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skboy/
emotion_trt_roberta_lr2e5_preval10

Name: emotion_trt_roberta_lr2e5_preval10
Availability: InStock

FacebookAI/roberta-base

finetune

endpoints_compatible

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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:

text

.gitattributes
README.md
model.py
tokenizer.json
tokenizer_config.json
emotion_trt_predictor_lr2e5_preval10_seed42.safetensors

Usage

python

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.

skboy/emotion_trt_roberta_lr2e5_preval10