transformers pytorch jax safetensors bert question-answering bert-base en dataset:squad license:mit model-index endpoints_compatible region:us
---
datasets:
- squad
language: en
license: mit
metrics:
- squad
tags:
- question-answering
- bert
- bert-base
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.'
model-index:
- name: csarron/bert-base-uncased-squad-v1
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 80.9104
      name: Exact Match
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDJlOWQ0OTE0ZjRhMTQwNDY5MjVhYmZiN2RmYzY0OWJiOWUyNjcyMWU5N2I3YmU0OThjZTVjNTc2MjM2Yzg5NiIsInZlcnNpb24iOjF9.cuJ34B-ngUur5wKGhfhVP8FM6NX4IFrIJEdXypbLQJw1i8M5Bb2EeIs-0M5n35YIx2PfqSQcnVj_jP8vLUk4Dg
    - type: f1
      value: 88.2302
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE4NzFmNDA3MDRiODk3ZDg5NWYyNjczOGE5YjdkZWQ0ZmEzNWU5YjFjMzc1ODA2OGRjYzU0Y2M5MmU0NGNhYSIsInZlcnNpb24iOjF9.phmkVWF3I-rl2xrHW0EW9OQqzfuefoqNjWplOpFdzJuW8d2C4sJ8snW0Ikw9kQqZaBCdwdkmsf5VTgOupHb8Dw
---

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

This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
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:

  ```shell
  # 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](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **80.9** | **80.8** |
| **F1** | **88.2** | **88.5** |

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

## Example Usage


```python
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](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp) 

> Made with ❤️ in New York.
