Hugging Face's logo

KellyShiiii
/
bert-finetuned-ner

Token Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
KellyShiiii commited on
Commit
459a4c2
•
1 Parent(s): 7748384

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +25 -25
README.md CHANGED
@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
18
 
19
  This model was trained from scratch on the None dataset.
20
  It achieves the following results on the evaluation set:
21
- - Loss: 0.7451
22
- - Precision: 0.4163
23
- - Recall: 0.3481
24
- - F1: 0.3791
25
- - Accuracy: 0.9057
26
 
27
  ## Model description
28
 
@@ -53,26 +53,26 @@ The following hyperparameters were used during training:
53
 
54
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
55
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
56
- | No log | 1.0 | 125 | 0.4506 | 0.2801 | 0.1713 | 0.2126 | 0.8852 |
57
- | No log | 2.0 | 250 | 0.4257 | 0.3293 | 0.3039 | 0.3161 | 0.8992 |
58
- | No log | 3.0 | 375 | 0.4821 | 0.3899 | 0.3260 | 0.3551 | 0.9059 |
59
- | 0.1136 | 4.0 | 500 | 0.5208 | 0.3768 | 0.3775 | 0.3772 | 0.9062 |
60
- | 0.1136 | 5.0 | 625 | 0.5434 | 0.3903 | 0.3996 | 0.3949 | 0.9087 |
61
- | 0.1136 | 6.0 | 750 | 0.6386 | 0.4454 | 0.3683 | 0.4032 | 0.9079 |
62
- | 0.1136 | 7.0 | 875 | 0.6286 | 0.4148 | 0.3812 | 0.3973 | 0.9056 |
63
- | 0.0077 | 8.0 | 1000 | 0.6875 | 0.4151 | 0.3241 | 0.3640 | 0.9037 |
64
- | 0.0077 | 9.0 | 1125 | 0.7166 | 0.4161 | 0.3241 | 0.3644 | 0.9039 |
65
- | 0.0077 | 10.0 | 1250 | 0.7340 | 0.4118 | 0.3223 | 0.3616 | 0.9061 |
66
- | 0.0077 | 11.0 | 1375 | 0.6873 | 0.4161 | 0.3517 | 0.3812 | 0.9075 |
67
- | 0.0015 | 12.0 | 1500 | 0.7451 | 0.4195 | 0.3168 | 0.3610 | 0.9028 |
68
- | 0.0015 | 13.0 | 1625 | 0.7303 | 0.4179 | 0.3517 | 0.3820 | 0.9068 |
69
- | 0.0015 | 14.0 | 1750 | 0.7652 | 0.3968 | 0.3186 | 0.3534 | 0.9047 |
70
- | 0.0015 | 15.0 | 1875 | 0.7891 | 0.4140 | 0.3149 | 0.3577 | 0.9036 |
71
- | 0.0006 | 16.0 | 2000 | 0.7651 | 0.4209 | 0.3333 | 0.3720 | 0.9049 |
72
- | 0.0006 | 17.0 | 2125 | 0.7746 | 0.4 | 0.3168 | 0.3535 | 0.9046 |
73
- | 0.0006 | 18.0 | 2250 | 0.7457 | 0.4169 | 0.3462 | 0.3783 | 0.9053 |
74
- | 0.0006 | 19.0 | 2375 | 0.7487 | 0.4178 | 0.3462 | 0.3787 | 0.9058 |
75
- | 0.0004 | 20.0 | 2500 | 0.7451 | 0.4163 | 0.3481 | 0.3791 | 0.9057 |
76
 
77
 
78
  ### Framework versions
 
18
 
19
  This model was trained from scratch on the None dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 0.6370
22
+ - Precision: 0.5313
23
+ - Recall: 0.4530
24
+ - F1: 0.4891
25
+ - Accuracy: 0.9290
26
 
27
  ## Model description
28
 
 
53
 
54
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
55
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
56
+ | No log | 1.0 | 125 | 0.5387 | 0.2190 | 0.0552 | 0.0882 | 0.8991 |
57
+ | No log | 2.0 | 250 | 0.4241 | 0.3430 | 0.1750 | 0.2317 | 0.9117 |
58
+ | No log | 3.0 | 375 | 0.4721 | 0.3502 | 0.1786 | 0.2366 | 0.9088 |
59
+ | 0.1529 | 4.0 | 500 | 0.6204 | 0.4300 | 0.2320 | 0.3014 | 0.9134 |
60
+ | 0.1529 | 5.0 | 625 | 0.6479 | 0.4470 | 0.2486 | 0.3195 | 0.9104 |
61
+ | 0.1529 | 6.0 | 750 | 0.4640 | 0.4532 | 0.4015 | 0.4258 | 0.9220 |
62
+ | 0.1529 | 7.0 | 875 | 0.5170 | 0.4288 | 0.4217 | 0.4253 | 0.9224 |
63
+ | 0.0229 | 8.0 | 1000 | 0.5846 | 0.5524 | 0.4273 | 0.4818 | 0.9233 |
64
+ | 0.0229 | 9.0 | 1125 | 0.5569 | 0.4644 | 0.4328 | 0.4480 | 0.9234 |
65
+ | 0.0229 | 10.0 | 1250 | 0.5818 | 0.5502 | 0.4438 | 0.4913 | 0.9258 |
66
+ | 0.0229 | 11.0 | 1375 | 0.6183 | 0.5607 | 0.4254 | 0.4838 | 0.9231 |
67
+ | 0.0048 | 12.0 | 1500 | 0.6148 | 0.5385 | 0.4254 | 0.4753 | 0.9250 |
68
+ | 0.0048 | 13.0 | 1625 | 0.6271 | 0.4896 | 0.4328 | 0.4594 | 0.9255 |
69
+ | 0.0048 | 14.0 | 1750 | 0.6475 | 0.5668 | 0.4217 | 0.4836 | 0.9267 |
70
+ | 0.0048 | 15.0 | 1875 | 0.6428 | 0.5704 | 0.4328 | 0.4921 | 0.9282 |
71
+ | 0.0016 | 16.0 | 2000 | 0.6577 | 0.5487 | 0.4254 | 0.4793 | 0.9270 |
72
+ | 0.0016 | 17.0 | 2125 | 0.6688 | 0.5556 | 0.4144 | 0.4747 | 0.9262 |
73
+ | 0.0016 | 18.0 | 2250 | 0.6481 | 0.5434 | 0.4383 | 0.4852 | 0.9282 |
74
+ | 0.0016 | 19.0 | 2375 | 0.6432 | 0.5428 | 0.4438 | 0.4883 | 0.9289 |
75
+ | 0.0007 | 20.0 | 2500 | 0.6370 | 0.5313 | 0.4530 | 0.4891 | 0.9290 |
76
 
77
 
78
  ### Framework versions