability which configuration is detailed in the data_config.json file. We only use the first context response when building the dataset.