modelBeto5

cuadron11/modelBeto5

modelBeto5 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: May 5, 2023
Updated: May 5, 2023
SHA: aabdf16e

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Model card

From the Hugging Face model README

modelBeto5

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: 0.1686
Precision: 0.5990
Recall: 0.6541
F1: 0.6253
Accuracy: 0.9727

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: 5e-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	0.2706	0.0	0.0	0.0	0.9451
No log	2.0	58	0.3328	0.0	0.0	0.0	0.9451
No log	3.0	87	0.1872	0.0476	0.0108	0.0176	0.9320
No log	4.0	116	0.1428	0.3971	0.1459	0.2134	0.9551
No log	5.0	145	0.1169	0.4690	0.2865	0.3557	0.9614
No log	6.0	174	0.1259	0.5414	0.5297	0.5355	0.9629
No log	7.0	203	0.1166	0.4575	0.6108	0.5231	0.9604
No log	8.0	232	0.1240	0.6149	0.4919	0.5465	0.9693
No log	9.0	261	0.1145	0.5276	0.5676	0.5469	0.9681
No log	10.0	290	0.1377	0.5612	0.5946	0.5774	0.9688
No log	11.0	319	0.1321	0.5833	0.6432	0.6118	0.9700
No log	12.0	348	0.1549	0.6581	0.5514	0.6	0.9717
No log	13.0	377	0.1482	0.6080	0.6541	0.6302	0.9713
No log	14.0	406	0.1589	0.5348	0.6649	0.5928	0.9675
No log	15.0	435	0.1507	0.6178	0.6378	0.6277	0.9720
No log	16.0	464	0.1554	0.6082	0.6378	0.6227	0.9720
No log	17.0	493	0.1658	0.5918	0.6270	0.6089	0.9708
0.0785	18.0	522	0.1616	0.5792	0.6919	0.6305	0.9715
0.0785	19.0	551	0.1632	0.6059	0.6649	0.6340	0.9717
0.0785	20.0	580	0.1638	0.6103	0.6432	0.6263	0.9726
0.0785	21.0	609	0.1603	0.6010	0.6432	0.6214	0.9724
0.0785	22.0	638	0.1652	0.6078	0.6703	0.6375	0.9722
0.0785	23.0	667	0.1577	0.6440	0.6649	0.6543	0.9738
0.0785	24.0	696	0.1600	0.6492	0.6703	0.6596	0.9743
0.0785	25.0	725	0.1663	0.6256	0.6595	0.6421	0.9733
0.0785	26.0	754	0.1686	0.6106	0.6865	0.6463	0.9713
0.0785	27.0	783	0.1691	0.5951	0.6595	0.6256	0.9720
0.0785	28.0	812	0.1668	0.61	0.6595	0.6338	0.9731
0.0785	29.0	841	0.1679	0.5931	0.6541	0.6221	0.9724
0.0785	30.0	870	0.1678	0.6162	0.6595	0.6371	0.9734
0.0785	31.0	899	0.1683	0.6040	0.6595	0.6305	0.9729
0.0785	32.0	928	0.1686	0.5990	0.6541	0.6253	0.9727

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: May 5, 2023
Updated: May 5, 2023