Hugging Face's logo

ricardo-filho
/
bert_base_tcm_teste

Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
ricardo-filho commited on
Commit
c38d3c9
•
1 Parent(s): 7d85bb7

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +32 -33
README.md CHANGED
@@ -14,35 +14,35 @@ should probably proofread and complete it, then remove this comment. -->
14
 
15
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
16
  It achieves the following results on the evaluation set:
17
- - Loss: 0.0205
18
- - Criterio Julgamento Precision: 0.7719
19
- - Criterio Julgamento Recall: 0.8462
20
- - Criterio Julgamento F1: 0.8073
21
  - Criterio Julgamento Number: 104
22
- - Data Sessao Precision: 0.7812
23
- - Data Sessao Recall: 0.9091
24
- - Data Sessao F1: 0.8403
25
  - Data Sessao Number: 55
26
- - Modalidade Licitacao Precision: 0.9507
27
- - Modalidade Licitacao Recall: 0.9620
28
- - Modalidade Licitacao F1: 0.9563
29
  - Modalidade Licitacao Number: 421
30
  - Numero Exercicio Precision: 0.9375
31
  - Numero Exercicio Recall: 0.9730
32
  - Numero Exercicio F1: 0.9549
33
  - Numero Exercicio Number: 185
34
- - Objeto Licitacao Precision: 0.5309
35
- - Objeto Licitacao Recall: 0.7288
36
- - Objeto Licitacao F1: 0.6143
37
  - Objeto Licitacao Number: 59
38
- - Valor Objeto Precision: 0.8409
39
- - Valor Objeto Recall: 0.9024
40
- - Valor Objeto F1: 0.8706
41
  - Valor Objeto Number: 41
42
- - Overall Precision: 0.8719
43
- - Overall Recall: 0.9283
44
- - Overall F1: 0.8992
45
- - Overall Accuracy: 0.9967
46
 
47
  ## Model description
48
 
@@ -61,7 +61,7 @@ More information needed
61
  ### Training hyperparameters
62
 
63
  The following hyperparameters were used during training:
64
- - learning_rate: 1e-05
65
  - train_batch_size: 8
66
  - eval_batch_size: 8
67
  - seed: 42
@@ -73,18 +73,17 @@ The following hyperparameters were used during training:
73
 
