---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- deBERTa
- sequence-classification
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
model-index:
- name: banking77-deBERTa-v3-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: banking77
      type: banking77
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9195402298850575
      name: Accuracy
---

<!-- 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. -->

# banking77-deBERTa-v3-base

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3281
- Accuracy: 0.9195
- F1 Macro: 0.9170
- Precision Macro: 0.9222
- Recall Macro: 0.9159
- F1 Weighted: 0.9194
- Precision Weighted: 0.9229
- Recall Weighted: 0.9195

## 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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | F1 Weighted | Precision Weighted | Recall Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-----------:|:------------------:|:---------------:|
| 3.4666        | 1.0   | 501  | 3.1762          | 0.3548   | 0.2479   | 0.3016          | 0.3195       | 0.2774      | 0.3421             | 0.3548          |
| 1.2538        | 2.0   | 1002 | 1.0122          | 0.8141   | 0.7625   | 0.8091          | 0.7795       | 0.7946      | 0.8291             | 0.8141          |
| 0.5576        | 3.0   | 1503 | 0.4823          | 0.8941   | 0.8797   | 0.9012          | 0.8786       | 0.8915      | 0.9021             | 0.8941          |
| 0.3544        | 4.0   | 2004 | 0.3625          | 0.9110   | 0.9090   | 0.9170          | 0.9084       | 0.9108      | 0.9172             | 0.9110          |
| 0.2603        | 5.0   | 2505 | 0.3281          | 0.9195   | 0.9170   | 0.9222          | 0.9159       | 0.9194      | 0.9229             | 0.9195          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1