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Implemented initial training structure, adding further logic soon including loss, stopping, optimisation and loop

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charlie-rasberry
2026-02-23 12:54:23 +00:00
parent 76d9b8509b
commit 7bd68108d0
3 changed files with 74 additions and 10 deletions
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14
src/dataset.py
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@@ -43,14 +43,16 @@ class ReviewDataset(Dataset):
return {
'input_ids': encoding['input_ids'].squeeze(0),
'attention_mask': encoding['attention_mask'].squeeze(0),
'bug_report': torch.tensor(self.df.iloc[idx]['bug_report']),
'feature_request': torch.tensor(self.df.iloc[idx]['feature_request']),
'aspect': torch.tensor(self.df.iloc[idx]['aspect']),
'aspect_sentiment': torch.tensor(self.df.iloc[idx]['aspect_sentiment'])
'bug_report': torch.tensor(self.df.iloc[idx]['bug_report'], dtype=torch.long),
'feature_request': torch.tensor(self.df.iloc[idx]['feature_request'], dtype=torch.long),
'aspect': torch.tensor(self.df.iloc[idx]['aspect'], dtype=torch.long),
'aspect_sentiment': torch.tensor(self.df.iloc[idx]['aspect_sentiment'], dtype=torch.long)
}
if __name__ == "__main__":
dataset = ReviewDataset("data/processed/original_train.csv", AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base"))
print(dataset.__getitem__(1))
# uber = ReviewDataset("data/processed/original_train.csv", AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base"))
# print(uber.__getitem__(1))