README.md

metadata

language: en
tags:
  - question-answering
  - pytorch
  - bert
  - squad
license: mit
datasets:
  - squad
pipeline_tag: question-answering
model-index:
  - name: bert-sliding-window_epoch_1
    results:
      - task:
          type: question-answering
          name: Question Answering
        metrics:
          - type: exact_match
            value: N/A
            name: Exact Match
          - type: f1
            value: N/A
            name: F1
        dataset:
          name: SQuAD
          type: squad
          config: plain_text
          split: validation

bert-sliding-window_epoch_1

Model description

This is a fine-tuned version of DistilBERT for question answering tasks. The model was trained on SQuAD dataset.

Training procedure

The model was trained with the following hyperparameters:

• Learning Rate: 3e-05
• Batch Size: 8
• Epochs: 3
• Weight Decay: 0.01

Intended uses & limitations

This model is intended to be used for question answering tasks, particularly on SQuAD-like datasets. It performs best on factual questions where the answer can be found as a span of text within the given context.

Training Details

Training Data

The model was trained on the SQuAD dataset, which consists of questions posed by crowdworkers on a set of Wikipedia articles.

Training Hyperparameters

The model was trained with the following hyperparameters:

• learning_rate: 3e-05
• batch_size: 8
• num_epochs: 3
• weight_decay: 0.01

Uses

This model can be used for:

• Extracting answers from text passages given questions
• Question answering tasks
• Reading comprehension tasks

Limitations

• The model can only extract answers that are directly present in the given context
• Performance may vary on out-of-domain texts
• The model may struggle with complex reasoning questions

Additional Information

• Model type: DistilBERT
• Language: English
• License: MIT
• Framework: PyTorch