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#!/usr/bin/env bash
set -e
set -x
CKPT_NAME=gen-RAMS
MODEL=gen
python train.py --model=$MODEL --ckpt_name=$CKPT_NAME-pred \
--load_ckpt=checkpoints/$CKPT_NAME-what-head/epoch=2-v0.ckpt \
--dataset=RAMS \
--eval_only \
--train_file=data/RAMS_1.0/data/train.jsonlines \
--val_file=data/RAMS_1.0/data/dev.jsonlines \
--test_file=data/RAMS_1.0/data/test_head.jsonlines \
--train_batch_size=2 \
--eval_batch_size=4 \
--learning_rate=3e-5 \
--accumulate_grad_batches=4 \
--num_train_epochs=3
#span eval
#python src/genie/convert_gen_to_output5.py --gen-file=checkpoints/$CKPT_NAME-pred/predictions.jsonl \
#--output-file=checkpoints/$CKPT_NAME-pred/span_output.jsonl
#python data/RAMS_1.0/scorer/scorer.py -g=data/RAMS_1.0/data/test.jsonlines -p=checkpoints/$CKPT_NAME-pred/span_output.jsonl \
#--reuse_gold_format --do_all > checkpoints/$CKPT_NAME-pred/span_metrics.txt
# head eval
python src/genie/convert_gen_to_output5.py --gen-file=checkpoints/$CKPT_NAME-pred/predictions.jsonl \
--output-file=checkpoints/$CKPT_NAME-pred/output.jsonl --head-only
python data/RAMS_1.0/scorer/scorer.py -g=data/RAMS_1.0/data/test_head.jsonlines -p=checkpoints/$CKPT_NAME-pred/output.jsonl \
--reuse_gold_format --do_all > checkpoints/$CKPT_NAME-pred/head_metrics.txt
# head + coref eval
#python genie/convert_gen_to_output.py --gen-file=checkpoints/$CKPT_NAME-pred/predictions.jsonl \
#--test-file=data/RAMS_1.0/data/test_head_coref.jsonlines \
#--output-file=checkpoints/$CKPT_NAME-pred/coref_output.jsonl --head-only --coref
#python data/RAMS_1.0/scorer/scorer.py -g=data/RAMS_1.0/data/test_head_coref.jsonlines -p=checkpoints/$CKPT_NAME-pred/coref_output.jsonl \
#--reuse_gold_format --do_all > checkpoints/$CKPT_NAME-pred/coref_metrics.txt
# visualize
python visualize_output.py --result-file=checkpoints/$CKPT_NAME-pred/span_output.jsonl
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