Extending the MINERS Framework

This fork was developed for Ontario Tech University's CSCI 6720 Group Project, focusing on extending MINERS Framework's ICL classification and Deep Passage Retrieval tasks.

Group Members:

• Alexie Linardatos([email protected])
• Madhav Goyani([email protected])
• Zikun Fu([email protected])

A 3 minute video summary on this project can be found here: Link

The final report can be found here: Link

🔧 Environment Setup

pip install -r requirements.txt

Microsoft Visual C++ 14.0 or greater is required to run the project. You can download it from Microsoft's website.

📝 Experiment Logs

Full Experiment logs can be accessed here.

🚀 Running Experiments

ICL Classification

❱❱❱ python icl_NER.py --dataset {dataset} --seed 42 --model_checkpoint {model} --gen_model_checkpoint {gen_model_checkpoint}  --cuda --load_in_8bit --k {k}
❱❱❱ python icl_NER.py --dataset masakhaner --seed 42 --model_checkpoint sentence-transformers/LaBSE --gen_model_checkpoint meta-llama/Meta-Llama-3.1-8B-Instruct --cuda --load_in_8bit --k 2

Dense Passage Retrieval

Download the corpus:

!wget https://huggingface.co/datasets/miracl/miracl-corpus/resolve/main/miracl-corpus-v1.0-bn/docs-0.jsonl.gz

Chunk and Encode the downloaded corpus:

python chunk_dataset.py
bash encode_corpus.sh

Run the evaluation:

bash search_eval.sh

💻 Models Support

All models used for the experiments are listed below:

Encoder LMs and APIs

Open-source LMs:

• sentence-transformers/LaBSE
• intfloat/multilingual-e5-base
• FacebookAI/xlm-roberta-base

Generative LMs:

• Gemma 2 Instruct google/gemma-2-9b-it
• Llama 3 8B Instruct meta-llama/Meta-Llama-3.1-8B-Instruct

📜 Credits

• OpenSub bitext mining dataset
• Masakhaner NER dataset
• MIRACL dataset
• Framework code based on the MINERS paper:

  @article{winata2024miners,
  title={MINERS: Multilingual Language Models as Semantic Retrievers},
  author={Winata, Genta Indra and Zhang, Ruochen and Adelani, David Ifeoluwa},
  journal={arXiv preprint arXiv:2406.07424},
  year={2024}
  }