UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_MULTI

franfj/UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_MULTI

UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask1_MULTI is a text classification model from franfj. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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Model card

From the Hugging Face model README

SEMEVAL23_TASK3_SUBTASK1_MULTI

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7683
F1: 0.6317

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.9869	1.0	40	0.9517	0.4244
0.5569	2.0	80	0.7365	0.5514
0.4559	3.0	120	0.7829	0.5348
0.2018	4.0	160	0.6281	0.6241
0.1125	5.0	200	0.9291	0.5614
0.2594	6.0	240	1.0367	0.5884
0.1883	7.0	280	0.7683	0.6317
0.1083	8.0	320	0.8660	0.6106
0.0659	9.0	360	0.8695	0.6189
0.2554	10.0	400	1.0094	0.5839

Framework versions

Transformers 4.25.1
Pytorch 1.13.1
Datasets 2.8.0
Tokenizers 0.13.2

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Jan 20, 2023
Updated: May 11, 2025