bert-finetuned-ner

Este es un moelo afinado sobre el modelo BSC-LT/roberta-base-bne-capitel-ner sobre conll2002 dataset. Se lora un excelente rendimiento porque el modelo original fue preentrenado con textos en español logrando los siguientes resultados:

Loss: 0.0950
Precision: 0.8599
Recall: 0.8773
F1: 0.8685
Accuracy: 0.9787

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1045	1.0	521	0.0932	0.8593	0.8704	0.8648	0.9764
0.0343	2.0	1042	0.0870	0.8616	0.8757	0.8686	0.9781
0.019	3.0	1563	0.0950	0.8599	0.8773	0.8685	0.9787

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 2.20.0
Tokenizers 0.20.0

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for raulgdp/bert-finetuned-ner

Base model

BSC-LT/roberta-base-bne-capitel-ner

Finetuned

(1)

this model

Dataset used to train raulgdp/bert-finetuned-ner

Collection including raulgdp/bert-finetuned-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

0.860
Recall on conll2002
validation set self-reported

0.877
F1 on conll2002
validation set self-reported

0.869
Accuracy on conll2002
validation set self-reported

0.979