Create README.md

README.md

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+---
+datasets:
+- imdb
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- movies
+- gpt2
+- sentiment-analysis
+- fine-tuned
+---
+
+# Fine-tuned GPT-2 Model for IMDb Movie Review Sentiment Analysis
+
+## Model Description
+
+This is a GPT-2 model fine-tuned on the IMDb movie review dataset for sentiment analysis. It classifies a movie review text into two classes: "positive" or "negative".
+
+## Intended Uses & Limitations
+
+This model is intended to be used for binary sentiment analysis of English movie reviews. It can determine whether a review is positive or negative. It should not be used for languages other than English, or for text with ambiguous sentiment.
+
+## How to Use
+
+Here's a simple way to use this model:
+
+```python
+from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
+
+tokenizer = GPT2Tokenizer.from_pretrained("hipnologo/gpt2-imdb-finetune")
+model = GPT2ForSequenceClassification.from_pretrained("hipnologo/gpt2-imdb-finetune")
+
+text = "Your review text here!"
+
+# encoding the input text
+input_ids = tokenizer.encode(text, return_tensors="pt")
+
+# Move the input_ids tensor to the same device as the model
+input_ids = input_ids.to(model.device)
+
+# getting the logits 
+logits = model(input_ids).logits
+
+# getting the predicted class
+predicted_class = logits.argmax(-1).item()
+
+print(f"The sentiment predicted by the model is: {'Positive' if predicted_class == 1 else 'Negative'}")
+```
+
+## Training Procedure
+The model was trained using the 'Trainer' class from the transformers library, with a learning rate of 2e-5, batch size of 1, and 3 training epochs.
+
+## Fine-tuning Details
+The model was fine-tuned using the IMDb movie review dataset.
+