README.md · csarron/bert-base-uncased-squad-v1 at d313fdc216c8c49479a3b2b916132bcf62dc5e17

metadata

language: en
thumbnail: null
license: mit
tags:
  - question-answering
  - bert
  - bert-base
datasets:
  - squad
metrics:
  - squad
widget:
  - text: Which name is also used to describe the Amazon rainforest in English?
    context: >-
      The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia;
      Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt
      amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia
      or the Amazon Jungle, is a moist broadleaf forest that covers most of the
      Amazon basin of South America. This basin encompasses 7,000,000 square
      kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres
      (2,100,000 sq mi) are covered by the rainforest. This region includes
      territory belonging to nine nations. The majority of the forest is
      contained within Brazil, with 60% of the rainforest, followed by Peru with
      13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador,
      Bolivia, Guyana, Suriname and French Guiana. States or departments in four
      nations contain "Amazonas" in their names. The Amazon represents over half
      of the planet's remaining rainforests, and comprises the largest and most
      biodiverse tract of tropical rainforest in the world, with an estimated
      390 billion individual trees divided into 16,000 species.
  - text: How many square kilometers of rainforest is covered in the basin?
    context: >-
      The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia;
      Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt
      amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia
      or the Amazon Jungle, is a moist broadleaf forest that covers most of the
      Amazon basin of South America. This basin encompasses 7,000,000 square
      kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres
      (2,100,000 sq mi) are covered by the rainforest. This region includes
      territory belonging to nine nations. The majority of the forest is
      contained within Brazil, with 60% of the rainforest, followed by Peru with
      13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador,
      Bolivia, Guyana, Suriname and French Guiana. States or departments in four
      nations contain "Amazonas" in their names. The Amazon represents over half
      of the planet's remaining rainforests, and comprises the largest and most
      biodiverse tract of tropical rainforest in the world, with an estimated
      390 billion individual trees divided into 16,000 species.

BERT-base uncased model fine-tuned on SQuAD v1

This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. This model is case-insensitive: it does not make a difference between english and English.

Details

Dataset	Split	# samples
SQuAD1.1	train	90.6K
SQuAD1.1	eval	11.1k

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

Metric	# Value	# Original (Table 2)
EM	80.9	80.8
F1	88.2	88.5

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

Example 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'}

Created by Qingqing Cao | GitHub | Twitter

Made with ❤️ in New York.