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GPT2 custom & dataset metrics

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Jakub Adamski 2023-02-18 10:25:07 +01:00
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@ -62,6 +62,20 @@ Najważniejsze cechy:
- Accuracy: 99%
- MCC: 0.960
### Transformer Decoder - GPT2 - modyfikacja
Najważniejsze cechy:
- wytrenowany model gpt2
- typ modelu zmodyfikowany transformers.GPT2ForSequenceClassification - dodatkowe dwie warstwy liniowe
- input modelu - treść smsa
- output modelu - klasa 1 lub 2
- finetuning na zbiorze treningowym
- adamW optimizer
- learning rate 2e-5
- 8 batch size
- 4 epoch
- Accuracy: 99.1%
- MCC: 0.958
### Transformer Encoder-Decoder - T5
Najważniejsze cechy:
- wytrenowany model t5-base

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