custom-ner-model-viajes

hucruz/custom-ner-model-viajes

custom-ner-model-viajes is a token classification model from hucruz. 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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At a glance

Task: Token Classification
Library: transformers
Model type: distilbert
Access: Public
Created: Feb 1, 2023
Updated: Sep 19, 2023
SHA: 19822614

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Base models

dccuchile/distilbert-base-spanish-uncased

Model card

From the Hugging Face model README

custom-ner-model2

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

Loss: 0.2050
Precision: 0.8542
Recall: 0.8817
F1: 0.8677
Accuracy: 0.9595

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	105	0.5185	0.5840	0.5484	0.5656	0.8596
No log	2.0	210	0.3212	0.7365	0.7312	0.7338	0.9050
No log	3.0	315	0.2440	0.8123	0.8065	0.8094	0.9389
No log	4.0	420	0.2186	0.8014	0.8100	0.8057	0.9431
0.4107	5.0	525	0.1911	0.8481	0.8602	0.8541	0.9516
0.4107	6.0	630	0.1931	0.8235	0.8530	0.8380	0.9546
0.4107	7.0	735	0.1720	0.8368	0.8638	0.8501	0.9570
0.4107	8.0	840	0.1858	0.8385	0.8746	0.8561	0.9583
0.4107	9.0	945	0.1858	0.85	0.8530	0.8515	0.9552
0.0667	10.0	1050	0.1961	0.8526	0.8710	0.8617	0.9564
0.0667	11.0	1155	0.1970	0.8537	0.8781	0.8657	0.9589
0.0667	12.0	1260	0.1865	0.8478	0.8781	0.8627	0.9619
0.0667	13.0	1365	0.1994	0.8379	0.8710	0.8541	0.9583
0.0667	14.0	1470	0.1913	0.8507	0.8781	0.8642	0.9613
0.0274	15.0	1575	0.2064	0.8512	0.8817	0.8662	0.9595
0.0274	16.0	1680	0.2053	0.8478	0.8781	0.8627	0.9601
0.0274	17.0	1785	0.2037	0.8576	0.8853	0.8713	0.9601
0.0274	18.0	1890	0.2056	0.8632	0.8817	0.8723	0.9595
0.0274	19.0	1995	0.2066	0.8571	0.8817	0.8693	0.9601
0.0162	20.0	2100	0.2050	0.8542	0.8817	0.8677	0.9595

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: distilbert
Created: Feb 1, 2023
Updated: Sep 19, 2023