Hugging Face's logo

jordyvl
/
dit-small_tobacco3482_kd_CEKD_t5.0_a0.7

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Inference Endpoints
Model card Files Community
jordyvl commited on
Commit
3b0434e
•
1 Parent(s): 1d5c1a9

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +91 -0
README.md ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - generated_from_trainer
4
+ metrics:
5
+ - accuracy
6
+ model-index:
7
+ - name: dit-small_tobacco3482_kd_CEKD_t5.0_a0.7
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
+ # dit-small_tobacco3482_kd_CEKD_t5.0_a0.7
15
+
16
+ This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 3.1347
19
+ - Accuracy: 0.185
20
+ - Brier Loss: 0.8666
21
+ - Nll: 5.9997
22
+ - F1 Micro: 0.185
23
+ - F1 Macro: 0.0488
24
+ - Ece: 0.2480
25
+ - Aurc: 0.7353
26
+
27
+ ## Model description
28
+
29
+ More information needed
30
+
31
+ ## Intended uses & limitations
32
+
33
+ More information needed
34
+
35
+ ## Training and evaluation data
36
+
37
+ More information needed
38
+
39
+ ## Training procedure
40
+
41
+ ### Training hyperparameters
42
+
43
+ The following hyperparameters were used during training:
44
+ - learning_rate: 2e-05
45
+ - train_batch_size: 16
46
+ - eval_batch_size: 16
47
+ - seed: 42
48
+ - gradient_accumulation_steps: 16
49
+ - total_train_batch_size: 256
50
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
+ - lr_scheduler_type: linear
52
+ - lr_scheduler_warmup_ratio: 0.1
53
+ - num_epochs: 25
54
+
55
+ ### Training results
56
+
57
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
58
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
59
+ | No log | 0.96 | 3 | 3.3695 | 0.06 | 0.9042 | 9.1505 | 0.06 | 0.0114 | 0.1750 | 0.9033 |
60
+ | No log | 1.96 | 6 | 3.2847 | 0.18 | 0.8890 | 7.1646 | 0.18 | 0.0305 | 0.2263 | 0.8027 |
61
+ | No log | 2.96 | 9 | 3.2039 | 0.18 | 0.8773 | 8.6118 | 0.18 | 0.0305 | 0.2478 | 0.8186 |
62
+ | No log | 3.96 | 12 | 3.1950 | 0.18 | 0.8806 | 7.4891 | 0.18 | 0.0305 | 0.2514 | 0.8131 |
63
+ | No log | 4.96 | 15 | 3.1951 | 0.185 | 0.8795 | 6.7125 | 0.185 | 0.0488 | 0.2555 | 0.7835 |
64
+ | No log | 5.96 | 18 | 3.1931 | 0.185 | 0.8766 | 5.2600 | 0.185 | 0.0488 | 0.2526 | 0.7702 |
65
+ | No log | 6.96 | 21 | 3.1876 | 0.185 | 0.8741 | 5.6453 | 0.185 | 0.0488 | 0.2372 | 0.7672 |
66
+ | No log | 7.96 | 24 | 3.1800 | 0.185 | 0.8726 | 5.9473 | 0.185 | 0.0488 | 0.2412 | 0.7644 |
67
+ | No log | 8.96 | 27 | 3.1712 | 0.185 | 0.8712 | 5.9421 | 0.185 | 0.0488 | 0.2491 | 0.7615 |
68
+ | No log | 9.96 | 30 | 3.1656 | 0.185 | 0.8704 | 6.6276 | 0.185 | 0.0488 | 0.2516 | 0.7602 |
69
+ | No log | 10.96 | 33 | 3.1623 | 0.185 | 0.8704 | 6.8796 | 0.185 | 0.0488 | 0.2487 | 0.7598 |
70
+ | No log | 11.96 | 36 | 3.1601 | 0.185 | 0.8708 | 7.1352 | 0.185 | 0.0488 | 0.2451 | 0.7559 |
71
+ | No log | 12.96 | 39 | 3.1573 | 0.185 | 0.8706 | 7.0151 | 0.185 | 0.0488 | 0.2492 | 0.7531 |
72
+ | No log | 13.96 | 42 | 3.1531 | 0.185 | 0.8699 | 6.7912 | 0.185 | 0.0488 | 0.2450 | 0.7484 |
73
+ | No log | 14.96 | 45 | 3.1485 | 0.185 | 0.8693 | 6.6578 | 0.185 | 0.0488 | 0.2513 | 0.7491 |
74
+ | No log | 15.96 | 48 | 3.1449 | 0.185 | 0.8685 | 6.1407 | 0.185 | 0.0488 | 0.2596 | 0.7463 |
75
+ | No log | 16.96 | 51 | 3.1428 | 0.185 | 0.8681 | 5.9160 | 0.185 | 0.0488 | 0.2548 | 0.7432 |
76
+ | No log | 17.96 | 54 | 3.1421 | 0.185 | 0.8678 | 5.8419 | 0.185 | 0.0488 | 0.2449 | 0.7401 |
77
+ | No log | 18.96 | 57 | 3.1413 | 0.185 | 0.8677 | 5.7417 | 0.185 | 0.0488 | 0.2606 | 0.7382 |
78
+ | No log | 19.96 | 60 | 3.1391 | 0.185 | 0.8673 | 5.7824 | 0.185 | 0.0488 | 0.2432 | 0.7365 |
79
+ | No log | 20.96 | 63 | 3.1378 | 0.185 | 0.8671 | 5.9509 | 0.185 | 0.0488 | 0.2598 | 0.7368 |
80
+ | No log | 21.96 | 66 | 3.1364 | 0.185 | 0.8668 | 6.0164 | 0.185 | 0.0488 | 0.2477 | 0.7361 |
81
+ | No log | 22.96 | 69 | 3.1355 | 0.185 | 0.8667 | 6.0109 | 0.185 | 0.0488 | 0.2437 | 0.7352 |
82
+ | No log | 23.96 | 72 | 3.1350 | 0.185 | 0.8666 | 6.0029 | 0.185 | 0.0488 | 0.2438 | 0.7351 |
83
+ | No log | 24.96 | 75 | 3.1347 | 0.185 | 0.8666 | 5.9997 | 0.185 | 0.0488 | 0.2480 | 0.7353 |
84
+
85
+
86
+ ### Framework versions
87
+
88
+ - Transformers 4.26.1
89
+ - Pytorch 1.13.1.post200
90
+ - Datasets 2.9.0
91
+ - Tokenizers 0.13.2