Hugging Face's logo

jinhybr
/
OCR-LayoutLMv3

Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
Metrics Community
2
jinhybr commited on
Commit
ed7990f
·
1 Parent(s): e7d32d3

End of training

Browse files
Files changed (3) hide show
  1. README.md +108 -0
  2. logs/events.out.tfevents.1667612186.158-101-127-87.64910.0 +2 -2
  3. pytorch_model.bin +1 -1
README.md ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-sa-4.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - funsd-layoutlmv3
7
+ metrics:
8
+ - precision
9
+ - recall
10
+ - f1
11
+ - accuracy
12
+ model-index:
13
+ - name: OCR-LayoutLMv3
14
+ results:
15
+ - task:
16
+ name: Token Classification
17
+ type: token-classification
18
+ dataset:
19
+ name: funsd-layoutlmv3
20
+ type: funsd-layoutlmv3
21
+ config: funsd
22
+ split: train
23
+ args: funsd
24
+ metrics:
25
+ - name: Precision
26
+ type: precision
27
+ value: 0.8988653182042428
28
+ - name: Recall
29
+ type: recall
30
+ value: 0.905116741182315
31
+ - name: F1
32
+ type: f1
33
+ value: 0.9019801980198019
34
+ - name: Accuracy
35
+ type: accuracy
36
+ value: 0.8403661000832046
37
+ ---
38
+
39
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
40
+ should probably proofread and complete it, then remove this comment. -->
41
+
42
+ # OCR-LayoutLMv3
43
+
44
+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset.
45
+ It achieves the following results on the evaluation set:
46
+ - Loss: 0.9788
47
+ - Precision: 0.8989
48
+ - Recall: 0.9051
49
+ - F1: 0.9020
50
+ - Accuracy: 0.8404
51
+
52
+ ## Model description
53
+
54
+ More information needed
55
+
56
+ ## Intended uses & limitations
57
+
58
+ More information needed
59
+
60
+ ## Training and evaluation data
61
+
62
+ More information needed
63
+
64
+ ## Training procedure
65
+
66
+ ### Training hyperparameters
67
+
68
+ The following hyperparameters were used during training:
69
+ - learning_rate: 1e-05
70
+ - train_batch_size: 2
71
+ - eval_batch_size: 2
72
+ - seed: 42
73
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
74
+ - lr_scheduler_type: linear
75
+ - training_steps: 2000
76
+
77
+ ### Training results
78
+
79
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
80
+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
81
+ | No log | 1.33 | 100 | 0.6966 | 0.7418 | 0.8063 | 0.7727 | 0.7801 |
82
+ | No log | 2.67 | 200 | 0.5767 | 0.8104 | 0.8644 | 0.8365 | 0.8117 |
83
+ | No log | 4.0 | 300 | 0.5355 | 0.8246 | 0.8852 | 0.8539 | 0.8295 |
84
+ | No log | 5.33 | 400 | 0.5240 | 0.8706 | 0.8922 | 0.8813 | 0.8427 |
85
+ | 0.5326 | 6.67 | 500 | 0.6337 | 0.8528 | 0.8778 | 0.8651 | 0.8260 |
86
+ | 0.5326 | 8.0 | 600 | 0.6870 | 0.8698 | 0.8828 | 0.8762 | 0.8240 |
87
+ | 0.5326 | 9.33 | 700 | 0.6584 | 0.8723 | 0.9061 | 0.8889 | 0.8342 |
88
+ | 0.5326 | 10.67 | 800 | 0.7186 | 0.8868 | 0.9031 | 0.8949 | 0.8335 |
89
+ | 0.5326 | 12.0 | 900 | 0.6822 | 0.9040 | 0.9076 | 0.9058 | 0.8526 |
90
+ | 0.1248 | 13.33 | 1000 | 0.7042 | 0.8872 | 0.9021 | 0.8946 | 0.8511 |
91
+ | 0.1248 | 14.67 | 1100 | 0.7920 | 0.9027 | 0.9036 | 0.9032 | 0.8480 |
92
+ | 0.1248 | 16.0 | 1200 | 0.8052 | 0.8964 | 0.9151 | 0.9056 | 0.8389 |
93
+ | 0.1248 | 17.33 | 1300 | 0.8932 | 0.8995 | 0.9066 | 0.9030 | 0.8329 |
94
+ | 0.1248 | 18.67 | 1400 | 0.8728 | 0.8950 | 0.9061 | 0.9005 | 0.8398 |
95
+ | 0.0442 | 20.0 | 1500 | 0.9051 | 0.8960 | 0.9116 | 0.9037 | 0.8347 |
96
+ | 0.0442 | 21.33 | 1600 | 0.9587 | 0.8947 | 0.9031 | 0.8989 | 0.8401 |
97
+ | 0.0442 | 22.67 | 1700 | 0.9822 | 0.9042 | 0.9046 | 0.9044 | 0.8389 |
98
+ | 0.0442 | 24.0 | 1800 | 0.9734 | 0.9043 | 0.9061 | 0.9052 | 0.8391 |
99
+ | 0.0442 | 25.33 | 1900 | 0.9842 | 0.9042 | 0.9091 | 0.9066 | 0.8410 |
100
+ | 0.0225 | 26.67 | 2000 | 0.9788 | 0.8989 | 0.9051 | 0.9020 | 0.8404 |
101
+
102
+
103
+ ### Framework versions
104
+
105
+ - Transformers 4.25.0.dev0
106
+ - Pytorch 1.12.1
107
+ - Datasets 2.6.1
108
+ - Tokenizers 0.13.1
logs/events.out.tfevents.1667612186.158-101-127-87.64910.0 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:41a460189ffb28165e3b7bb4d803c0fd2d1d4d9120ed1ae90a1c2d7e057c4793
3
- size 14377
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:59c922113b0fb77086e9d9cc51af0076802de7a5884ac6b487fbca8b5e4454a5
3
+ size 14731
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:a0a566fe727cb9d89c6f89a7d4665b0af2c98f8dd7afeccc89da6dd9039e390c
3
  size 501405907
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7ee11a73887f19f1c2fbc75bdc3bd76d15060adca7f175aefe4a76b67fc453b9
3
  size 501405907