bert_base_tcm_0.9_no_valor_objeto

ricardo-filho/bert_base_tcm_0.9_no_valor_objeto

bert_base_tcm_0.9_no_valor_objeto is a token classification model from ricardo-filho. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

Downloads · 30 days

2% of all-time downloads

All-time downloads

425

Public

Repo size

4.8 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin433 MB · 99%

At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Apr 12, 2023
Updated: Apr 12, 2023
SHA: 141d935a

Try a prompt

Datasets

ricardo-filho/tcm-0.9-no-valor-objeto

Model card

From the Hugging Face model README

bert_base_tcm_0.9_no_valor_objeto

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the ricardo-filho/tcm-0.9-no-valor-objeto dataset. It achieves the following results on the evaluation set:

Loss: 0.0176
Criterio Julgamento Precision: 0.8310
Criterio Julgamento Recall: 0.8310
Criterio Julgamento F1: 0.8310
Criterio Julgamento Number: 142
Data Sessao Precision: 0.7909
Data Sessao Recall: 0.9667
Data Sessao F1: 0.87
Data Sessao Number: 90
Modalidade Licitacao Precision: 0.9564
Modalidade Licitacao Recall: 0.9815
Modalidade Licitacao F1: 0.9688
Modalidade Licitacao Number: 648
Numero Exercicio Precision: 0.9362
Numero Exercicio Recall: 0.9788
Numero Exercicio F1: 0.9570
Numero Exercicio Number: 330
Objeto Licitacao Precision: 0.4460
Objeto Licitacao Recall: 0.5849
Objeto Licitacao F1: 0.5061
Objeto Licitacao Number: 106
Overall Precision: 0.8751
Overall Recall: 0.9316
Overall F1: 0.9025
Overall Accuracy: 0.9953

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Criterio Julgamento Precision	Criterio Julgamento Recall	Criterio Julgamento F1	Criterio Julgamento Number	Data Sessao Precision	Data Sessao Recall	Data Sessao F1	Data Sessao Number	Modalidade Licitacao Precision	Modalidade Licitacao Recall	Modalidade Licitacao F1	Modalidade Licitacao Number	Numero Exercicio Precision	Numero Exercicio Recall	Numero Exercicio F1	Numero Exercicio Number	Objeto Licitacao Precision	Objeto Licitacao Recall	Objeto Licitacao F1	Objeto Licitacao Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0159	1.0	3497	0.0176	0.8310	0.8310	0.8310	142	0.7909	0.9667	0.87	90	0.9564	0.9815	0.9688	648	0.9362	0.9788	0.9570	330	0.4460	0.5849	0.5061	106	0.8751	0.9316	0.9025	0.9953
0.0161	2.0	6994	0.0191	0.8312	0.9014	0.8649	142	0.7890	0.9556	0.8643	90	0.9580	0.9846	0.9711	648	0.9475	0.9848	0.9658	330	0.5556	0.6604	0.6034	106	0.8920	0.9476	0.9189	0.9954
0.0094	3.0	10491	0.0215	0.8125	0.9155	0.8609	142	0.7818	0.9556	0.86	90	0.9608	0.9846	0.9726	648	0.9503	0.9848	0.9673	330	0.5108	0.6698	0.5796	106	0.8834	0.9498	0.9154	0.9955
0.0057	4.0	13988	0.0212	0.8269	0.9085	0.8658	142	0.8095	0.9444	0.8718	90	0.9697	0.9861	0.9778	648	0.9501	0.9818	0.9657	330	0.5290	0.6887	0.5984	106	0.8935	0.9498	0.9208	0.9960
0.0049	5.0	17485	0.0214	0.8344	0.9225	0.8763	142	0.7905	0.9222	0.8513	90	0.9652	0.9830	0.9740	648	0.9474	0.9818	0.9643	330	0.5217	0.6792	0.5902	106	0.8894	0.9476	0.9176	0.9957
0.0036	6.0	20982	0.0297	0.8397	0.9225	0.8792	142	0.7748	0.9556	0.8557	90	0.9636	0.9799	0.9717	648	0.9585	0.9788	0.9685	330	0.5435	0.7075	0.6148	106	0.8922	0.9498	0.9201	0.9953
0.0016	7.0	24479	0.0297	0.8302	0.9296	0.8771	142	0.7925	0.9333	0.8571	90	0.9652	0.9830	0.9740	648	0.9467	0.9697	0.9581	330	0.5746	0.7264	0.6417	106	0.8948	0.9498	0.9215	0.9955
0.0016	8.0	27976	0.0298	0.8212	0.8732	0.8464	142	0.8095	0.9444	0.8718	90	0.9666	0.9815	0.9740	648	0.9524	0.9697	0.9610	330	0.5746	0.7264	0.6417	106	0.8974	0.9438	0.9200	0.9955
0.0011	9.0	31473	0.0319	0.7949	0.8732	0.8322	142	0.7788	0.9	0.8351	90	0.9650	0.9799	0.9724	648	0.9467	0.9697	0.9581	330	0.6016	0.7264	0.6581	106	0.8938	0.9400	0.9163	0.9954
0.0011	10.0	34970	0.0324	0.8141	0.8944	0.8523	142	0.7524	0.8778	0.8103	90	0.9680	0.9815	0.9747	648	0.9494	0.9667	0.9580	330	0.5878	0.7264	0.6498	106	0.8939	0.9407	0.9167	0.9954

Framework versions

Transformers 4.28.0.dev0
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: bert
License: mit
Created: Apr 12, 2023
Updated: Apr 12, 2023