finetune_huggingface_t5

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finetune_huggingface_t5

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FineTune HuggingFace's T5 implementation on NMT

finetune_huggingface_t5 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Finetune HuggingFace's T5

This repository allows you to finetune HuggingFace's T5 implementation on Neural Machine Translation.

## How to Use:

### 1. Create configuration file:

The first thing to do is to specify configurations in a config file. Therem you will input desired pretrained model size, training details, data paths, model prefix, and so on. Check out t5_config.yml for an example configuration file.

### 2. Specify experiment name, configuration and run fine-tuning:

Assuming your desired experiment name is en_pd and config file is in t5_config.yml, run the finetune_t5.py file as follows:
```
python finetune_t5.py --experiment_name=en_pd --config_path=t5_config.yml
```

This command begins finetuning T5 on your input parallel data and saves the experiment outputs to a created directory of experiment_name + current date and time : 
```
$PWD/experiments/en_pd_{date and time}
```
### 3. Evaluate fine-tuned model:

After training, you can evaluate an input test set (assuming src.txt and tgt.txt) with the following command:
```
python evaluate_test.py --experiment_path=experimets/en_pd_{date and time} --src_test_path=src.txt --tgt_test_path=tgt.txt
```

Make sure to run the help command below to see a full description and format of all input flags
```
python evaluate_test.py --helpshort
```
Other flags:
- --save_as_pretrained : boolean - If True, save the loaded model as a huggingface pretrained model

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