README.md

metadata

license: mit
language: en
base_model: FacebookAI/roberta-large
pipeline_tag: text-classification
tags:
  - emotion
  - valence
  - arousal
  - memory
  - LUFY

LUFY — RoBERTa-large valence/arousal predictor

Fine-tuned roberta-large that predicts valence and arousal of a text (2-output regression), trained on EmoBank.

This model is part of LUFY — a RAG chatbot that selectively forgets unimportant conversations — where it estimates the emotional intensity of conversation turns as one signal of memory importance. See the paper: Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting.

The companion conversation dataset is at RuiSumida/LUFY (dataset).

Files

• best_roberta_large.pth — PyTorch state_dict for RobertaForSequenceClassification (num_labels=2, outputs [valence, arousal])

Usage

import torch
from huggingface_hub import hf_hub_download
from transformers import RobertaTokenizer, RobertaForSequenceClassification

model = RobertaForSequenceClassification.from_pretrained("roberta-large", num_labels=2)
model_path = hf_hub_download(repo_id="RuiSumida/LUFY", filename="best_roberta_large.pth")
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

tokenizer = RobertaTokenizer.from_pretrained("roberta-large")
enc = tokenizer("I can't believe we won the finals!", max_length=128,
                padding="max_length", truncation=True, return_tensors="pt")
with torch.no_grad():
    valence, arousal = model(**enc).logits.squeeze()

Citation

If you use this model, please cite the LUFY paper: https://arxiv.org/abs/2409.12524