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calogero-jerik-scozzaro/BERT_MULTIPLEYE_no_HumanRights

sourceHugging Faceupdated 5mo agoView on Hugging Face

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Model Card

BERTMULTIPLEYEno_HumanRights

This repository contains a BERT encoder saved after eye-tracking fine-tuning in the VDA_ET workflow.

The temporary token-level regression head used during training is not included. Load the checkpoint with AutoModel.from_pretrained for downstream encoder analysis or continued fine-tuning.

python

from transformers import AutoModel, AutoTokenizer

model_id = "calogero-jerik-scozzaro/BERT_MULTIPLEYE_no_HumanRights"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModel.from_pretrained(model_id)

Training metadata

Field	Value
`all_train_texts`	ArgPISACowsMilk, ArgPISARapaNui, LearningMobility, LitAlchemist, LitBrokenApril, LitMagicMountain, LitSolaris, PopSciCaveman, PopSciMultiplEYE
`batch_size`	8
`epochs`	100
`learning_rate`	2e-05
`max_length`	256
`measures`	FFD, FPRT, TFT, RRT, skipped, FPF, RR
`num_train_sentences`	90
`source_model`	dbmdz/bert-base-italian-uncased
`stage`	1
`stage_train_texts`	ArgPISACowsMilk, ArgPISARapaNui, LearningMobility, LitAlchemist, LitBrokenApril, LitMagicMountain, LitSolaris, PopSciCaveman, PopSciMultiplEYE
`test_texts`	HumanRights
`variant`	BERTMULTIPLEYEno_HumanRights

The uploaded files include et_label_scaler.json, which records the min-max scaling statistics used for the eye-tracking labels.