Modelpublic

calogero-jerik-scozzaro/BERT_VDA_MULTIPLEYE_no_HumanRights_seed_44

sourceHugging Faceupdated 5mo agoView on Hugging Face

0likes7downloads

Model Card

BERTVDAMULTIPLEYEnoHumanRightsseed44

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_VDA_MULTIPLEYE_no_HumanRights_seed_44"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModel.from_pretrained(model_id)

Training metadata

Field	Value
`all_train_texts`	lupo, lupoe, ciclisti, ciclistis, 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
`seed`	44
`source_model`	downstream-tasks/outputs/intermediatestages/44/BERTVDAMULTIPLEYEnoHumanRights/stage1
`stage`	2
`stage_train_texts`	ArgPISACowsMilk, ArgPISARapaNui, LearningMobility, LitAlchemist, LitBrokenApril, LitMagicMountain, LitSolaris, PopSciCaveman, PopSciMultiplEYE
`test_texts`	HumanRights
`variant`	BERTVDAMULTIPLEYEnoHumanRights

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