CNEC_1_1_Supertypes_robeczech-base

stulcrad/CNEC_1_1_Supertypes_robeczech-base

CNEC_1_1_Supertypes_robeczech-base 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 cc-by-nc-sa-4.0.

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

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-nc-sa-4.0
Model type: roberta
Access: Public
Created: Apr 23, 2024
Updated: Apr 29, 2024
SHA: f5b0faf4

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Base models

ufal/robeczech-base

Datasets

cnec

Model card

From the Hugging Face model README

CNEC_1_1_Supertypes_robeczech-base

This model is a fine-tuned version of ufal/robeczech-base on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2799
Precision: 0.8446
Recall: 0.8912
F1: 0.8673
Accuracy: 0.9518

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.0614	1.7	500	0.6385	0.2880	0.1057	0.1546	0.8234
0.5512	3.4	1000	0.3567	0.7105	0.7542	0.7317	0.9197
0.3472	5.1	1500	0.2644	0.7602	0.8254	0.7914	0.9342
0.2659	6.8	2000	0.2466	0.7945	0.8492	0.8209	0.9389
0.2169	8.5	2500	0.2240	0.8252	0.8621	0.8432	0.9453
0.1797	10.2	3000	0.2113	0.8345	0.8714	0.8525	0.9487
0.1609	11.9	3500	0.2178	0.8213	0.8815	0.8503	0.9487
0.1371	13.61	4000	0.2126	0.8406	0.8811	0.8603	0.9509
0.1237	15.31	4500	0.2127	0.8422	0.8775	0.8595	0.9510
0.1101	17.01	5000	0.2065	0.8520	0.8855	0.8684	0.9538
0.0988	18.71	5500	0.2113	0.8457	0.8895	0.8671	0.9534
0.0904	20.41	6000	0.2280	0.8390	0.8895	0.8635	0.9523
0.0831	22.11	6500	0.2268	0.8430	0.8948	0.8681	0.9532
0.0758	23.81	7000	0.2472	0.8396	0.8864	0.8624	0.9502
0.0713	25.51	7500	0.2377	0.8402	0.8877	0.8633	0.9511
0.066	27.21	8000	0.2533	0.8346	0.8855	0.8593	0.9495
0.0591	28.91	8500	0.2449	0.8494	0.8926	0.8704	0.9527
0.0601	30.61	9000	0.2503	0.8421	0.8890	0.8649	0.9527
0.0528	32.31	9500	0.2605	0.8474	0.8935	0.8698	0.9514
0.051	34.01	10000	0.2677	0.8389	0.8886	0.8630	0.9511
0.0462	35.71	10500	0.2628	0.8391	0.8921	0.8648	0.9513
0.0438	37.41	11000	0.2629	0.8457	0.8939	0.8691	0.9526
0.0423	39.12	11500	0.2673	0.8406	0.8930	0.8660	0.9502
0.0395	40.82	12000	0.2700	0.8423	0.8904	0.8657	0.9518
0.0386	42.52	12500	0.2716	0.8486	0.8943	0.8709	0.9528
0.0384	44.22	13000	0.2727	0.8465	0.8921	0.8687	0.9523
0.0352	45.92	13500	0.2741	0.8494	0.8926	0.8704	0.9526
0.0351	47.62	14000	0.2776	0.8469	0.8926	0.8691	0.9520
0.0327	49.32	14500	0.2799	0.8446	0.8912	0.8673	0.9518

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: roberta
License: cc-by-nc-sa-4.0
Created: Apr 23, 2024
Updated: Apr 29, 2024