skboy/emotion_et_model

Emotion-specific ET Predictor 2: CMCL -> IITB Scale-Zero

This repository contains a RoBERTa-base token-level eye-tracking feature predictor.

Files

• et_predictor2_iitb_scalezero_seed42.safetensors: model weights
• model.py: self-contained inference wrapper
• config.json: RoBERTa-base config
• tokenizer.json, tokenizer_config.json, vocab.json, merges.txt, special_tokens_map.json: tokenizer files
• metrics_best.json: selected checkpoint validation metrics
• zero_scaling_ablation_summary.tsv: ablation summary

Model

• Encoder: roberta-base
• Regression head: hidden size 768 -> 5 eye-tracking features
• Feature order: [nFix, FFD, GPT, TRT, fixProp]
• TRT index: 3
• Selected run: zero_scaling_ablation/scale_zero_rows
• Selected epoch: 121
• Selected metric: all
• Selected score: 4.754521422774324

Validation MAE

Feature	MAE
nFix	4.639451
FFD	0.844525
GPT	2.705354
TRT	1.813485
fixProp	13.769792
all	4.754521

Usage

Install runtime dependencies first:

pip install torch transformers safetensors huggingface_hub numpy

Load from Hugging Face:

from huggingface_hub import snapshot_download
import sys

model_dir = snapshot_download("YOUR_NAMESPACE/YOUR_REPO")
sys.path.insert(0, model_dir)

from model import load_et_predictor, predict_word_trt

model, tokenizer = load_et_predictor(model_dir)
words, trt = predict_word_trt("This sentence is emotionally intense.", model, tokenizer)

For all five ET features:

from model import load_et_predictor, predict_word_features

model, tokenizer = load_et_predictor(model_dir)
words, features = predict_word_features("This sentence is emotionally intense.", model, tokenizer)