---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: output
  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. -->

# output

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: 0.0014
- F1: 0.9009
- Accuracy: 0.89

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.3529        | 0.16  | 10   | 0.2584          | 0.6667 | 0.5      |
| 0.2348        | 0.32  | 20   | 0.0989          | 0.6667 | 0.5      |
| 0.0492        | 0.48  | 30   | 0.0314          | 0.9615 | 0.96     |
| 0.0336        | 0.64  | 40   | 0.0132          | 0.6849 | 0.54     |
| 0.0185        | 0.8   | 50   | 0.0345          | 0.6667 | 0.5      |
| 0.0114        | 0.96  | 60   | 0.0490          | 0.9524 | 0.95     |
| 0.0118        | 1.12  | 70   | 0.0235          | 0.7042 | 0.58     |
| 0.01          | 1.28  | 80   | 0.0352          | 0.7299 | 0.63     |
| 0.0061        | 1.44  | 90   | 0.0195          | 0.8    | 0.75     |
| 0.0067        | 1.6   | 100  | 0.0108          | 0.8547 | 0.83     |
| 0.0055        | 1.76  | 110  | 0.0186          | 0.7874 | 0.73     |
| 0.0052        | 1.92  | 120  | 0.0141          | 0.9174 | 0.91     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0