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_simkd_CEKD_t1_aNone
|
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_simkd_CEKD_t1_aNone
|
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: 0.9983
|
19 |
+
- Accuracy: 0.18
|
20 |
+
- Brier Loss: 0.8965
|
21 |
+
- Nll: 6.7849
|
22 |
+
- F1 Micro: 0.18
|
23 |
+
- F1 Macro: 0.0305
|
24 |
+
- Ece: 0.2195
|
25 |
+
- Aurc: 0.8182
|
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: 4
|
46 |
+
- eval_batch_size: 4
|
47 |
+
- seed: 42
|
48 |
+
- gradient_accumulation_steps: 16
|
49 |
+
- total_train_batch_size: 64
|
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 | 12 | 1.0062 | 0.18 | 0.8980 | 6.1518 | 0.18 | 0.0309 | 0.2213 | 0.7838 |
|
60 |
+
| No log | 1.96 | 24 | 1.0034 | 0.18 | 0.8987 | 5.7795 | 0.18 | 0.0305 | 0.2273 | 0.8165 |
|
61 |
+
| No log | 2.96 | 36 | 1.0025 | 0.18 | 0.8984 | 6.4819 | 0.18 | 0.0305 | 0.2249 | 0.8306 |
|
62 |
+
| No log | 3.96 | 48 | 1.0018 | 0.18 | 0.8982 | 6.8521 | 0.18 | 0.0306 | 0.2205 | 0.8505 |
|
63 |
+
| No log | 4.96 | 60 | 1.0015 | 0.16 | 0.8980 | 6.6853 | 0.16 | 0.0324 | 0.2089 | 0.8798 |
|
64 |
+
| No log | 5.96 | 72 | 1.0011 | 0.175 | 0.8979 | 6.8349 | 0.175 | 0.0314 | 0.2134 | 0.8345 |
|
65 |
+
| No log | 6.96 | 84 | 1.0008 | 0.18 | 0.8976 | 6.8293 | 0.18 | 0.0313 | 0.2249 | 0.8208 |
|
66 |
+
| No log | 7.96 | 96 | 1.0005 | 0.18 | 0.8975 | 6.9400 | 0.18 | 0.0305 | 0.2230 | 0.8140 |
|
67 |
+
| No log | 8.96 | 108 | 1.0003 | 0.18 | 0.8974 | 6.5877 | 0.18 | 0.0306 | 0.2230 | 0.8246 |
|
68 |
+
| No log | 9.96 | 120 | 1.0000 | 0.18 | 0.8973 | 6.5454 | 0.18 | 0.0306 | 0.2188 | 0.8188 |
|
69 |
+
| No log | 10.96 | 132 | 0.9998 | 0.18 | 0.8972 | 6.5555 | 0.18 | 0.0306 | 0.2274 | 0.8151 |
|
70 |
+
| No log | 11.96 | 144 | 0.9996 | 0.18 | 0.8971 | 6.5819 | 0.18 | 0.0306 | 0.2254 | 0.8131 |
|
71 |
+
| No log | 12.96 | 156 | 0.9994 | 0.18 | 0.8970 | 6.7150 | 0.18 | 0.0305 | 0.2255 | 0.8162 |
|
72 |
+
| No log | 13.96 | 168 | 0.9993 | 0.18 | 0.8969 | 6.6542 | 0.18 | 0.0305 | 0.2213 | 0.8220 |
|
73 |
+
| No log | 14.96 | 180 | 0.9991 | 0.18 | 0.8968 | 6.6025 | 0.18 | 0.0305 | 0.2213 | 0.8125 |
|
74 |
+
| No log | 15.96 | 192 | 0.9990 | 0.18 | 0.8968 | 7.0424 | 0.18 | 0.0305 | 0.2301 | 0.8201 |
|
75 |
+
| No log | 16.96 | 204 | 0.9988 | 0.18 | 0.8967 | 6.6676 | 0.18 | 0.0305 | 0.2258 | 0.8153 |
|
76 |
+
| No log | 17.96 | 216 | 0.9987 | 0.18 | 0.8967 | 6.6621 | 0.18 | 0.0305 | 0.2270 | 0.8145 |
|
77 |
+
| No log | 18.96 | 228 | 0.9986 | 0.18 | 0.8967 | 7.0058 | 0.18 | 0.0305 | 0.2259 | 0.8214 |
|
78 |
+
| No log | 19.96 | 240 | 0.9985 | 0.18 | 0.8966 | 6.8777 | 0.18 | 0.0305 | 0.2194 | 0.8183 |
|
79 |
+
| No log | 20.96 | 252 | 0.9984 | 0.18 | 0.8966 | 6.7612 | 0.18 | 0.0305 | 0.2282 | 0.8131 |
|
80 |
+
| No log | 21.96 | 264 | 0.9984 | 0.18 | 0.8966 | 6.7811 | 0.18 | 0.0305 | 0.2282 | 0.8145 |
|
81 |
+
| No log | 22.96 | 276 | 0.9983 | 0.18 | 0.8965 | 6.7044 | 0.18 | 0.0305 | 0.2239 | 0.8167 |
|
82 |
+
| No log | 23.96 | 288 | 0.9983 | 0.18 | 0.8965 | 6.7813 | 0.18 | 0.0305 | 0.2217 | 0.8183 |
|
83 |
+
| No log | 24.96 | 300 | 0.9983 | 0.18 | 0.8965 | 6.7849 | 0.18 | 0.0305 | 0.2195 | 0.8182 |
|
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
|