Plant-gob-roberta-base-bne-capitel-ner

Este modelo fue afinado sobre el modelo BSC-LT/roberta-base-bne-capitel-ner sobre conll2002 dataset con un batch_size de 16 logrando la siguiente precisión.

Loss: 0.1071
Precision: 0.8628
Recall: 0.8826
F1: 0.8726
Accuracy: 0.9785

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0681	1.0	521	0.0818	0.8645	0.8842	0.8742	0.9789
0.0313	2.0	1042	0.0841	0.8516	0.8768	0.8640	0.9779
0.0179	3.0	1563	0.0979	0.8584	0.8773	0.8677	0.9781
0.0091	4.0	2084	0.0996	0.8600	0.8819	0.8708	0.9784
0.0068	5.0	2605	0.1071	0.8628	0.8826	0.8726	0.9785

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

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Dataset used to train raulgdp/Plant-gob-roberta-base-bne-capitel-ner

Collection including raulgdp/Plant-gob-roberta-base-bne-capitel-ner

Modelos de NER-conll2002

Collection

En ésta colección se encuentran los últimos modelos de NER usando las últimas técnicas • 6 items • Updated Mar 2

Evaluation results

Precision on conll2002
validation set self-reported

0.863
Recall on conll2002
validation set self-reported

0.883
F1 on conll2002
validation set self-reported

0.873
Accuracy on conll2002
validation set self-reported

0.978