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-small_tobacco3482_kd_CEKD_t5.0_a0.9
|
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_a0.9
|
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: 2.4735
|
19 |
+
- Accuracy: 0.19
|
20 |
+
- Brier Loss: 0.8651
|
21 |
+
- Nll: 6.3618
|
22 |
+
- F1 Micro: 0.19
|
23 |
+
- F1 Macro: 0.0641
|
24 |
+
- Ece: 0.2456
|
25 |
+
- Aurc: 0.7331
|
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 | 2.6674 | 0.06 | 0.9042 | 9.2824 | 0.06 | 0.0114 | 0.1749 | 0.9042 |
|
60 |
+
| No log | 1.96 | 6 | 2.5911 | 0.18 | 0.8886 | 6.4746 | 0.18 | 0.0305 | 0.2317 | 0.8026 |
|
61 |
+
| No log | 2.96 | 9 | 2.5252 | 0.18 | 0.8764 | 7.5079 | 0.18 | 0.0305 | 0.2390 | 0.8141 |
|
62 |
+
| No log | 3.96 | 12 | 2.5235 | 0.185 | 0.8777 | 6.9489 | 0.185 | 0.0488 | 0.2553 | 0.7838 |
|
63 |
+
| No log | 4.96 | 15 | 2.5223 | 0.185 | 0.8754 | 6.8606 | 0.185 | 0.0488 | 0.2572 | 0.7773 |
|
64 |
+
| No log | 5.96 | 18 | 2.5213 | 0.185 | 0.8732 | 5.9794 | 0.185 | 0.0488 | 0.2384 | 0.7684 |
|
65 |
+
| No log | 6.96 | 21 | 2.5203 | 0.185 | 0.8723 | 5.9244 | 0.185 | 0.0488 | 0.2406 | 0.7603 |
|
66 |
+
| No log | 7.96 | 24 | 2.5149 | 0.185 | 0.8713 | 5.9034 | 0.185 | 0.0488 | 0.2484 | 0.7560 |
|
67 |
+
| No log | 8.96 | 27 | 2.5064 | 0.185 | 0.8701 | 5.9325 | 0.185 | 0.0488 | 0.2525 | 0.7529 |
|
68 |
+
| No log | 9.96 | 30 | 2.5014 | 0.185 | 0.8695 | 6.7123 | 0.185 | 0.0488 | 0.2399 | 0.7528 |
|
69 |
+
| No log | 10.96 | 33 | 2.4977 | 0.185 | 0.8693 | 6.7598 | 0.185 | 0.0488 | 0.2487 | 0.7511 |
|
70 |
+
| No log | 11.96 | 36 | 2.4944 | 0.185 | 0.8692 | 6.8130 | 0.185 | 0.0488 | 0.2488 | 0.7476 |
|
71 |
+
| No log | 12.96 | 39 | 2.4908 | 0.185 | 0.8688 | 6.7610 | 0.185 | 0.0488 | 0.2488 | 0.7452 |
|
72 |
+
| No log | 13.96 | 42 | 2.4867 | 0.185 | 0.8680 | 6.6686 | 0.185 | 0.0488 | 0.2484 | 0.7428 |
|
73 |
+
| No log | 14.96 | 45 | 2.4830 | 0.185 | 0.8673 | 6.6283 | 0.185 | 0.0488 | 0.2426 | 0.7431 |
|
74 |
+
| No log | 15.96 | 48 | 2.4805 | 0.185 | 0.8668 | 6.4857 | 0.185 | 0.0488 | 0.2385 | 0.7410 |
|
75 |
+
| No log | 16.96 | 51 | 2.4794 | 0.185 | 0.8666 | 6.4425 | 0.185 | 0.0488 | 0.2459 | 0.7385 |
|
76 |
+
| No log | 17.96 | 54 | 2.4795 | 0.185 | 0.8664 | 6.0769 | 0.185 | 0.0488 | 0.2406 | 0.7352 |
|
77 |
+
| No log | 18.96 | 57 | 2.4793 | 0.185 | 0.8664 | 6.1000 | 0.185 | 0.0488 | 0.2402 | 0.7355 |
|
78 |
+
| No log | 19.96 | 60 | 2.4774 | 0.185 | 0.8660 | 6.3802 | 0.185 | 0.0488 | 0.2506 | 0.7346 |
|
79 |
+
| No log | 20.96 | 63 | 2.4762 | 0.185 | 0.8657 | 6.4330 | 0.185 | 0.0488 | 0.2550 | 0.7344 |
|
80 |
+
| No log | 21.96 | 66 | 2.4750 | 0.185 | 0.8654 | 6.3721 | 0.185 | 0.0488 | 0.2513 | 0.7333 |
|
81 |
+
| No log | 22.96 | 69 | 2.4741 | 0.19 | 0.8652 | 6.3676 | 0.19 | 0.0641 | 0.2453 | 0.7332 |
|
82 |
+
| No log | 23.96 | 72 | 2.4738 | 0.19 | 0.8652 | 6.3645 | 0.19 | 0.0641 | 0.2455 | 0.7331 |
|
83 |
+
| No log | 24.96 | 75 | 2.4735 | 0.19 | 0.8651 | 6.3618 | 0.19 | 0.0641 | 0.2456 | 0.7331 |
|
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
|