Hugging Face's logo

tiennvcs
/
bert-base-uncased-finetuned-vi-infovqa

Question Answering
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
tiennvcs commited on
Commit
7a09151
路
1 Parent(s): f7312ff

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +101 -0
README.md ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: bert-base-uncased-finetuned-vi-infovqa
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # bert-base-uncased-finetuned-vi-infovqa
14
+
15
+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 13.9895
18
+
19
+ ## Model description
20
+
21
+ More information needed
22
+
23
+ ## Intended uses & limitations
24
+
25
+ More information needed
26
+
27
+ ## Training and evaluation data
28
+
29
+ More information needed
30
+
31
+ ## Training procedure
32
+
33
+ ### Training hyperparameters
34
+
35
+ The following hyperparameters were used during training:
36
+ - learning_rate: 0.0001
37
+ - train_batch_size: 4
38
+ - eval_batch_size: 4
39
+ - seed: 250500
40
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
41
+ - lr_scheduler_type: linear
42
+ - num_epochs: 50
43
+
44
+ ### Training results
45
+
46
+ | Training Loss | Epoch | Step | Validation Loss |
47
+ |:-------------:|:-----:|:-----:|:---------------:|
48
+ | 4.3635 | 1.07 | 500 | 4.5286 |
49
+ | 3.8961 | 2.13 | 1000 | 4.4055 |
50
+ | 3.3012 | 3.2 | 1500 | 5.0429 |
51
+ | 2.5998 | 4.26 | 2000 | 5.4774 |
52
+ | 1.8851 | 5.33 | 2500 | 6.4376 |
53
+ | 1.3968 | 6.4 | 3000 | 6.8778 |
54
+ | 1.1244 | 7.46 | 3500 | 7.1008 |
55
+ | 0.8851 | 8.53 | 4000 | 6.8303 |
56
+ | 0.7654 | 9.59 | 4500 | 8.6628 |
57
+ | 0.6895 | 10.66 | 5000 | 6.0228 |
58
+ | 0.5989 | 11.73 | 5500 | 8.8191 |
59
+ | 0.6033 | 12.79 | 6000 | 9.7359 |
60
+ | 0.6005 | 13.86 | 6500 | 7.6668 |
61
+ | 0.522 | 14.93 | 7000 | 8.7185 |
62
+ | 0.464 | 15.99 | 7500 | 10.1035 |
63
+ | 0.4112 | 17.06 | 8000 | 8.7928 |
64
+ | 0.3501 | 18.12 | 8500 | 9.9157 |
65
+ | 0.3401 | 19.19 | 9000 | 12.1013 |
66
+ | 0.3233 | 20.26 | 9500 | 10.2730 |
67
+ | 0.2513 | 21.32 | 10000 | 8.4839 |
68
+ | 0.2319 | 22.39 | 10500 | 10.9367 |
69
+ | 0.2269 | 23.45 | 11000 | 9.6821 |
70
+ | 0.237 | 24.52 | 11500 | 10.2357 |
71
+ | 0.1868 | 25.59 | 12000 | 9.8762 |
72
+ | 0.1655 | 26.65 | 12500 | 10.7398 |
73
+ | 0.1561 | 27.72 | 13000 | 11.8157 |
74
+ | 0.1714 | 28.78 | 13500 | 10.8686 |
75
+ | 0.1098 | 29.85 | 14000 | 13.1537 |
76
+ | 0.1222 | 30.92 | 14500 | 14.5398 |
77
+ | 0.1325 | 31.98 | 15000 | 12.6095 |
78
+ | 0.1088 | 33.05 | 15500 | 12.0747 |
79
+ | 0.0855 | 34.12 | 16000 | 12.4450 |
80
+ | 0.0832 | 35.18 | 16500 | 12.9436 |
81
+ | 0.0687 | 36.25 | 17000 | 12.5902 |
82
+ | 0.0804 | 37.31 | 17500 | 11.9873 |
83
+ | 0.0427 | 38.38 | 18000 | 12.6357 |
84
+ | 0.0529 | 39.45 | 18500 | 11.5851 |
85
+ | 0.0478 | 40.51 | 19000 | 13.1796 |
86
+ | 0.0513 | 41.58 | 19500 | 13.8597 |
87
+ | 0.0449 | 42.64 | 20000 | 13.6467 |
88
+ | 0.0327 | 43.71 | 20500 | 13.0391 |
89
+ | 0.0329 | 44.78 | 21000 | 13.1984 |
90
+ | 0.0266 | 45.84 | 21500 | 13.9690 |
91
+ | 0.0301 | 46.91 | 22000 | 13.9516 |
92
+ | 0.0193 | 47.97 | 22500 | 13.9104 |
93
+ | 0.0273 | 49.04 | 23000 | 13.9895 |
94
+
95
+
96
+ ### Framework versions
97
+
98
+ - Transformers 4.14.1
99
+ - Pytorch 1.10.0+cu111
100
+ - Datasets 1.16.1
101
+ - Tokenizers 0.10.3