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_t2.0_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_t2.0_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: 3.5976
|
| 19 |
+
- Accuracy: 0.18
|
| 20 |
+
- Brier Loss: 0.8781
|
| 21 |
+
- Nll: 6.8947
|
| 22 |
+
- F1 Micro: 0.18
|
| 23 |
+
- F1 Macro: 0.0306
|
| 24 |
+
- Ece: 0.2499
|
| 25 |
+
- Aurc: 0.8510
|
| 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 | 3.8479 | 0.145 | 0.8999 | 10.1604 | 0.145 | 0.0253 | 0.2222 | 0.8467 |
|
| 60 |
+
| No log | 1.96 | 6 | 3.8090 | 0.145 | 0.8946 | 10.5967 | 0.145 | 0.0253 | 0.2246 | 0.8470 |
|
| 61 |
+
| No log | 2.96 | 9 | 3.7500 | 0.16 | 0.8866 | 8.6365 | 0.16 | 0.0406 | 0.2205 | 0.8486 |
|
| 62 |
+
| No log | 3.96 | 12 | 3.7003 | 0.16 | 0.8805 | 6.5484 | 0.16 | 0.0327 | 0.2242 | 0.8816 |
|
| 63 |
+
| No log | 4.96 | 15 | 3.6677 | 0.155 | 0.8776 | 6.7592 | 0.155 | 0.0271 | 0.2365 | 0.8919 |
|
| 64 |
+
| No log | 5.96 | 18 | 3.6477 | 0.155 | 0.8770 | 7.2639 | 0.155 | 0.0278 | 0.2368 | 0.8961 |
|
| 65 |
+
| No log | 6.96 | 21 | 3.6339 | 0.18 | 0.8774 | 7.3546 | 0.18 | 0.0313 | 0.2486 | 0.8556 |
|
| 66 |
+
| No log | 7.96 | 24 | 3.6240 | 0.18 | 0.8781 | 7.0685 | 0.18 | 0.0308 | 0.2654 | 0.8528 |
|
| 67 |
+
| No log | 8.96 | 27 | 3.6163 | 0.18 | 0.8784 | 7.0041 | 0.18 | 0.0306 | 0.2561 | 0.8532 |
|
| 68 |
+
| No log | 9.96 | 30 | 3.6114 | 0.18 | 0.8787 | 6.9904 | 0.18 | 0.0306 | 0.2584 | 0.8537 |
|
| 69 |
+
| No log | 10.96 | 33 | 3.6078 | 0.18 | 0.8788 | 6.9806 | 0.18 | 0.0306 | 0.2594 | 0.8538 |
|
| 70 |
+
| No log | 11.96 | 36 | 3.6052 | 0.18 | 0.8789 | 6.9768 | 0.18 | 0.0306 | 0.2596 | 0.8537 |
|
| 71 |
+
| No log | 12.96 | 39 | 3.6034 | 0.18 | 0.8788 | 6.9716 | 0.18 | 0.0306 | 0.2507 | 0.8532 |
|
| 72 |
+
| No log | 13.96 | 42 | 3.6018 | 0.18 | 0.8786 | 6.9683 | 0.18 | 0.0306 | 0.2548 | 0.8527 |
|
| 73 |
+
| No log | 14.96 | 45 | 3.6005 | 0.18 | 0.8786 | 6.9040 | 0.18 | 0.0306 | 0.2597 | 0.8524 |
|
| 74 |
+
| No log | 15.96 | 48 | 3.5995 | 0.18 | 0.8784 | 6.8978 | 0.18 | 0.0306 | 0.2685 | 0.8518 |
|
| 75 |
+
| No log | 16.96 | 51 | 3.5989 | 0.18 | 0.8784 | 6.8972 | 0.18 | 0.0306 | 0.2641 | 0.8515 |
|
| 76 |
+
| No log | 17.96 | 54 | 3.5989 | 0.18 | 0.8784 | 6.8961 | 0.18 | 0.0306 | 0.2550 | 0.8513 |
|
| 77 |
+
| No log | 18.96 | 57 | 3.5988 | 0.18 | 0.8784 | 6.8968 | 0.18 | 0.0306 | 0.2505 | 0.8510 |
|
| 78 |
+
| No log | 19.96 | 60 | 3.5982 | 0.18 | 0.8782 | 6.8956 | 0.18 | 0.0306 | 0.2478 | 0.8511 |
|
| 79 |
+
| No log | 20.96 | 63 | 3.5980 | 0.18 | 0.8782 | 6.8954 | 0.18 | 0.0306 | 0.2456 | 0.8507 |
|
| 80 |
+
| No log | 21.96 | 66 | 3.5978 | 0.18 | 0.8782 | 6.8951 | 0.18 | 0.0306 | 0.2499 | 0.8511 |
|
| 81 |
+
| No log | 22.96 | 69 | 3.5976 | 0.18 | 0.8781 | 6.8949 | 0.18 | 0.0306 | 0.2499 | 0.8510 |
|
| 82 |
+
| No log | 23.96 | 72 | 3.5976 | 0.18 | 0.8781 | 6.8949 | 0.18 | 0.0306 | 0.2499 | 0.8510 |
|
| 83 |
+
| No log | 24.96 | 75 | 3.5976 | 0.18 | 0.8781 | 6.8947 | 0.18 | 0.0306 | 0.2499 | 0.8510 |
|
| 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
|