
# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0

BERT model with [10 Transformer layers and hidden embedding of size 512](https://huggingface.co/google/bert_uncased_L-10_H-512_A-8), referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), [fine-tuned for MLM](https://huggingface.co/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616) on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0.

## Training the model

```bash
python run_squad.py
    --model_type bert
    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
    --train_file 'train-v2.0.json'
    --predict_file 'dev-v2.0.json'
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --max_seq_length 384
    --per_gpu_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2

{"datasets": ["squad_v2"]}
transformers pytorch jax bert question-answering dataset:squad_v2 arxiv:1908.08962 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #question-answering #dataset-squad_v2 #arxiv-1908.08962 #endpoints_compatible #region-us 


# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0

BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0.

## Training the model

'''bash
python run_squad.py
    --model_type bert
    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
    --train_file 'train-v2.0.json'
    --predict_file 'dev-v2.0.json'
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --max_seq_length 384
    --per_gpu_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2

# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0

BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0. ## Training the model

'''bash
python run_squad.py
    --model_type bert
    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
    --train_file 'train-v2.0.json'
    --predict_file 'dev-v2.0.json'
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --max_seq_length 384
    --per_gpu_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2
TAGS
#transformers #pytorch #jax #bert #question-answering #dataset-squad_v2 #arxiv-1908.08962 #endpoints_compatible #region-us 
 # BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0

BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0. ## Training the model

'''bash
python run_squad.py
    --model_type bert
    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
    --train_file 'train-v2.0.json'
    --predict_file 'dev-v2.0.json'
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --max_seq_length 384
    --per_gpu_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2
passage: TAGS
#transformers #pytorch #jax #bert #question-answering #dataset-squad_v2 #arxiv-1908.08962 #endpoints_compatible #region-us 
# BERT L-10 H-512 CORD-19 (2020/06/16) fine-tuned on SQuAD v2.0

BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16) and fine-tuned for QA on SQuAD v2.0.## Training the model

'''bash
python run_squad.py
    --model_type bert
    --model_name_or_path aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
    --train_file 'train-v2.0.json'
    --predict_file 'dev-v2.0.json'
    --do_train
    --do_eval
    --do_lower_case
    --version_2_with_negative
    --max_seq_length 384
    --per_gpu_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616_squad2