README.md

---
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 Amaz\xF4nica or Amaz\xF4nia;\
    \ Spanish: Selva Amaz\xF3nica, Amazon\xEDa or usually Amazonia; French: For\xEA\
    t 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 Amaz\xF4nica or Amaz\xF4nia;\
    \ Spanish: Selva Amaz\xF3nica, Amazon\xEDa or usually Amazonia; French: For\xEA\
    t 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:
    - name: Exact Match
      type: exact_match
      value: 80.9104
      verified: true
    - name: F1
      type: f1
      value: 88.2302
      verified: true
---

## 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.