README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train_instances.json
      - split: dev
        path: data/dev_instances.json
      - split: test
        path: data/test_instances.json
  - config_name: has_html
    data_files:
      - split: train
        path: data/train_instances_with_html.json
      - split: dev
        path: data/dev_instances_with_html.json
      - split: test
        path: data/test_instances_with_html.json

Preprocessed QASPER dataset

Working doc: https://docs.google.com/document/d/1gYPhPNJ5LGttgjix1dwai8pdNcqS6PbqhsM7W0rhKNQ/edit?usp=sharing

Original:

• Dataset: https://github.com/allenai/qasper-led-baseline
• Baseline repo: https://github.com/allenai/qasper-led-baseline
• HF: https://huggingface.co/datasets/allenai/qasper

Differences of our implementation over the original implementation:

1. We use the dataset provided at https://huggingface.co/datasets/allenai/qasper since it doesn't require manually downloading files.
2. We remove usage of allennlp since the Python package cannot be installed anymore.
3. We add baselines to qasper/models. Currently, we have
   • QASPER (Longformer Encoder Decoder)
   • GPT-3.5-Turbo
   • TODO: RAG (with R=TF-IDF or Contriever) implemented in LangChain?
4. We replace allennlp special tokens with the special tokens of the HF transformer tokenizer:
   • paragraph separator: '' -> tokenizer.sep_token
   • sequence pair start tokens: _tokenizer.sequence_pair_start_tokens -> tokenizer.bos_token

Usage

from datasets import load_dataset

dataset = load_dataset("ag2435/qasper")