---
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