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README.md
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# sentiment-polish-gpt2-large
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This model is a fine-tuned version of [sdadas/polish-gpt2-large](https://huggingface.co/sdadas/polish-gpt2-large) on the
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It achieves the following results on the evaluation set:
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- epoch: 10.0
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- eval_accuracy: 0.9634
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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# sentiment-polish-gpt2-large
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This model is a fine-tuned version of [sdadas/polish-gpt2-large](https://huggingface.co/sdadas/polish-gpt2-large) on the [polemo2-official](https://huggingface.co/datasets/clarin-pl/polemo2-official) dataset.
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It achieves the following results on the evaluation set:
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- epoch: 10.0
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- eval_accuracy: 0.9634
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## Model description
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Trained from [polish-gpt2-large](https://huggingface.co/sdadas/polish-gpt2-large)
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## Intended uses & limitations
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Sentiment analysis - neutral/negative/positive/ambiguous
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## Training and evaluation data
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Merged all rows from [polemo2-official](https://huggingface.co/datasets/clarin-pl/polemo2-official) dataset.
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Discarded rows with length > 512.
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Train/test split: 80%/20%
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Datacollator:
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```py
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data_collator = DataCollatorWithPadding(
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tokenizer=tokenizer,
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padding="longest",
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max_length=MAX_INPUT_LENGTH,
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pad_to_multiple_of=8
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)
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```
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## Training procedure
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GPU: 2x RTX 4060Ti 16GB
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Training time: 29:16:50
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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