Hugging Face's logo

PEKETI
/
layoutlmv2-base-uncased_finetuned_docvqa

Document Question Answering
Transformers
TensorBoard
Safetensors
layoutlmv2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
PEKETI commited on
Commit
cb7cda3
•
1 Parent(s): 7ae4db9

End of training

Browse files
Files changed (1) hide show
  1. README.md +146 -0
README.md ADDED
@@ -0,0 +1,146 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-sa-4.0
3
+ base_model: microsoft/layoutlmv2-base-uncased
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: layoutlmv2-base-uncased_finetuned_docvqa
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # layoutlmv2-base-uncased_finetuned_docvqa
15
+
16
+ This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 4.4147
19
+
20
+ ## Model description
21
+
22
+ More information needed
23
+
24
+ ## Intended uses & limitations
25
+
26
+ More information needed
27
+
28
+ ## Training and evaluation data
29
+
30
+ More information needed
31
+
32
+ ## Training procedure
33
+
34
+ ### Training hyperparameters
35
+
36
+ The following hyperparameters were used during training:
37
+ - learning_rate: 5e-05
38
+ - train_batch_size: 4
39
+ - eval_batch_size: 8
40
+ - seed: 42
41
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
+ - lr_scheduler_type: linear
43
+ - num_epochs: 20
44
+
45
+ ### Training results
46
+
47
+ | Training Loss | Epoch | Step | Validation Loss |
48
+ |:-------------:|:-------:|:----:|:---------------:|
49
+ | 5.2445 | 0.2212 | 50 | 4.5117 |
50
+ | 4.4073 | 0.4425 | 100 | 4.2219 |
51
+ | 4.1282 | 0.6637 | 150 | 3.8024 |
52
+ | 3.8337 | 0.8850 | 200 | 3.6136 |
53
+ | 3.5425 | 1.1062 | 250 | 3.4874 |
54
+ | 3.3022 | 1.3274 | 300 | 3.1532 |
55
+ | 3.2627 | 1.5487 | 350 | 3.0497 |
56
+ | 2.9534 | 1.7699 | 400 | 3.0351 |
57
+ | 2.7062 | 1.9912 | 450 | 3.0241 |
58
+ | 2.3171 | 2.2124 | 500 | 2.6357 |
59
+ | 2.0499 | 2.4336 | 550 | 2.3957 |
60
+ | 1.9222 | 2.6549 | 600 | 2.4198 |
61
+ | 1.8248 | 2.8761 | 650 | 2.5430 |
62
+ | 1.5168 | 3.0973 | 700 | 4.2506 |
63
+ | 1.4417 | 3.3186 | 750 | 2.3309 |
64
+ | 1.3993 | 3.5398 | 800 | 2.3422 |
65
+ | 1.3945 | 3.7611 | 850 | 2.0276 |
66
+ | 1.138 | 3.9823 | 900 | 2.3541 |
67
+ | 1.2168 | 4.2035 | 950 | 2.8071 |
68
+ | 1.1358 | 4.4248 | 1000 | 2.6772 |
69
+ | 1.0205 | 4.6460 | 1050 | 2.7978 |
70
+ | 0.9784 | 4.8673 | 1100 | 3.3150 |
71
+ | 1.001 | 5.0885 | 1150 | 2.5239 |
72
+ | 0.8487 | 5.3097 | 1200 | 2.9815 |
73
+ | 1.0126 | 5.5310 | 1250 | 3.2436 |
74
+ | 0.6363 | 5.7522 | 1300 | 3.2784 |
75
+ | 0.9224 | 5.9735 | 1350 | 3.4480 |
76
+ | 0.6946 | 6.1947 | 1400 | 3.1487 |
77
+ | 0.6052 | 6.4159 | 1450 | 3.4397 |
78
+ | 0.5203 | 6.6372 | 1500 | 2.9999 |
79
