---
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  # You can update this with actual metrics if available
        name: Exact Match
      - type: f1
        value: N/A  # You can update this with actual metrics if available
        name: F1
    dataset:
      name: SQuAD
      type: squad
      config: plain_text  # Adding the config field
      split: validation  # Adding the split field
---

# bert-sliding-window_epoch_1

## Model description

This is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-cased-distilled-squad) 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