74
  | 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 | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
75
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
76
- | 0.0168 | 0.96 | 2750 | 0.0169 | 0.7016 | 0.8365 | 0.7632 | 104 | 0.6707 | 1.0 | 0.8029 | 55 | 0.9424 | 0.9715 | 0.9567 | 421 | 0.9110 | 0.9405 | 0.9255 | 185 | 0.3304 | 0.6271 | 0.4327 | 59 | 0.76 | 0.9268 | 0.8352 | 41 | 0.8056 | 0.9249 | 0.8611 | 0.9950 |
77
- | 0.0164 | 1.92 | 5500 | 0.0125 | 0.7565 | 0.8365 | 0.7945 | 104 | 0.6923 | 0.9818 | 0.8120 | 55 | 0.9491 | 0.9739 | 0.9613 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.4138 | 0.6102 | 0.4932 | 59 | 0.8085 | 0.9268 | 0.8636 | 41 | 0.8465 | 0.9306 | 0.8866 | 0.9965 |
78
- | 0.0076 | 2.88 | 8250 | 0.0204 | 0.7184 | 0.7115 | 0.7150 | 104 | 0.8070 | 0.8364 | 0.8214 | 55 | 0.9468 | 0.9715 | 0.9590 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.4783 | 0.5593 | 0.5156 | 59 | 0.7209 | 0.7561 | 0.7381 | 41 | 0.8610 | 0.8948 | 0.8776 | 0.9961 |
79
- | 0.0067 | 3.84 | 11000 | 0.0168 | 0.7589 | 0.8173 | 0.7870 | 104 | 0.8 | 0.8 | 0.8000 | 55 | 0.9487 | 0.9667 | 0.9576 | 421 | 0.9319 | 0.9622 | 0.9468 | 185 | 0.5309 | 0.7288 | 0.6143 | 59 | 0.8636 | 0.9268 | 0.8941 | 41 | 0.8717 | 0.9191 | 0.8948 | 0.9965 |
80
- | 0.0043 | 4.8 | 13750 | 0.0144 | 0.736 | 0.8846 | 0.8035 | 104 | 0.8033 | 0.8909 | 0.8448 | 55 | 0.9512 | 0.9715 | 0.9612 | 421 | 0.9316 | 0.9568 | 0.944 | 185 | 0.5135 | 0.6441 | 0.5714 | 59 | 0.8444 | 0.9268 | 0.8837 | 41 | 0.8681 | 0.9283 | 0.8972 | 0.9967 |
81
- | 0.0072 | 5.76 | 16500 | 0.0161 | 0.8091 | 0.8558 | 0.8318 | 104 | 0.7237 | 1.0 | 0.8397 | 55 | 0.9487 | 0.9667 | 0.9576 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4318 | 0.6441 | 0.5170 | 59 | 0.8222 | 0.9024 | 0.8605 | 41 | 0.8565 | 0.9318 | 0.8926 | 0.9966 |
82
- | 0.003 | 6.72 | 19250 | 0.0205 | 0.7719 | 0.8462 | 0.8073 | 104 | 0.7812 | 0.9091 | 0.8403 | 55 | 0.9507 | 0.9620 | 0.9563 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.5309 | 0.7288 | 0.6143 | 59 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8719 | 0.9283 | 0.8992 | 0.9967 |
83
- | 0.0033 | 7.68 | 22000 | 0.0197 | 0.7736 | 0.7885 | 0.7810 | 104 | 0.7463 | 0.9091 | 0.8197 | 55 | 0.9466 | 0.9691 | 0.9577 | 421 | 0.9227 | 0.9676 | 0.9446 | 185 | 0.5286 | 0.6271 | 0.5736 | 59 | 0.7442 | 0.7805 | 0.7619 | 41 | 0.8650 | 0.9110 | 0.8874 | 0.9964 |
84
- | 0.0043 | 8.64 | 24750 | 0.0250 | 0.7607 | 0.8558 | 0.8054 | 104 | 0.7612 | 0.9273 | 0.8361 | 55 | 0.9400 | 0.9667 | 0.9532 | 421 | 0.9427 | 0.9784 | 0.9602 | 185 | 0.5479 | 0.6780 | 0.6061 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8675 | 0.9306 | 0.8979 | 0.9965 |
85
- | 0.0014 | 9.61 | 27500 | 0.0257 | 0.8018 | 0.8558 | 0.8279 | 104 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9417 | 0.9596 | 0.9506 | 421 | 0.9372 | 0.9676 | 0.9521 | 185 | 0.5143 | 0.6102 | 0.5581 | 59 | 0.8 | 0.8780 | 0.8372 | 41 | 0.8689 | 0.9191 | 0.8933 | 0.9966 |
86
- | 0.0025 | 10.57 | 30250 | 0.0258 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.7424 | 0.8909 | 0.8099 | 55 | 0.9465 | 0.9667 | 0.9565 | 421 | 0.9424 | 0.9730 | 0.9574 | 185 | 0.5352 | 0.6441 | 0.5846 | 59 | 0.8222 | 0.9024 | 0.8605 | 41 | 0.8728 | 0.9202 | 0.8959 | 0.9963 |
87
- | 0.0016 | 11.53 | 33000 | 0.0273 | 0.7925 | 0.8077 | 0.8000 | 104 | 0.7246 | 0.9091 | 0.8065 | 55 | 0.9485 | 0.9620 | 0.9552 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.56 | 0.7119 | 0.6269 | 59 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8723 | 0.9237 | 0.8972 | 0.9964 |
88
 
89
 
90
  ### Framework versions
 
14
 
15
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
16
  It achieves the following results on the evaluation set:
17
+ - Loss: 0.0225
18
+ - Criterio Julgamento Precision: 0.7798
19
+ - Criterio Julgamento Recall: 0.8173
20
+ - Criterio Julgamento F1: 0.7981
21
  - Criterio Julgamento Number: 104
22
+ - Data Sessao Precision: 0.6912
23
+ - Data Sessao Recall: 0.8545
24
+ - Data Sessao F1: 0.7642
25
  - Data Sessao Number: 55
26
+ - Modalidade Licitacao Precision: 0.9508
27
+ - Modalidade Licitacao Recall: 0.9644
28
+ - Modalidade Licitacao F1: 0.9575
29
  - Modalidade Licitacao Number: 421
30
  - Numero Exercicio Precision: 0.9375
31
  - Numero Exercicio Recall: 0.9730
32
  - Numero Exercicio F1: 0.9549
33
  - Numero Exercicio Number: 185
34
+ - Objeto Licitacao Precision: 0.5395
35
+ - Objeto Licitacao Recall: 0.6949
36
+ - Objeto Licitacao F1: 0.6074
37
  - Objeto Licitacao Number: 59
38
+ - Valor Objeto Precision: 0.8478
39
+ - Valor Objeto Recall: 0.9512
40
+ - Valor Objeto F1: 0.8966
41
  - Valor Objeto Number: 41
42
+ - Overall Precision: 0.8693
43
+ - Overall Recall: 0.9225
44
+ - Overall F1: 0.8951
45
+ - Overall Accuracy: 0.9964
46
 
47
  ## Model description
48
 
 
61
  ### Training hyperparameters
62
 
63
  The following hyperparameters were used during training:
64
+ - learning_rate: 5e-06
65
  - train_batch_size: 8
66
  - eval_batch_size: 8
67
  - seed: 42
 
