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

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/