GPT-2-medium fine-tuned for Sentiment Analysis ππ
OpenAI's GPT-2 medium fine-tuned on SST-2 dataset for Sentiment Analysis downstream task.
Details of GPT-2
The GPT-2 model was presented in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
Model fine-tuning ποΈβ
The model has been finetuned for 10 epochs on standard hyperparameters
Val set metrics π§Ύ
|precision | recall | f1-score |support|
|----------|----------|---------|----------|-------|
|negative | 0.92 | 0.92| 0.92| 428 |
|positive | 0.92 | 0.93| 0.92| 444 |
|----------|----------|---------|----------|-------|
|accuracy| | | 0.92| 872 |
|macro avg| 0.92| 0.92| 0.92| 872 |
|weighted avg| 0.92| 0.92| 0.92| 872 |
Model in Action π
from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
tokenizer = GPT2Tokenizer.from_pretrained("michelecafagna26/gpt2-medium-finetuned-sst2-sentiment")
model = GPT2ForSequenceClassification.from_pretrained("michelecafagna26/gpt2-medium-finetuned-sst2-sentiment")
inputs = tokenizer("I love it", return_tensors="pt")
model(**inputs).logits.argmax(axis=1)
# 1: Positive, 0: Negative
# Output: tensor([1])
This model card is based on "mrm8488/t5-base-finetuned-imdb-sentiment" by Manuel Romero/@mrm8488
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