README.md

metadata

language:
  - en
license: mit
tags:
  - bart
  - question-answering
  - squad
  - squad_v2
datasets:
  - squad_v2
  - squad
base_model: facebook/bart-base
model-index:
  - name: sjrhuschlee/bart-base-squad2
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad_v2
          type: squad_v2
          config: squad_v2
          split: validation
        metrics:
          - type: exact_match
            value: 75.223
            name: Exact Match
          - type: f1
            value: 78.443
            name: F1
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad
          type: squad
          config: plain_text
          split: validation
        metrics:
          - type: exact_match
            value: 83.406
            name: Exact Match
          - type: f1
            value: 90.377
            name: F1

bart-base for Extractive QA

This model is a fine-tuned version of facebook/bart-base on the SQuAD2.0 dataset.

Overview

Language model: bart-base
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x NVIDIA 3070

Model Usage

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/bart-base-squad2"
# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Metrics

# Squad v2
{
    "eval_HasAns_exact": 76.45074224021593,
    "eval_HasAns_f1": 82.88605283171232,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 74.01177460050462,
    "eval_NoAns_f1": 74.01177460050462,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 75.23793481007327,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 78.45098300230696,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 75.22951233892024,
    "eval_f1": 78.44256053115387,
    "eval_runtime": 131.875,
    "eval_samples": 11955,
    "eval_samples_per_second": 90.654,
    "eval_steps_per_second": 3.784,
    "eval_total": 11873
}

# Squad
{
    "eval_exact_match": 83.40586565752129,
    "eval_f1": 90.37706849113668,
    "eval_runtime": 117.2093,
    "eval_samples": 10619,
    "eval_samples_per_second": 90.599,
    "eval_steps_per_second": 3.78
}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• max_seq_length 512
• doc_stride 128
• learning_rate: 2e-06
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 6
• total_train_batch_size: 96
• optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4.0
• gradient_checkpointing: True
• tf32: True

Framework versions

• Transformers 4.30.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3