init
Browse files
training_scripts/finetune_t5.py
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@@ -195,6 +195,7 @@ def train(
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else:
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best_model = sorted(model_score, key=lambda x: x[2])[-1][0]
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copy_tree(best_model, f'{output_dir}/best_model')
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else:
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logging.info('skip hyperparameter search & model training (already done)')
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else:
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best_model = sorted(model_score, key=lambda x: x[2])[-1][0]
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copy_tree(best_model, f'{output_dir}/best_model')
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tokenizer.save_pretrained(f'{output_dir}/best_model')
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else:
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logging.info('skip hyperparameter search & model training (already done)')
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training_scripts/script.sh
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python finetune_t5.py --dataset-name ja --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-ja --model-organization cardiffnlp
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python finetune_t5.py --dataset-name gr --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-gr --model-organization cardiffnlp
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python finetune_t5.py --dataset-name es --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-es --model-organization cardiffnlp
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python finetune_t5.py --dataset-name en --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en --model-organization cardiffnlp
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# TweetTopic
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python finetune_t5.py --dataset-name en_2022 --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp
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python finetune_t5.py --dataset-name es --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-es --model-organization cardiffnlp
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python finetune_t5.py --dataset-name en --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en --model-organization cardiffnlp
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python finetune_t5.py --dataset-name ja --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-ja --model-organization cardiffnlp
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python finetune_t5.py --dataset-name gr --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-gr --model-organization cardiffnlp
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