Bert Base Uncased Squad V1

🚀 BERT-base uncased model fine-tuned on SQuAD v1

This model is fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. It is case-insensitive, treating "english" and "English" the same.

🚀 Quick Start

This model is fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. It doesn't distinguish between uppercase and lowercase letters.

✨ Features

• Case-insensitive: It does not differentiate between different cases of English words.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/bert-base-uncased-squad-v1",
    tokenizer="csarron/bert-base-uncased-squad-v1"
)

predictions = qa_pipeline({
    'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
    'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.8730505704879761, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}

📚 Documentation

Details

Fine-tuning

• Python: 3.7.5
• Machine specs:
  • CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz
  • Memory: 32 GiB
  • GPUs: 2 GeForce GTX 1070, each with 8GiB memory
  • GPU driver: 418.87.01, CUDA: 10.1
• Script:

# after install https://github.com/huggingface/transformers

cd examples/question-answering
mkdir -p data

wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json

wget -O data/dev-v1.1.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

python run_squad.py \
    --model_type bert \
    --model_name_or_path bert-base-uncased \
    --do_train \
    --do_eval \
    --do_lower_case \
    --train_file train-v1.1.json \
    --predict_file dev-v1.1.json \
    --per_gpu_train_batch_size 12 \
    --per_gpu_eval_batch_size=16 \
    --learning_rate 3e-5 \
    --num_train_epochs 2.0 \
    --max_seq_length 320 \
    --doc_stride 128 \
    --data_dir data \
    --output_dir data/bert-base-uncased-squad-v1 2>&1 | tee train-energy-bert-base-squad-v1.log

It took about 2 hours to finish.

Results

Model size: 418M

Note that the above results didn't involve any hyperparameter search.

🔧 Technical Details

The model was fine - tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. The fine - tuning process used specific Python version, machine specifications, and a script. The training and evaluation were conducted on the SQuAD1.1 dataset, and the results were obtained without hyperparameter search.

📄 License

This project is licensed under the MIT license.

Created by Qingqing Cao | GitHub | Twitter

Made with ❤️ in New York.