+ | 0.6589 | 6.8584 | 1550 | 3.2889 |
80
+ | 0.6399 | 7.0796 | 1600 | 3.1920 |
81
+ | 0.4313 | 7.3009 | 1650 | 2.9790 |
82
+ | 0.3867 | 7.5221 | 1700 | 3.4399 |
83
+ | 0.5132 | 7.7434 | 1750 | 3.0626 |
84
+ | 0.4955 | 7.9646 | 1800 | 3.2692 |
85
+ | 0.3658 | 8.1858 | 1850 | 3.4662 |
86
+ | 0.2021 | 8.4071 | 1900 | 3.7119 |
87
+ | 0.394 | 8.6283 | 1950 | 3.5633 |
88
+ | 0.4442 | 8.8496 | 2000 | 3.7246 |
89
+ | 0.3807 | 9.0708 | 2050 | 3.5174 |
90
+ | 0.2692 | 9.2920 | 2100 | 3.8268 |
91
+ | 0.3595 | 9.5133 | 2150 | 3.6366 |
92
+ | 0.3495 | 9.7345 | 2200 | 3.5126 |
93
+ | 0.3814 | 9.9558 | 2250 | 3.4845 |
94
+ | 0.2319 | 10.1770 | 2300 | 3.5154 |
95
+ | 0.1587 | 10.3982 | 2350 | 3.9049 |
96
+ | 0.2771 | 10.6195 | 2400 | 3.9095 |
97
+ | 0.2156 | 10.8407 | 2450 | 3.9481 |
98
+ | 0.1906 | 11.0619 | 2500 | 3.9076 |
99
+ | 0.2064 | 11.2832 | 2550 | 3.9890 |
100
+ | 0.1756 | 11.5044 | 2600 | 3.8731 |
101
+ | 0.1934 | 11.7257 | 2650 | 3.8914 |
102
+ | 0.1177 | 11.9469 | 2700 | 4.0169 |
103
+ | 0.2135 | 12.1681 | 2750 | 3.6795 |
104
+ | 0.1198 | 12.3894 | 2800 | 3.9709 |
105
+ | 0.1219 | 12.6106 | 2850 | 3.7425 |
106
+ | 0.1073 | 12.8319 | 2900 | 4.2397 |
107
+ | 0.1428 | 13.0531 | 2950 | 3.9107 |
108
+ | 0.0728 | 13.2743 | 3000 | 4.2249 |
109
+ | 0.0516 | 13.4956 | 3050 | 3.9716 |
110
+ | 0.1044 | 13.7168 | 3100 | 4.2036 |
111
+ | 0.2026 | 13.9381 | 3150 | 3.8552 |
112
+ | 0.1182 | 14.1593 | 3200 | 4.0365 |
113
+ | 0.0368 | 14.3805 | 3250 | 4.3629 |
114
+ | 0.0331 | 14.6018 | 3300 | 4.4697 |
115
+ | 0.1629 | 14.8230 | 3350 | 3.9966 |
116
+ | 0.0619 | 15.0442 | 3400 | 4.1223 |
117
+ | 0.0167 | 15.2655 | 3450 | 4.2150 |
118
+ | 0.0602 | 15.4867 | 3500 | 4.1427 |
119
+ | 0.1045 | 15.7080 | 3550 | 3.9883 |
120
+ | 0.0629 | 15.9292 | 3600 | 4.1485 |
121
+ | 0.0492 | 16.1504 | 3650 | 3.9531 |
122
+ | 0.0657 | 16.3717 | 3700 | 4.2826 |
123
+ | 0.0354 | 16.5929 | 3750 | 4.1867 |
124
+ | 0.0327 | 16.8142 | 3800 | 4.1699 |
125
+ | 0.0045 | 17.0354 | 3850 | 4.1904 |
126
+ | 0.0139 | 17.2566 | 3900 | 4.2937 |
127
+ | 0.0373 | 17.4779 | 3950 | 4.1179 |
128
+ | 0.039 | 17.6991 | 4000 | 4.1837 |
129
+ | 0.0717 | 17.9204 | 4050 | 4.2483 |
130
+ | 0.0316 | 18.1416 | 4100 | 4.2423 |
131
+ | 0.0041 | 18.3628 | 4150 | 4.2651 |
132
+ | 0.0268 | 18.5841 | 4200 | 4.3379 |
133
+ | 0.0156 | 18.8053 | 4250 | 4.3978 |
134
+ | 0.0265 | 19.0265 | 4300 | 4.3942 |
135
+ | 0.0461 | 19.2478 | 4350 | 4.4056 |
136
+ | 0.0195 | 19.4690 | 4400 | 4.4082 |
137
+ | 0.0105 | 19.6903 | 4450 | 4.4160 |
138
+ | 0.0387 | 19.9115 | 4500 | 4.4147 |
139
+
140
+
141
+ ### Framework versions
142
+
143
+ - Transformers 4.42.4
144
+ - Pytorch 2.3.0+cu121
145
+ - Datasets 2.20.0
146
+ - Tokenizers 0.19.1