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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
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9
Safetensors
Model size
139M params
Tensor type
F32

Datasets used to train sjrhuschlee/bart-base-squad2

Evaluation results