Hugging Face's logo

swarnava112
/
LayoutLMV2-Standard-Tune

Document Question Answering
Transformers
TensorBoard
Safetensors
layoutlmv2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
swarnava112 commited on
Commit
8810f71
•
1 Parent(s): c52a563

Model save

Browse files
Files changed (1) hide show
  1. README.md +147 -0
README.md ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: cc-by-nc-sa-4.0
4
+ base_model: microsoft/layoutlmv2-base-uncased
5
+ tags:
6
+ - generated_from_trainer
7
+ model-index:
8
+ - name: LayoutLMV2-Standard-Tune
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # LayoutLMV2-Standard-Tune
16
+
17
+ This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 4.8908
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 5e-05
39
+ - train_batch_size: 4
40
+ - eval_batch_size: 8
41
+ - seed: 42
42
+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
43
+ - lr_scheduler_type: linear
44
+ - num_epochs: 20
45
+
46
+ ### Training results
47
+
48
+ | Training Loss | Epoch | Step | Validation Loss |
49
+ |:-------------:|:-------:|:----:|:---------------:|
50
+ | 5.2839 | 0.2212 | 50 | 4.7945 |
51
+ | 4.5829 | 0.4425 | 100 | 4.1757 |
52
+ | 4.1957 | 0.6637 | 150 | 4.0915 |
53
+ | 3.9136 | 0.8850 | 200 | 3.7030 |
54
+ | 3.4744 | 1.1062 | 250 | 3.5116 |
55
+ | 3.2748 | 1.3274 | 300 | 3.1921 |
56
+ | 3.067 | 1.5487 | 350 | 2.9631 |
57
+ | 2.7681 | 1.7699 | 400 | 2.6921 |
58
+ | 2.3719 | 1.9912 | 450 | 2.8024 |
59
+ | 2.1407 | 2.2124 | 500 | 2.6848 |
60
+ | 1.8237 | 2.4336 | 550 | 2.3111 |
61
+ | 1.8715 | 2.6549 | 600 | 2.2330 |
62
+ | 1.7399 | 2.8761 | 650 | 2.3720 |
63
+ | 1.7406 | 3.0973 | 700 | 2.8147 |
64
+ | 1.4482 | 3.3186 | 750 | 2.5001 |
65
+ | 1.4329 | 3.5398 | 800 | 2.5033 |
66
+ | 1.5602 | 3.7611 | 850 | 2.4586 |
67
+ | 1.293 | 3.9823 | 900 | 2.7511 |
68
+ | 1.0454 | 4.2035 | 950 | 3.0238 |
69
+ | 1.0479 | 4.4248 | 1000 | 2.5079 |
70
+ | 0.9167 | 4.6460 | 1050 | 2.6259 |
71
+ | 0.9181 | 4.8673 | 1100 | 2.8871 |
72
+ | 0.8904 | 5.0885 | 1150 | 2.4504 |
73
+ | 0.7538 | 5.3097 | 1200 | 2.9350 |
74
+ | 0.8497 | 5.5310 | 1250 | 3.0230 |
75
+ | 0.6692 | 5.7522 | 1300 | 3.2195 |
76
+ | 0.8399 | 5.9735 | 1350 | 2.9667 |
77
+ | 0.5473 | 6.1947 | 1400 | 3.1973 |
78
+ | 0.8275 | 6.4159 | 1450 | 3.2960 |
79
+ | 0.5785 | 6.6372 | 1500 | 3.0990 |
80
+ | 0.5653 | 6.8584 | 1550 | 3.3700 |
81
+ | 0.5588 | 7.0796 | 1600 | 3.0558 |
82
+ | 0.4161 | 7.3009 | 1650 | 3.5987 |
83
+ | 0.2991 | 7.5221 | 1700 | 3.7233 |
84
+ | 0.5851 | 7.7434 | 1750 | 3.5847 |
