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import os
from pathlib import Path
from typing import Union
import pandas as pd
import pytorch_lightning as pl
import wandb
from transformers import logging
from src.cross_validate import CrossValidation
from src.data_reader import load_train_test_df
from src.model_finetuning.config import CONFIG
from src.model_finetuning.model import BertLightningModel
from src.model_finetuning.train import predict, train
from src.solutions.base_solution import BaseSolution
from src.utils import get_random_string, get_x_columns
logging.set_verbosity_error()
os.environ['GROUP_NAME'] = 'train_deberta_model-' + get_random_string(6)
class BertFinetuningPredictor(BaseSolution):
def __init__(
self,
model_name="microsoft/deberta-v3-large",
num_classes=6,
lr=2e-5,
batch_size=8,
num_workers=8,
max_length=512,
weight_decay=0.01,
accelerator='gpu',
max_epochs=5,
accumulate_grad_batches=4,
precision=16,
gradient_clip_val=1000,
train_size=0.8,
num_cross_val_splits=5,
num_frozen_layers=20,
):
super(BertFinetuningPredictor, self).__init__()
self.config = dict(
model_name=model_name,
num_classes=num_classes,
lr=lr,
batch_size=batch_size,
num_workers=num_workers,
max_length=max_length,
weight_decay=weight_decay,
accelerator=accelerator,
max_epochs=max_epochs,
accumulate_grad_batches=accumulate_grad_batches,
precision=precision,
gradient_clip_val=gradient_clip_val,
train_size=train_size,
num_cross_val_splits=num_cross_val_splits,
num_frozen_layers=num_frozen_layers,
)
def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs):
train_df = pd.concat([X, y], axis='columns')
val_X, val_y, = kwargs['val_X'], kwargs['val_y']
val_df = pd.concat([val_X, val_y], axis='columns')
self.model: BertLightningModel = train(self.config, train_df, val_df, verbose=False)
def predict(self, X: pd.DataFrame) -> pd.DataFrame:
assert self.model is not None, "Model is not trained yet"
predictions = predict(self.config, self.model, X)
return predictions
def save(self, directory: Union[str, Path]) -> None:
directory = Path(directory)
if not directory.is_dir():
directory.mkdir(parents=True)
trainer = pl.Trainer(accelerator=self.config['accelerator'])
trainer.model = self.model
# trainer.save_checkpoint(directory / "lightning_model.ckpt", weights_only=True)
def load(self, directory: Union[str, Path]) -> None:
filepath = Path(directory) / "lightning_model.ckpt"
if not filepath.is_file():
raise OSError(f"File not found: {filepath.resolve()}")
self.model = BertLightningModel.load_from_checkpoint(str(filepath), config=self.config)
def main():
config = CONFIG
saving_dir = Path("checkpoints/finetune_bert")
train_df, test_df = load_train_test_df()
x_columns = get_x_columns()
train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
predictor = BertFinetuningPredictor(config)
cv = CrossValidation(saving_dir=str(saving_dir), n_splits=config['num_cross_val_splits'])
results = cv.fit(predictor, train_x, train_y)
print(f"CV metric: {results.iloc[len(results) - 1].mean()}")
print("CV results")
print(results)
results.to_csv(saving_dir / "cv_results.csv")
submission_df = cv.predict(test_df)
submission_df.to_csv(saving_dir / "submission.csv", index=False)
wandb.init(
project="automated_essay_evaluator",
entity="parmezano",
group=os.environ['GROUP_NAME'],
name='weights_cv'
)
art = wandb.Artifact("bert-finetune-solution", type="model")
art.add_dir(str(saving_dir.absolute()), name='data/')
wandb.log_artifact(art)
print("Finished training!")
if __name__ == '__main__':
main()
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