eurobert-immigration-stance-negative

luissattelmayer/eurobert-immigration-stance-negative

eurobert-immigration-stance-negative is a text classification model from luissattelmayer. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

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

Downloads · 30 days

7% of all-time downloads

All-time downloads

161

Public

Parameters

212M

56.6 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors849 MB · 98%

At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: eurobert
Access: Public
Created: Mar 17, 2025
Updated: Mar 25, 2025
SHA: 6c3f997b

Try a prompt

Base models

EuroBERT/EuroBERT-210m

Model card

From the Hugging Face model README

EuroBERT-immigration-stance-positive

This model is a fine-tuned version of EuroBERT/EuroBERT-210m on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1444
Accuracy: 0.7389
F1 Macro: 0.7210
Accuracy Balanced: 0.7165
F1 Micro: 0.7389
Precision Macro: 0.7342
Recall Macro: 0.7165
Precision Micro: 0.7389
Recall Micro: 0.7389

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: 4
eval_batch_size: 40
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Accuracy Balanced	F1 Micro	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.697	1.0	654	0.6768	0.5908	0.3714	0.5	0.5908	0.2954	0.5	0.5908	0.5908
0.7267	2.0	1308	0.6543	0.5878	0.5797	0.6340	0.5878	0.6642	0.6340	0.5878	0.5878
0.7024	3.0	1962	0.9052	0.6779	0.6778	0.7044	0.6779	0.7076	0.7044	0.6779	0.6779
0.675	4.0	2616	1.1944	0.7206	0.7088	0.7073	0.7206	0.7108	0.7073	0.7206	0.7206
0.5142	5.0	3270	1.4177	0.7160	0.7097	0.7126	0.7160	0.7084	0.7126	0.7160	0.7160
0.3573	6.0	3924	1.6717	0.7267	0.7159	0.7148	0.7267	0.7172	0.7148	0.7267	0.7267
0.1643	7.0	4578	1.6307	0.7389	0.7295	0.7292	0.7389	0.7300	0.7292	0.7389	0.7389
0.1253	8.0	5232	1.8601	0.7374	0.7187	0.7141	0.7374	0.7331	0.7141	0.7374	0.7374
0.0937	9.0	5886	2.0865	0.7420	0.7209	0.7157	0.7420	0.7412	0.7157	0.7420	0.7420
0.0562	10.0	6540	2.1444	0.7389	0.7210	0.7165	0.7389	0.7342	0.7165	0.7389	0.7389

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Task: Text Classification
Library: transformers
Type: eurobert
License: apache-2.0
Created: Mar 17, 2025
Updated: Mar 25, 2025