|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: microsoft/deberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: CS221-deberta-base-finetuned-semeval-aug |
|
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-deberta-base-finetuned-semeval-aug |
|
|
|
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2871 |
|
- F1: 0.8364 |
|
- Roc Auc: 0.8738 |
|
- Accuracy: 0.6387 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
|
| 0.2713 | 1.0 | 139 | 0.3886 | 0.7310 | 0.8051 | 0.4228 | |
|
| 0.2914 | 2.0 | 278 | 0.3337 | 0.7849 | 0.8464 | 0.5248 | |
|
| 0.2201 | 3.0 | 417 | 0.3073 | 0.7995 | 0.8511 | 0.5501 | |
|
| 0.1444 | 4.0 | 556 | 0.3027 | 0.8275 | 0.8727 | 0.6007 | |
|
| 0.0964 | 5.0 | 695 | 0.2871 | 0.8364 | 0.8738 | 0.6387 | |
|
| 0.0536 | 6.0 | 834 | 0.3024 | 0.8432 | 0.8796 | 0.6612 | |
|
| 0.042 | 7.0 | 973 | 0.2909 | 0.8631 | 0.8960 | 0.6920 | |
|
| 0.0334 | 8.0 | 1112 | 0.2976 | 0.8675 | 0.9002 | 0.7037 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.5.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
