Files

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

Usage

python

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