Update README.md

README.md

@@ -1,3 +1,29 @@
 ---
 license: cc-by-nc-sa-4.0
+language:
+- en
+pipeline_tag: text-generation
 ---
+# Purpose
+As a part of a project assignment in EPFL's CS-401 class, we need a simple model to extract the importance of the character from the movie plot. From the movie script data crawled from https://imsdb.com/, we calculated gold portion of each character over entire script, and joined it with movie plot datasets.
+
+# Model info
+- Input prompt format
+f"Predict the percentage of a movie's plot that a character takes up.\nCharacter: {character_name} \nPlot: {plot}"
+- Output
+13.4
+
+We used max_token = 2048 for training.
+Sample code
+```python
+tokenizer = T5Tokenizer.from_pretrained("Hyeongdon/t5-large-character_plot_portion") # same as default t5 tokenizer
+model = T5ForConditionalGeneration.from_pretrained("Hyeongdon/t5-large-character_plot_portion")
+model_inputs = tokenizer(prompts, max_length=2048, truncation=True, padding='max_length', return_tensors='pt')
+model.eval()
+with torch.no_grad():
+    probs = model.generate(input_ids=model_inputs['input_ids'].to(device), attention_mask=model_inputs['attention_mask'].to(device))
+```
+
+# Limitation & Tips
+ChatGPT shows better performance without any fine-tuning. Based on our internal metric, T5-large slightly underperforms compared to GPT-3.5 or GPT-4.
+If you are interested in our research project, refer https://margg00.github.io/