readme: ---
  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 pipeli