Hugging Face's logo

adityaprakhar
/
test

Token Classification
Transformers
TensorBoard
Safetensors
lilt
Generated from Trainer
Model card Files
xet
Metrics Community
adityaprakhar commited on
Commit
585d75b
·
verified ·
1 Parent(s): 6b98d2f

End of training

Browse files
Files changed (1) hide show
  1. README.md +14 -14
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 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
- - Loss: 0.3848
24
- - Precision: 0.8205
25
  - Recall: 0.8649
26
- - F1: 0.8421
27
- - Accuracy: 0.8505
28
 
29
  ## Model description
30
 
@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
58
- | No log | 3.85 | 50 | 0.5998 | 0.7848 | 0.8378 | 0.8105 | 0.8224 |
59
- | No log | 7.69 | 100 | 0.4655 | 0.8 | 0.8649 | 0.8312 | 0.8505 |
60
- | No log | 11.54 | 150 | 0.4716 | 0.7949 | 0.8378 | 0.8158 | 0.8318 |
61
- | No log | 15.38 | 200 | 0.4252 | 0.8354 | 0.8919 | 0.8627 | 0.8598 |
62
- | No log | 19.23 | 250 | 0.4485 | 0.8 | 0.8649 | 0.8312 | 0.8411 |
63
- | No log | 23.08 | 300 | 0.4121 | 0.7875 | 0.8514 | 0.8182 | 0.8318 |
64
- | No log | 26.92 | 350 | 0.3892 | 0.8205 | 0.8649 | 0.8421 | 0.8505 |
65
- | No log | 30.77 | 400 | 0.4003 | 0.8205 | 0.8649 | 0.8421 | 0.8505 |
66
- | No log | 34.62 | 450 | 0.3827 | 0.8462 | 0.8919 | 0.8684 | 0.8785 |
67
- | 0.1768 | 38.46 | 500 | 0.3848 | 0.8205 | 0.8649 | 0.8421 | 0.8505 |
68
 
69
 
70
  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.4675
24
+ - Precision: 0.8
25
  - Recall: 0.8649
26
+ - F1: 0.8312
27
+ - Accuracy: 0.8318
28
 
29
  ## Model description
30
 
 
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
58
+ | No log | 3.85 | 50 | 1.1808 | 0.7013 | 0.7297 | 0.7152 | 0.7196 |
59
+ | No log | 7.69 | 100 | 0.7117 | 0.7317 | 0.8108 | 0.7692 | 0.8037 |
60
+ | No log | 11.54 | 150 | 0.5580 | 0.7778 | 0.8514 | 0.8129 | 0.8224 |
61
+ | No log | 15.38 | 200 | 0.5009 | 0.8228 | 0.8784 | 0.8497 | 0.8411 |
62
+ | No log | 19.23 | 250 | 0.4659 | 0.8228 | 0.8784 | 0.8497 | 0.8505 |
63
+ | No log | 23.08 | 300 | 0.4734 | 0.7901 | 0.8649 | 0.8258 | 0.8318 |
64
+ | No log | 26.92 | 350 | 0.4496 | 0.8205 | 0.8649 | 0.8421 | 0.8318 |
65
+ | No log | 30.77 | 400 | 0.4619 | 0.8 | 0.8649 | 0.8312 | 0.8318 |
66
+ | No log | 34.62 | 450 | 0.4560 | 0.8125 | 0.8784 | 0.8442 | 0.8411 |
67
+ | 0.3885 | 38.46 | 500 | 0.4675 | 0.8 | 0.8649 | 0.8312 | 0.8318 |
68
 
69
 
70
  ### Framework versions