Hugging Face's logo

wsqstar
/
bert-finetuned-weibo-luobokuaipao

Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Model card Files
xet
 Metrics Community
1
wsqstar commited on
Commit
9df0c2d
·
verified ·
1 Parent(s): b7f5f70

End of training

Browse files
Files changed (1) hide show
  1. README.md +23 -13
README.md CHANGED
@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
16
 
17
  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 2.5350
20
- - Accuracy: 0.6297
21
 
22
  ## Model description
23
 
@@ -42,22 +42,32 @@ The following hyperparameters were used during training:
42
  - seed: 42
43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
44
  - lr_scheduler_type: linear
45
- - num_epochs: 10
46
 
47
  ### Training results
48
 
49
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
50
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
51
- | No log | 1.0 | 198 | 0.8916 | 0.6045 |
52
- | No log | 2.0 | 396 | 0.8631 | 0.6096 |
53
- | 0.8202 | 3.0 | 594 | 1.0508 | 0.6096 |
54
- | 0.8202 | 4.0 | 792 | 1.4061 | 0.5995 |
55
- | 0.8202 | 5.0 | 990 | 1.7345 | 0.6171 |
56
- | 0.3506 | 6.0 | 1188 | 1.8544 | 0.6599 |
57
- | 0.3506 | 7.0 | 1386 | 2.1316 | 0.6045 |
58
- | 0.1208 | 8.0 | 1584 | 2.3656 | 0.6020 |
59
- | 0.1208 | 9.0 | 1782 | 2.5180 | 0.6171 |
60
- | 0.1208 | 10.0 | 1980 | 2.5350 | 0.6297 |
 
 
 
 
 
 
 
 
 
 
61
 
62
 
63
  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 3.2677
20
+ - Accuracy: 0.6348
21
 
22
  ## Model description
23
 
 
42
  - seed: 42
43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
44
  - lr_scheduler_type: linear
45
+ - num_epochs: 20
46
 
47
  ### Training results
48
 
49
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
50
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
51
+ | No log | 1.0 | 198 | 2.5581 | 0.5919 |
52
+ | No log | 2.0 | 396 | 2.2918 | 0.6348 |
53
+ | 0.2014 | 3.0 | 594 | 1.9679 | 0.6348 |
54
+ | 0.2014 | 4.0 | 792 | 2.9800 | 0.6196 |
55
+ | 0.2014 | 5.0 | 990 | 2.6793 | 0.6398 |
56
+ | 0.1375 | 6.0 | 1188 | 2.8340 | 0.6247 |
57
+ | 0.1375 | 7.0 | 1386 | 2.5889 | 0.6247 |
58
+ | 0.1278 | 8.0 | 1584 | 2.3041 | 0.6725 |
59
+ | 0.1278 | 9.0 | 1782 | 2.5275 | 0.6524 |
60
+ | 0.1278 | 10.0 | 1980 | 3.1778 | 0.6171 |
61
+ | 0.0614 | 11.0 | 2178 | 2.8898 | 0.6196 |
62
+ | 0.0614 | 12.0 | 2376 | 2.7480 | 0.6322 |
63
+ | 0.028 | 13.0 | 2574 | 3.0678 | 0.6322 |
64
+ | 0.028 | 14.0 | 2772 | 3.0487 | 0.6448 |
65
+ | 0.028 | 15.0 | 2970 | 3.2878 | 0.6373 |
66
+ | 0.0177 | 16.0 | 3168 | 3.1296 | 0.6373 |
67
+ | 0.0177 | 17.0 | 3366 | 3.2056 | 0.6297 |
68
+ | 0.0193 | 18.0 | 3564 | 3.2349 | 0.6247 |
69
+ | 0.0193 | 19.0 | 3762 | 3.2624 | 0.6247 |
70
+ | 0.0193 | 20.0 | 3960 | 3.2677 | 0.6348 |
71
 
72
 
73
  ### Framework versions