This project aims to improve text embedding of smaller Language Models (LMs) up to 2B parameters using the contrastive fine-tuning technique. Specifically, the InfoNCE loss is utilized as a training objective.
$$\min - \log \frac{e^{\text{sim}(\textbf{h}_i, \textbf{h}_i^+) / \tau}}{\sum_i \left( e^{\text{sim}(\textbf{h}_i, \textbf{h}_j^+) / \tau }+ e^{\text{sim}(\textbf{h}_i, \textbf{h}_j^-) / \tau} \right)}$$
where $\textbf{h}_i$ denotes an embedding vector of a premise $x_i$, $\tau$ denotes a temperature and $\text{sim}(\textbf{h}_i, \textbf{h}_i^+)$ computes the cosine similarity between embedding vectors $\textbf{h}_i$ and $\textbf{h}_i^+$.
We employ LoRA as our parameter-efficient fine-tuning technique in order to reduce the memory requirement.
We have fine-tuned 3 models and we provide their LoRA adapter weights in this Hugging Face 🤗 collection.
The base models consist of
The performance and fine-tuning details can be seen in the Hugging Face model page.
We utilize the processed NLI dataset as our fine-tuning dataset. The dataset consists of 275K triplets of anchors, their corresponding entailments along with hard negatives. Please follow this README to see how to download the dataset.
If you are willing to fine-tune the LMs with your own resources, we've provided the code for you. Our code can work with multi-GPUs settings. The more GPUs you have, the larger batch size you can fine-tune.
First, you need to setup the virtual environment. We provided the environment setup file you you.
conda env create --file environment.yml
conda activate cftThen, download the processed NLI dataset following this README
After that, please follow this README for the fine-tuning steps.
This work is the final project of the Natural Language Processing Spring 2024 course at Tsinghua University 🟣. We would like to express our sincere gratitude to this course !