Create README.md

model_cards/mrm8488/bert-uncased-finetuned-qnli/README.md

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+language: english
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+---
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+# [BERT](https://huggingface.co/deepset/bert-base-cased-squad2) fine tuned on [QNLI](https://github.com/rhythmcao/QNLI)+ compression ([BERT-of-Theseus](https://github.com/JetRunner/BERT-of-Theseus))
+
+I used a [Bert model fine tuned on **SQUAD v2**](https://huggingface.co/deepset/bert-base-cased-squad2) and then I fine tuned it on **QNLI** using **compression** (with a constant replacing rate) as proposed in **BERT-of-Theseus**
+
+## Details of the downstream task (QNLI):
+
+### Getting the dataset
+```bash
+wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/train.tsv
+wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/test.tsv
+wget https://raw.githubusercontent.com/rhythmcao/QNLI/master/data/QNLI/dev.tsv
+
+mkdir QNLI_dataset
+mv *.tsv QNLI_dataset
+```
+
+### Model training
+
+The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
+
+```bash
+!python /content/BERT-of-Theseus/run_glue.py \
+  --model_name_or_path deepset/bert-base-cased-squad2 \
+  --task_name qnli \
+  --do_train \
+  --do_eval \
+  --do_lower_case \
+  --data_dir /content/QNLI_dataset \
+  --max_seq_length 128 \
+  --per_gpu_train_batch_size 32 \
+  --per_gpu_eval_batch_size 32 \
+  --learning_rate 2e-5 \
+  --save_steps 2000 \
+  --num_train_epochs 50 \
+  --output_dir /content/ouput_dir \
+  --evaluate_during_training \
+  --replacing_rate 0.7 \
+  --steps_for_replacing 2500 
+```
+
+## Metrics:
+
+| Model          | Accuracy |
+|-----------------|------|
+| BERT-base       | 91.2 |
+| BERT-of-Theseus | 88.8 |
+| [bert-uncased-finetuned-qnli](https://huggingface.co/mrm8488/bert-uncased-finetuned-qnli) | 87.2
+| DistillBERT     | 85.3 |
+
+
+
+
+> [See all my models](https://huggingface.co/models?search=mrm8488)
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain