Edit model card

GPT-2-medium fine-tuned for Sentiment Analysis πŸ‘πŸ‘Ž

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 πŸ‹οΈβ€

The model has been finetuned for 10 epochs on standard hyperparameters

Val set metrics 🧾

           |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 πŸš€

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

Downloads last month
525
Safetensors
Model size
380M params
Tensor type
F32
Β·
U8
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train michelecafagna26/gpt2-medium-finetuned-sst2-sentiment