73
 
74
  | 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 | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
75
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
76
+ | 0.0193 | 0.96 | 2750 | 0.0190 | 0.7016 | 0.8365 | 0.7632 | 104 | 0.6585 | 0.9818 | 0.7883 | 55 | 0.9446 | 0.9715 | 0.9578 | 421 | 0.9036 | 0.9622 | 0.9319 | 185 | 0.2261 | 0.4407 | 0.2989 | 59 | 0.7 | 0.8537 | 0.7692 | 41 | 0.7882 | 0.9121 | 0.8457 | 0.9946 |
77
+ | 0.0165 | 1.92 | 5500 | 0.0133 | 0.7203 | 0.8173 | 0.7658 | 104 | 0.675 | 0.9818 | 0.8 | 55 | 0.9447 | 0.9739 | 0.9591 | 421 | 0.9430 | 0.9838 | 0.9630 | 185 | 0.4691 | 0.6441 | 0.5429 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8466 | 0.9318 | 0.8872 | 0.9964 |
78
+ | 0.0089 | 2.88 | 8250 | 0.0150 | 0.7636 | 0.8077 | 0.7850 | 104 | 0.7895 | 0.8182 | 0.8036 | 55 | 0.9491 | 0.9739 | 0.9613 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.4444 | 0.6102 | 0.5143 | 59 | 0.8636 | 0.9268 | 0.8941 | 41 | 0.8640 | 0.9179 | 0.8901 | 0.9965 |
79
+ | 0.0066 | 3.84 | 11000 | 0.0150 | 0.7692 | 0.8654 | 0.8145 | 104 | 0.7333 | 0.8 | 0.7652 | 55 | 0.9464 | 0.9644 | 0.9553 | 421 | 0.9278 | 0.9730 | 0.9499 | 185 | 0.5 | 0.6780 | 0.5755 | 59 | 0.7708 | 0.9024 | 0.8315 | 41 | 0.8588 | 0.9214 | 0.8890 | 0.9966 |
80
+ | 0.0055 | 4.8 | 13750 | 0.0176 | 0.75 | 0.8654 | 0.8036 | 104 | 0.7903 | 0.8909 | 0.8376 | 55 | 0.9490 | 0.9715 | 0.9601 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4568 | 0.6271 | 0.5286 | 59 | 0.7872 | 0.9024 | 0.8409 | 41 | 0.8587 | 0.9272 | 0.8916 | 0.9963 |
81
+ | 0.0066 | 5.76 | 16500 | 0.0155 | 0.7965 | 0.8654 | 0.8295 | 104 | 0.7162 | 0.9636 | 0.8217 | 55 | 0.9554 | 0.9667 | 0.9610 | 421 | 0.9323 | 0.9676 | 0.9496 | 185 | 0.5270 | 0.6610 | 0.5865 | 59 | 0.8444 | 0.9268 | 0.8837 | 41 | 0.8723 | 0.9318 | 0.9011 | 0.9966 |
82
+ | 0.0031 | 6.72 | 19250 | 0.0181 | 0.775 | 0.8942 | 0.8304 | 104 | 0.7879 | 0.9455 | 0.8595 | 55 | 0.9533 | 0.9691 | 0.9611 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4875 | 0.6610 | 0.5612 | 59 | 0.8261 | 0.9268 | 0.8736 | 41 | 0.8682 | 0.9364 | 0.9010 | 0.9965 |
83
+ | 0.0066 | 7.68 | 22000 | 0.0192 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.6986 | 0.9273 | 0.7969 | 55 | 0.9353 | 0.9620 | 0.9485 | 421 | 0.8995 | 0.9676 | 0.9323 | 185 | 0.4 | 0.5763 | 0.4722 | 59 | 0.7551 | 0.9024 | 0.8222 | 41 | 0.8344 | 0.9145 | 0.8726 | 0.9961 |
84
+ | 0.0052 | 8.64 | 24750 | 0.0201 | 0.8036 | 0.8654 | 0.8333 | 104 | 0.7869 | 0.8727 | 0.8276 | 55 | 0.9465 | 0.9667 | 0.9565 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.5060 | 0.7119 | 0.5915 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8692 | 0.9295 | 0.8983 | 0.9966 |
85
+ | 0.0015 | 9.61 | 27500 | 0.0202 | 0.7838 | 0.8365 | 0.8093 | 104 | 0.7313 | 0.8909 | 0.8033 | 55 | 0.9482 | 0.9572 | 0.9527 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4865 | 0.6102 | 0.5414 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8646 | 0.9156 | 0.8894 | 0.9966 |
86
+ | 0.0015 | 10.57 | 30250 | 0.0225 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.6912 | 0.8545 | 0.7642 | 55 | 0.9508 | 0.9644 | 0.9575 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.5395 | 0.6949 | 0.6074 | 59 | 0.8478 | 0.9512 | 0.8966 | 41 | 0.8693 | 0.9225 | 0.8951 | 0.9964 |
 
87
 
88
 
89
  ### Framework versions