Emotion-specific TRT Predictor: Official ZuCo/Provo -> Emotion Gaze
This repository contains a RoBERTa-base token-level eye-tracking predictor. This export predicts one scalar per token: scaled total reading time (TRT).
Model
- Encoder:
roberta-base - Head: linear regression layer, hidden size 768 -> 1
- Output feature order:
TRT - TRT index:
0 - Weight file:
emotion_trt_predictor_lr2e5_preval10_seed42.safetensors
Training
- Pretraining data: Provo + official ZuCo 2.0 NR/TSR, CMCL/Provo-scaled
- Fine-tuning data: official ZuCo 1.0 sentiment + IITB SA-I/SA-II sentiment gaze, CMCL/Provo-scaled
- Seed: 42
- Best LR: 2e-5
- Max length: 512
Validation MAE
| Feature | MAE |
|---|---|
| TRT | 1.892526 |
Bundle Files
The export includes the files expected by downstream ET/VAD pipelines:
.gitattributes
README.md
model.py
tokenizer.json
tokenizer_config.json
emotion_trt_predictor_lr2e5_preval10_seed42.safetensors
Usage
from huggingface_hub import snapshot_download
from model import load_et_predictor, predict_word_trt
model_dir = snapshot_download("YOUR_NAMESPACE/YOUR_REPO")
model, tokenizer = load_et_predictor(model_dir)
words, trt = predict_word_trt("This sentence is emotionally intense.", model, tokenizer)
The exported model.py is a self-contained inference wrapper matching the training architecture.
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Base model
FacebookAI/roberta-base