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@ -766,27 +766,36 @@ Here is an example on evaluating a model using adversarial evaluation of natural
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The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
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```bash
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export HANS_DIR=/path/to/HANS
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python ./hans/test_hans.py \
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--model_type bert \
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--model_name_or_path bert-base-multilingual-cased \
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--language de \
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--train_language en \
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--do_train \
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--do_eval \
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--data_dir $XNLI_DIR \
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--per_gpu_train_batch_size 32 \
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--learning_rate 5e-5 \
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--num_train_epochs 2.0 \
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--max_seq_length 128 \
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--output_dir /tmp/debug_xnli/ \
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--save_steps -1
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```
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Evaluating with the previously defined hyper-parameters yields the following results:
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This is an example of using test_hans.py:
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```bash
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acc = 0.7093812375249501
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export HANS_DIR=path-to-hans
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export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
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export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
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python examples/test_hans.py \
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--task_name hans \
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--model_type $MODEL_TYPE \
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--do_eval \
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--do_lower_case \
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--data_dir $HANS_DIR \
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--model_name_or_path $MODEL_PATH \
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--max_seq_length 128 \
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-output_dir $MODEL_PATH \
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```
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This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
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The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
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```bash
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Heuristic entailed results:
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lexical_overlap: 0.9702
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subsequence: 0.9942
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constituent: 0.9962
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Heuristic non-entailed results:
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lexical_overlap: 0.199
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subsequence: 0.0396
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constituent: 0.118
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```
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