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README.md
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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We used the
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The command used for training is the following. For more information, please refer to the paper and GitHub repository.
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```python
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--
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--
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--batch_size=32 \
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--input_max_len=
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--target_max_len=
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--weight_decay=0.01 \
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--evaluation_strategy='epoch' \
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--save_strategy='epoch' \
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--logging_strategy='epoch' \
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--
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--
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--
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--USPTO_test_data_path='/home/acf15718oa/ReactionT5_neword/data/USPTO_MIT/MIT_separated/test.csv' \
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--disable_tqdm \
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--
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```
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### Results
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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We used the [USPTO_MIT dataset](https://yzhang.hpc.nyu.edu/T5Chem/index.html) for model finetuning.
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The command used for training is the following. For more information, please refer to the paper and GitHub repository.
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```python
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cd task_forward
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python finetune.py \
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--output_dir='t5' \
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--epochs=50 \
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--lr=2e-5 \
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--batch_size=32 \
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--input_max_len=200 \
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--target_max_len=150 \
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--evaluation_strategy='epoch' \
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--save_strategy='epoch' \
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--logging_strategy='epoch' \
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--save_total_limit=10 \
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--train_data_path='../data/USPTO_MIT/MIT_separated/train.csv' \
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--valid_data_path='../data/USPTO_MIT/MIT_separated/val.csv' \
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--disable_tqdm \
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--model_name_or_path='sagawa/ReactionT5v2-forward'
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```
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### Results
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