modelBeto6

cuadron11/modelBeto6

modelBeto6 is a token classification model from cuadron11. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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Hugging Face

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: Apr 29, 2023
Updated: Apr 29, 2023
SHA: 1f7590b1

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Model card

From the Hugging Face model README

modelBeto6

This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1808
Precision: 0.6219
Recall: 0.6545
F1: 0.6378
Accuracy: 0.9737

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: 6e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 32

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	29	0.2309	0.0	0.0	0.0	0.9440
No log	2.0	58	0.2034	0.0	0.0	0.0	0.9440
No log	3.0	87	0.1685	0.1429	0.0157	0.0283	0.9476
No log	4.0	116	0.1425	0.3034	0.1414	0.1929	0.9546
No log	5.0	145	0.1285	0.3802	0.2408	0.2949	0.9589
No log	6.0	174	0.1283	0.5922	0.3194	0.4150	0.9696
No log	7.0	203	0.1337	0.5630	0.3979	0.4663	0.9715
No log	8.0	232	0.1184	0.5505	0.6283	0.5868	0.9686
No log	9.0	261	0.1308	0.5882	0.5759	0.5820	0.9729
No log	10.0	290	0.1329	0.5989	0.5550	0.5761	0.9729
No log	11.0	319	0.1549	0.6781	0.5183	0.5875	0.9742
No log	12.0	348	0.1578	0.6221	0.5602	0.5895	0.9732
No log	13.0	377	0.1505	0.6117	0.6021	0.6069	0.9716
No log	14.0	406	0.1671	0.6412	0.5707	0.6039	0.9729
No log	15.0	435	0.1684	0.5902	0.5654	0.5775	0.9710
No log	16.0	464	0.1707	0.6216	0.6021	0.6117	0.9727
No log	17.0	493	0.1715	0.6453	0.5812	0.6116	0.9737
0.0738	18.0	522	0.1729	0.5734	0.6545	0.6112	0.9701
0.0738	19.0	551	0.1815	0.5990	0.6021	0.6005	0.9716
0.0738	20.0	580	0.1746	0.6354	0.6387	0.6371	0.9732
0.0738	21.0	609	0.1654	0.6686	0.5916	0.6278	0.9749
0.0738	22.0	638	0.1678	0.6359	0.6492	0.6425	0.9741
0.0738	23.0	667	0.1704	0.6218	0.6283	0.625	0.9742
0.0738	24.0	696	0.1746	0.6685	0.6440	0.6560	0.9747
0.0738	25.0	725	0.1772	0.6224	0.6387	0.6305	0.9739
0.0738	26.0	754	0.1792	0.6484	0.6178	0.6327	0.9741
0.0738	27.0	783	0.1788	0.6383	0.6283	0.6332	0.9741
0.0738	28.0	812	0.1802	0.6281	0.6545	0.6410	0.9741
0.0738	29.0	841	0.1803	0.6443	0.6545	0.6494	0.9747
0.0738	30.0	870	0.1804	0.6495	0.6597	0.6545	0.9749
0.0738	31.0	899	0.1805	0.6443	0.6545	0.6494	0.9746
0.0738	32.0	928	0.1808	0.6219	0.6545	0.6378	0.9737

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: bert
Created: Apr 29, 2023
Updated: Apr 29, 2023