85
+ | 0.4491 | 7.9646 | 1800 | 3.7572 |
86
+ | 0.3945 | 8.1858 | 1850 | 3.4518 |
87
+ | 0.2604 | 8.4071 | 1900 | 3.6431 |
88
+ | 0.3501 | 8.6283 | 1950 | 3.6098 |
89
+ | 0.3894 | 8.8496 | 2000 | 3.9602 |
90
+ | 0.4027 | 9.0708 | 2050 | 3.9866 |
91
+ | 0.297 | 9.2920 | 2100 | 4.1976 |
92
+ | 0.4525 | 9.5133 | 2150 | 4.4386 |
93
+ | 0.4868 | 9.7345 | 2200 | 3.5151 |
94
+ | 0.2205 | 9.9558 | 2250 | 4.2178 |
95
+ | 0.2727 | 10.1770 | 2300 | 4.1939 |
96
+ | 0.161 | 10.3982 | 2350 | 4.2756 |
97
+ | 0.2455 | 10.6195 | 2400 | 4.5170 |
98
+ | 0.4042 | 10.8407 | 2450 | 3.9808 |
99
+ | 0.1274 | 11.0619 | 2500 | 4.2683 |
100
+ | 0.1188 | 11.2832 | 2550 | 4.1454 |
101
+ | 0.3412 | 11.5044 | 2600 | 4.1659 |
102
+ | 0.1803 | 11.7257 | 2650 | 3.9312 |
103
+ | 0.1964 | 11.9469 | 2700 | 3.7040 |
104
+ | 0.1959 | 12.1681 | 2750 | 3.9490 |
105
+ | 0.1107 | 12.3894 | 2800 | 3.9846 |
106
+ | 0.1651 | 12.6106 | 2850 | 4.0311 |
107
+ | 0.2005 | 12.8319 | 2900 | 4.0973 |
108
+ | 0.2648 | 13.0531 | 2950 | 4.5676 |
109
+ | 0.0985 | 13.2743 | 3000 | 4.0938 |
110
+ | 0.1042 | 13.4956 | 3050 | 4.1858 |
111
+ | 0.1192 | 13.7168 | 3100 | 4.5617 |
112
+ | 0.1114 | 13.9381 | 3150 | 4.1155 |
113
+ | 0.1091 | 14.1593 | 3200 | 4.5171 |
114
+ | 0.1307 | 14.3805 | 3250 | 4.7358 |
115
+ | 0.1432 | 14.6018 | 3300 | 4.7484 |
116
+ | 0.1439 | 14.8230 | 3350 | 4.4776 |
117
+ | 0.0857 | 15.0442 | 3400 | 4.6668 |
118
+ | 0.0127 | 15.2655 | 3450 | 4.7343 |
119
+ | 0.0364 | 15.4867 | 3500 | 4.6299 |
120
+ | 0.1207 | 15.7080 | 3550 | 4.7548 |
121
+ | 0.1539 | 15.9292 | 3600 | 4.6832 |
122
+ | 0.0515 | 16.1504 | 3650 | 4.8701 |
123
+ | 0.0291 | 16.3717 | 3700 | 5.0909 |
124
+ | 0.0385 | 16.5929 | 3750 | 4.9299 |
125
+ | 0.0726 | 16.8142 | 3800 | 4.7428 |
126
+ | 0.1781 | 17.0354 | 3850 | 4.8832 |
127
+ | 0.0068 | 17.2566 | 3900 | 5.0250 |
128
+ | 0.1302 | 17.4779 | 3950 | 4.6736 |
129
+ | 0.0528 | 17.6991 | 4000 | 4.6847 |
130
+ | 0.0765 | 17.9204 | 4050 | 4.5936 |
131
+ | 0.071 | 18.1416 | 4100 | 4.8151 |
132
+ | 0.0651 | 18.3628 | 4150 | 4.8133 |
133
+ | 0.0066 | 18.5841 | 4200 | 4.8225 |
134
+ | 0.0294 | 18.8053 | 4250 | 4.8895 |
135
+ | 0.0808 | 19.0265 | 4300 | 4.8649 |
136
+ | 0.085 | 19.2478 | 4350 | 4.8763 |
137
+ | 0.0352 | 19.4690 | 4400 | 4.8788 |
138
+ | 0.1208 | 19.6903 | 4450 | 4.8931 |
139
+ | 0.0804 | 19.9115 | 4500 | 4.8908 |
140
+
141
+
142
+ ### Framework versions
143
+
144
+ - Transformers 4.47.1
145
+ - Pytorch 2.5.1+cu121
146
+ - Datasets 3.2.0
147
+ - Tokenizers 0.21.0