---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
datasets:
- yahoo_answers_topics
metrics:
- accuracy
model-index:
- name: deberta_finetuned_yahoo_answers_topics
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: yahoo_answers_topics
      type: yahoo_answers_topics
      config: yahoo_answers_topics
      split: test
      args: yahoo_answers_topics
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7073333333333334
---

<!-- 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. -->

# deberta_finetuned_yahoo_answers_topics

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the yahoo_answers_topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9246
- Accuracy: 0.7073

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.197         | 0.03  | 5000  | 1.1306          | 0.6511   |
| 1.0564        | 0.06  | 10000 | 1.0731          | 0.6690   |
| 0.9436        | 0.09  | 15000 | 1.0345          | 0.6864   |
| 1.0601        | 0.11  | 20000 | 0.9684          | 0.6925   |
| 0.9577        | 0.14  | 25000 | 0.9466          | 0.7015   |
| 0.9172        | 0.17  | 30000 | 0.9246          | 0.7073   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1