transformers safetensors t5 text2text-generation text-generation en license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
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/