update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- generated_from_trainer
|
4 |
+
metrics:
|
5 |
+
- accuracy
|
6 |
+
model-index:
|
7 |
+
- name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.5
|
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-tiny_tobacco3482_kd_CEKD_t1.5_a0.5
|
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: 2.9246
|
19 |
+
- Accuracy: 0.18
|
20 |
+
- Brier Loss: 0.8755
|
21 |
+
- Nll: 6.7967
|
22 |
+
- F1 Micro: 0.18
|
23 |
+
- F1 Macro: 0.0306
|
24 |
+
- Ece: 0.2497
|
25 |
+
- Aurc: 0.8499
|
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 | 3.1239 | 0.145 | 0.8999 | 10.1580 | 0.145 | 0.0253 | 0.2222 | 0.8467 |
|
60 |
+
| No log | 1.96 | 6 | 3.0895 | 0.145 | 0.8946 | 10.5934 | 0.145 | 0.0253 | 0.2303 | 0.8470 |
|
61 |
+
| No log | 2.96 | 9 | 3.0385 | 0.165 | 0.8866 | 8.6307 | 0.165 | 0.0502 | 0.2200 | 0.8458 |
|
62 |
+
| No log | 3.96 | 12 | 2.9972 | 0.21 | 0.8806 | 6.5449 | 0.2100 | 0.0615 | 0.2512 | 0.8364 |
|
63 |
+
| No log | 4.96 | 15 | 2.9719 | 0.155 | 0.8776 | 6.7565 | 0.155 | 0.0271 | 0.2414 | 0.8884 |
|
64 |
+
| No log | 5.96 | 18 | 2.9579 | 0.215 | 0.8768 | 7.0870 | 0.2150 | 0.0643 | 0.2713 | 0.8778 |
|
65 |
+
| No log | 6.96 | 21 | 2.9485 | 0.18 | 0.8768 | 7.0291 | 0.18 | 0.0308 | 0.2482 | 0.8532 |
|
66 |
+
| No log | 7.96 | 24 | 2.9417 | 0.18 | 0.8770 | 6.9706 | 0.18 | 0.0306 | 0.2559 | 0.8525 |
|
67 |
+
| No log | 8.96 | 27 | 2.9360 | 0.18 | 0.8768 | 6.9349 | 0.18 | 0.0306 | 0.2498 | 0.8527 |
|
68 |
+
| No log | 9.96 | 30 | 2.9326 | 0.18 | 0.8767 | 6.9268 | 0.18 | 0.0306 | 0.2635 | 0.8533 |
|
69 |
+
| No log | 10.96 | 33 | 2.9303 | 0.18 | 0.8765 | 6.9226 | 0.18 | 0.0306 | 0.2637 | 0.8531 |
|
70 |
+
| No log | 11.96 | 36 | 2.9289 | 0.18 | 0.8764 | 6.9217 | 0.18 | 0.0306 | 0.2591 | 0.8524 |
|
71 |
+
| No log | 12.96 | 39 | 2.9279 | 0.18 | 0.8762 | 6.8547 | 0.18 | 0.0306 | 0.2505 | 0.8526 |
|
72 |
+
| No log | 13.96 | 42 | 2.9270 | 0.18 | 0.8760 | 6.8491 | 0.18 | 0.0306 | 0.2500 | 0.8520 |
|
73 |
+
| No log | 14.96 | 45 | 2.9263 | 0.18 | 0.8759 | 6.8471 | 0.18 | 0.0306 | 0.2463 | 0.8518 |
|
74 |
+
| No log | 15.96 | 48 | 2.9258 | 0.18 | 0.8758 | 6.8445 | 0.18 | 0.0306 | 0.2462 | 0.8520 |
|
75 |
+
| No log | 16.96 | 51 | 2.9255 | 0.18 | 0.8758 | 6.8452 | 0.18 | 0.0306 | 0.2587 | 0.8511 |
|
76 |
+
| No log | 17.96 | 54 | 2.9256 | 0.18 | 0.8758 | 6.7940 | 0.18 | 0.0306 | 0.2585 | 0.8513 |
|
77 |
+
| No log | 18.96 | 57 | 2.9256 | 0.18 | 0.8758 | 6.7930 | 0.18 | 0.0306 | 0.2625 | 0.8508 |
|
78 |
+
| No log | 19.96 | 60 | 2.9252 | 0.18 | 0.8757 | 6.7945 | 0.18 | 0.0306 | 0.2580 | 0.8506 |
|
79 |
+
| No log | 20.96 | 63 | 2.9250 | 0.18 | 0.8756 | 6.7999 | 0.18 | 0.0306 | 0.2539 | 0.8505 |
|
80 |
+
| No log | 21.96 | 66 | 2.9248 | 0.18 | 0.8756 | 6.8441 | 0.18 | 0.0306 | 0.2538 | 0.8502 |
|
81 |
+
| No log | 22.96 | 69 | 2.9247 | 0.18 | 0.8755 | 6.8439 | 0.18 | 0.0306 | 0.2497 | 0.8500 |
|
82 |
+
| No log | 23.96 | 72 | 2.9247 | 0.18 | 0.8755 | 6.7977 | 0.18 | 0.0306 | 0.2497 | 0.8500 |
|
83 |
+
| No log | 24.96 | 75 | 2.9246 | 0.18 | 0.8755 | 6.7967 | 0.18 | 0.0306 | 0.2497 | 0.8499 |
|
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
|