---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Intent-classification-DeBERTa-model-Ashuv2
  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. -->

# Intent-classification-DeBERTa-model-Ashuv2

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1666
- Accuracy: 0.9012

## 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
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7819        | 0.12  | 10   | 1.7969          | 0.3665   |
| 1.7316        | 0.25  | 20   | 1.6351          | 0.3975   |
| 1.4279        | 0.37  | 30   | 1.2845          | 0.5776   |
| 1.0181        | 0.49  | 40   | 0.8974          | 0.7143   |
| 0.7285        | 0.62  | 50   | 0.6361          | 0.7640   |
| 0.7265        | 0.74  | 60   | 0.4886          | 0.8509   |
| 0.5108        | 0.86  | 70   | 0.3599          | 0.9006   |
| 0.4413        | 0.99  | 80   | 0.2510          | 0.8944   |
| 0.3556        | 1.11  | 90   | 0.2156          | 0.9130   |
| 0.2884        | 1.23  | 100  | 0.2777          | 0.8944   |
| 0.1914        | 1.36  | 110  | 0.2518          | 0.8944   |
| 0.5051        | 1.48  | 120  | 0.2118          | 0.9130   |
| 0.1151        | 1.6   | 130  | 0.1957          | 0.9130   |
| 0.1745        | 1.73  | 140  | 0.2052          | 0.8820   |
| 0.1987        | 1.85  | 150  | 0.2053          | 0.8882   |
| 0.2467        | 1.98  | 160  | 0.1945          | 0.8944   |
| 0.3075        | 2.1   | 170  | 0.2680          | 0.8944   |
| 0.1732        | 2.22  | 180  | 0.2642          | 0.8882   |
| 0.1627        | 2.35  | 190  | 0.1915          | 0.9068   |
| 0.1766        | 2.47  | 200  | 0.1708          | 0.9130   |
| 0.2563        | 2.59  | 210  | 0.1691          | 0.8944   |
| 0.189         | 2.72  | 220  | 0.1941          | 0.9130   |
| 0.1696        | 2.84  | 230  | 0.1907          | 0.9130   |
| 0.1865        | 2.96  | 240  | 0.4247          | 0.9130   |
| 0.3183        | 3.09  | 250  | 0.2251          | 0.8944   |
| 0.185         | 3.21  | 260  | 0.2289          | 0.8882   |
| 0.1636        | 3.33  | 270  | 0.1887          | 0.8944   |
| 0.2432        | 3.46  | 280  | 0.2055          | 0.8882   |
| 0.1518        | 3.58  | 290  | 0.2703          | 0.8944   |
| 0.2371        | 3.7   | 300  | 0.2638          | 0.8944   |
| 0.1742        | 3.83  | 310  | 0.2309          | 0.8944   |
| 0.2269        | 3.95  | 320  | 0.2208          | 0.8882   |
| 0.1404        | 4.07  | 330  | 0.2156          | 0.8820   |
| 0.1056        | 4.2   | 340  | 0.2192          | 0.9006   |
| 0.164         | 4.32  | 350  | 0.2282          | 0.9068   |
| 0.1419        | 4.44  | 360  | 0.2380          | 0.9068   |
| 0.1164        | 4.57  | 370  | 0.2438          | 0.9006   |
| 0.2167        | 4.69  | 380  | 0.2429          | 0.9006   |
| 0.1244        | 4.81  | 390  | 0.2363          | 0.8820   |
| 0.2341        | 4.94  | 400  | 0.2344          | 0.8882   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2+cpu
- Datasets 2.1.0
- Tokenizers 0.15.2