Hugging Face's logo

farizkuy
/
ner_fine_tuned

Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
farizkuy commited on
Commit
349231c
•
1 Parent(s): e1532ab

Training complete

Browse files
Files changed (1) hide show
  1. README.md +21 -9
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
20
 
21
  This model is a fine-tuned version of [cahya/bert-base-indonesian-NER](https://huggingface.co/cahya/bert-base-indonesian-NER) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
- - Loss: 0.6204
24
- - Precision: 0.7857
25
- - Recall: 0.5116
26
- - F1: 0.6197
27
- - Accuracy: 0.9038
28
 
29
  ## Model description
30
 
@@ -49,15 +49,27 @@ The following hyperparameters were used during training:
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
52
- - num_epochs: 3
53
 
54
  ### Training results
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
58
- | No log | 1.0 | 8 | 0.5715 | 0.6 | 0.4186 | 0.4932 | 0.8763 |
59
- | No log | 2.0 | 16 | 0.6157 | 0.7308 | 0.4419 | 0.5507 | 0.8935 |
60
- | No log | 3.0 | 24 | 0.6204 | 0.7857 | 0.5116 | 0.6197 | 0.9038 |
 
 
 
 
 
 
 
 
 
 
 
 
61
 
62
 
63
  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [cahya/bert-base-indonesian-NER](https://huggingface.co/cahya/bert-base-indonesian-NER) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 1.0080
24
+ - Precision: 0.6970
25
+ - Recall: 0.5349
26
+ - F1: 0.6053
27
+ - Accuracy: 0.8900
28
 
29
  ## Model description
30
 
 
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
52
+ - num_epochs: 15
53
 
54
  ### Training results
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
58
+ | No log | 1.0 | 8 | 0.5649 | 0.625 | 0.4651 | 0.5333 | 0.8832 |
59
+ | No log | 2.0 | 16 | 0.6457 | 0.7857 | 0.5116 | 0.6197 | 0.9003 |
60
+ | No log | 3.0 | 24 | 0.7181 | 0.6471 | 0.5116 | 0.5714 | 0.8832 |
61
+ | No log | 4.0 | 32 | 0.8134 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
62
+ | No log | 5.0 | 40 | 0.8528 | 0.6667 | 0.5116 | 0.5789 | 0.8866 |
63
+ | No log | 6.0 | 48 | 0.8893 | 0.6667 | 0.5116 | 0.5789 | 0.8866 |
64
+ | No log | 7.0 | 56 | 0.9148 | 0.6667 | 0.5116 | 0.5789 | 0.8866 |
65
+ | No log | 8.0 | 64 | 0.9440 | 0.6667 | 0.5116 | 0.5789 | 0.8866 |
66
+ | No log | 9.0 | 72 | 0.9744 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
67
+ | No log | 10.0 | 80 | 0.9895 | 0.6765 | 0.5349 | 0.5974 | 0.8900 |
68
+ | No log | 11.0 | 88 | 0.9968 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
69
+ | No log | 12.0 | 96 | 1.0015 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
70
+ | No log | 13.0 | 104 | 1.0049 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
71
+ | No log | 14.0 | 112 | 1.0072 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
72
+ | No log | 15.0 | 120 | 1.0080 | 0.6970 | 0.5349 | 0.6053 | 0.8900 |
73
 
74
 
75
  ### Framework versions