File size: 3,002 Bytes
3e5964c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CS221-mdeberta-v3-base-randomdrop
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CS221-mdeberta-v3-base-randomdrop
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5440
- F1: 0.6741
- Roc Auc: 0.7756
- Accuracy: 0.4071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.5661 | 1.0 | 99 | 0.5434 | 0.0 | 0.5 | 0.1425 |
| 0.5054 | 2.0 | 198 | 0.4744 | 0.4852 | 0.6560 | 0.2621 |
| 0.4409 | 3.0 | 297 | 0.4436 | 0.5766 | 0.7104 | 0.3308 |
| 0.3975 | 4.0 | 396 | 0.4284 | 0.6071 | 0.7316 | 0.3588 |
| 0.2827 | 5.0 | 495 | 0.4228 | 0.6095 | 0.7296 | 0.3562 |
| 0.2831 | 6.0 | 594 | 0.4540 | 0.6467 | 0.7642 | 0.3715 |
| 0.1846 | 7.0 | 693 | 0.4519 | 0.6325 | 0.7459 | 0.3893 |
| 0.1752 | 8.0 | 792 | 0.4538 | 0.6426 | 0.7535 | 0.3740 |
| 0.1547 | 9.0 | 891 | 0.4799 | 0.6541 | 0.7642 | 0.3791 |
| 0.1046 | 10.0 | 990 | 0.4793 | 0.6667 | 0.7687 | 0.4020 |
| 0.1052 | 11.0 | 1089 | 0.5001 | 0.6593 | 0.7658 | 0.4046 |
| 0.0843 | 12.0 | 1188 | 0.5069 | 0.6647 | 0.7705 | 0.3893 |
| 0.0653 | 13.0 | 1287 | 0.5275 | 0.6681 | 0.7669 | 0.4097 |
| 0.0575 | 14.0 | 1386 | 0.5455 | 0.6617 | 0.7632 | 0.3944 |
| 0.0503 | 15.0 | 1485 | 0.5440 | 0.6741 | 0.7756 | 0.4071 |
| 0.0499 | 16.0 | 1584 | 0.5555 | 0.6653 | 0.7660 | 0.4097 |
| 0.0431 | 17.0 | 1683 | 0.5557 | 0.6660 | 0.7675 | 0.4020 |
| 0.0422 | 18.0 | 1782 | 0.5599 | 0.6632 | 0.7664 | 0.3944 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|