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

# FakeNews-deberta-base-stopwords

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co./microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2102
- Accuracy: 0.9612

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3343        | 1.0   | 1605 | 0.3262          | 0.9196   |
| 0.3889        | 2.0   | 3210 | 0.3157          | 0.9276   |
| 0.2327        | 3.0   | 4815 | 0.2983          | 0.9383   |
| 0.2261        | 4.0   | 6420 | 0.2127          | 0.9528   |
| 0.1629        | 5.0   | 8025 | 0.2102          | 0.9612   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1