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Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2/README.md

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+---
+datasets:
+- squad_v2
+---
+
+# 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