---
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: 0.3475
- F1: 0.8781
- Roc Auc: 0.9070
- Accuracy: 0.7651

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4197        | 1.0   | 277  | 0.3731          | 0.6624 | 0.7504  | 0.4435   |
| 0.2694        | 2.0   | 554  | 0.3172          | 0.7734 | 0.8309  | 0.5411   |
| 0.1796        | 3.0   | 831  | 0.2815          | 0.7769 | 0.8256  | 0.5890   |
| 0.1281        | 4.0   | 1108 | 0.2802          | 0.8120 | 0.8543  | 0.6305   |
| 0.0754        | 5.0   | 1385 | 0.2998          | 0.8177 | 0.8565  | 0.6495   |
| 0.067         | 6.0   | 1662 | 0.2926          | 0.8367 | 0.8755  | 0.6838   |
| 0.0303        | 7.0   | 1939 | 0.2977          | 0.8409 | 0.8750  | 0.7010   |
| 0.009         | 8.0   | 2216 | 0.3252          | 0.8474 | 0.8777  | 0.7091   |
| 0.0114        | 9.0   | 2493 | 0.3181          | 0.8539 | 0.8899  | 0.7281   |
| 0.006         | 10.0  | 2770 | 0.3390          | 0.8581 | 0.8890  | 0.7344   |
| 0.0023        | 11.0  | 3047 | 0.3407          | 0.8646 | 0.8934  | 0.7353   |
| 0.0022        | 12.0  | 3324 | 0.3453          | 0.8674 | 0.8991  | 0.7525   |
| 0.0031        | 13.0  | 3601 | 0.3488          | 0.8708 | 0.9021  | 0.7507   |
| 0.0013        | 14.0  | 3878 | 0.3440          | 0.8736 | 0.9044  | 0.7579   |
| 0.0009        | 15.0  | 4155 | 0.3475          | 0.8781 | 0.9070  | 0.7651   |
| 0.0026        | 16.0  | 4432 | 0.3455          | 0.8767 | 0.9057  | 0.7651   |
| 0.0008        | 17.0  | 4709 | 0.3504          | 0.8755 | 0.9053  | 0.7615   |
| 0.0009        | 18.0  | 4986 | 0.3549          | 0.8742 | 0.9043  | 0.7588   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0