Hugging Face's logo

Kriyans
/
Bert-NER

Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
28
Kriyans commited on
Commit
7fcf2eb
•
1 Parent(s): 79efae4

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +61 -16
README.md CHANGED
@@ -24,16 +24,16 @@ model-index:
24
  metrics:
25
  - name: Precision
26
  type: precision
27
- value: 0.9999764528586229
28
  - name: Recall
29
  type: recall
30
- value: 0.9999764528586229
31
  - name: F1
32
  type: f1
33
- value: 0.9999764528586229
34
  - name: Accuracy
35
  type: accuracy
36
- value: 0.9999874989061542
37
  ---
38
 
39
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,10 +44,10 @@ should probably proofread and complete it, then remove this comment. -->
44
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset.
45
  It achieves the following results on the evaluation set:
46
  - Loss: 0.0000
47
- - Precision: 1.0000
48
- - Recall: 1.0000
49
- - F1: 1.0000
50
- - Accuracy: 1.0000
51
 
52
  ## Model description
53
 
@@ -72,17 +72,62 @@ The following hyperparameters were used during training:
72
  - seed: 42
73
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
74
  - lr_scheduler_type: linear
75
- - num_epochs: 5
76
 
77
  ### Training results
78
 
79
- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
80
- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
81
- | No log | 1.0 | 344 | 0.0003 | 1.0000 | 0.9999 | 1.0000 | 1.0000 |
82
- | 0.0318 | 2.0 | 688 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
83
- | 0.0049 | 3.0 | 1032 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
84
- | 0.0049 | 4.0 | 1376 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
85
- | 0.0012 | 5.0 | 1720 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
86
 
87
 
88
  ### Framework versions
 
24
  metrics:
25
  - name: Precision
26
  type: precision
27
+ value: 1.0
28
  - name: Recall
29
  type: recall
30
+ value: 1.0
31
  - name: F1
32
  type: f1
33
+ value: 1.0
34
  - name: Accuracy
35
  type: accuracy
36
+ value: 1.0
37
  ---
38
 
39
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
44
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset.
45
  It achieves the following results on the evaluation set:
46
  - Loss: 0.0000
47
+ - Precision: 1.0
48
+ - Recall: 1.0
49
+ - F1: 1.0
50
+ - Accuracy: 1.0
51
 
52
  ## Model description
53
 
 
72
  - seed: 42
73
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
74
  - lr_scheduler_type: linear
75
+ - num_epochs: 50
76
 
77
  ### Training results
78
 
79
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
80
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
81
+ | No log | 1.0 | 344 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
82
+ | 0.0027 | 2.0 | 688 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
83
+ | 0.0019 | 3.0 | 1032 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
84
+ | 0.0019 | 4.0 | 1376 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
85
+ | 0.0021 | 5.0 | 1720 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
86
+ | 0.0016 | 6.0 | 2064 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
87
+ | 0.0016 | 7.0 | 2408 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
88
+ | 0.0007 | 8.0 | 2752 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
89
+ | 0.001 | 9.0 | 3096 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
90
+ | 0.001 | 10.0 | 3440 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
91
+ | 0.001 | 11.0 | 3784 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
92
+ | 0.0008 | 12.0 | 4128 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
93
+ | 0.0008 | 13.0 | 4472 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
94
+ | 0.0007 | 14.0 | 4816 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
95
+ | 0.0009 | 15.0 | 5160 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
96
+ | 0.0006 | 16.0 | 5504 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
97
+ | 0.0006 | 17.0 | 5848 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
98
+ | 0.0003 | 18.0 | 6192 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
99
+ | 0.0006 | 19.0 | 6536 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
100
+ | 0.0006 | 20.0 | 6880 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
101
+ | 0.0007 | 21.0 | 7224 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
102
+ | 0.0007 | 22.0 | 7568 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
103
+ | 0.0007 | 23.0 | 7912 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
104
+ | 0.0005 | 24.0 | 8256 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
105
+ | 0.0001 | 25.0 | 8600 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
106
+ | 0.0001 | 26.0 | 8944 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
107
+ | 0.0002 | 27.0 | 9288 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
108
+ | 0.0003 | 28.0 | 9632 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
109
+ | 0.0003 | 29.0 | 9976 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
110
+ | 0.0001 | 30.0 | 10320 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
111
+ | 0.0 | 31.0 | 10664 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
112
+ | 0.0001 | 32.0 | 11008 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
113
+ | 0.0001 | 33.0 | 11352 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
114
+ | 0.0001 | 34.0 | 11696 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
115
+ | 0.0003 | 35.0 | 12040 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
116
+ | 0.0003 | 36.0 | 12384 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
117
+ | 0.0001 | 37.0 | 12728 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
118
+ | 0.0001 | 38.0 | 13072 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
119
+ | 0.0001 | 39.0 | 13416 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
120
+ | 0.0002 | 40.0 | 13760 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
121
+ | 0.0 | 41.0 | 14104 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
122
+ | 0.0 | 42.0 | 14448 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
123
+ | 0.0 | 43.0 | 14792 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
124
+ | 0.0 | 44.0 | 15136 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
125
+ | 0.0 | 45.0 | 15480 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
126
+ | 0.0 | 46.0 | 15824 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
127
+ | 0.0001 | 47.0 | 16168 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
128
+ | 0.0 | 48.0 | 16512 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
129
+ | 0.0 | 49.0 | 16856 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
130
+ | 0.0 | 50.0 | 17200 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
131
 
132
 
133
  ### Framework versions