CNEC2_0_xlm-roberta-large

stulcrad/CNEC2_0_xlm-roberta-large

CNEC2_0_xlm-roberta-large is a token classification model from stulcrad. Use it when you need labels on individual words, such as names. 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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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Mar 3, 2024
Updated: Jun 5, 2025
SHA: 65bc5f21

Base models

FacebookAI/xlm-roberta-large

Datasets

Model card

From the Hugging Face model README

CNEC2_0_xlm-roberta-large

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2807
Precision: 0.8544
Recall: 0.8813
F1: 0.8676
Accuracy: 0.9631

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7031	0.56	500	0.3041	0.6755	0.6917	0.6835	0.9328
0.2981	1.11	1000	0.2336	0.7821	0.8011	0.7915	0.9489
0.2327	1.67	1500	0.1969	0.8030	0.7947	0.7988	0.9518
0.1962	2.22	2000	0.1898	0.8152	0.8501	0.8323	0.9583
0.1683	2.78	2500	0.1690	0.8053	0.8401	0.8223	0.9585
0.1499	3.33	3000	0.1810	0.8319	0.8444	0.8381	0.9602
0.1376	3.89	3500	0.1888	0.8340	0.8591	0.8464	0.9599
0.1198	4.44	4000	0.2022	0.8089	0.8494	0.8287	0.9570
0.1089	5.0	4500	0.1930	0.8320	0.8448	0.8383	0.9578
0.0911	5.56	5000	0.1945	0.8412	0.8544	0.8478	0.9627
0.0945	6.11	5500	0.1961	0.8424	0.8430	0.8427	0.9606
0.0695	6.67	6000	0.2186	0.8289	0.8559	0.8422	0.9588
0.0628	7.22	6500	0.2016	0.8567	0.8723	0.8644	0.9629
0.0563	7.78	7000	0.2195	0.8528	0.8727	0.8626	0.9617
0.0504	8.33	7500	0.2301	0.8508	0.8730	0.8618	0.9609
0.0444	8.89	8000	0.2135	0.8486	0.8780	0.8631	0.9629
0.0386	9.44	8500	0.2347	0.8451	0.8838	0.8640	0.9625
0.0355	10.0	9000	0.2314	0.8499	0.8670	0.8584	0.9620
0.0305	10.56	9500	0.2467	0.8532	0.8709	0.8619	0.9627
0.0283	11.11	10000	0.2602	0.8440	0.8687	0.8562	0.9615
0.0217	11.67	10500	0.2639	0.8548	0.8777	0.8661	0.9632
0.0224	12.22	11000	0.2688	0.8504	0.8780	0.8640	0.9623
0.0194	12.78	11500	0.2661	0.8545	0.8798	0.8670	0.9629
0.0224	13.33	12000	0.2731	0.8512	0.8798	0.8653	0.9623
0.014	13.89	12500	0.2778	0.8537	0.8766	0.8650	0.9629
0.0146	14.44	13000	0.2783	0.8551	0.8798	0.8673	0.9629
0.0142	15.0	13500	0.2807	0.8544	0.8813	0.8676	0.9631

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: xlm-roberta
License: mit
Languages: cs
Created: Mar 3, 2024
Updated: Jun 5, 2025