---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deeva-modcat-seqclass-deberta-v1
  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. -->

# deeva-modcat-seqclass-deberta-v1

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6435
- Accuracy: 0.7161
- F1: 0.2922
- Precision: 0.1808
- Recall: 0.7619

## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.18  | 2    | 0.7148          | 0.4139   | 0.0476 | 0.0272    | 0.1905 |
| No log        | 0.36  | 4    | 0.7027          | 0.4835   | 0.0408 | 0.0238    | 0.1429 |
| No log        | 0.55  | 6    | 0.6917          | 0.5586   | 0.0474 | 0.0284    | 0.1429 |
| No log        | 0.73  | 8    | 0.6817          | 0.5604   | 0.0476 | 0.0286    | 0.1429 |
| No log        | 0.91  | 10   | 0.6727          | 0.5623   | 0.0478 | 0.0287    | 0.1429 |
| No log        | 1.09  | 12   | 0.6648          | 0.6374   | 0.0571 | 0.0357    | 0.1429 |
| No log        | 1.27  | 14   | 0.6578          | 0.6374   | 0.0571 | 0.0357    | 0.1429 |
| No log        | 1.45  | 16   | 0.6521          | 0.6355   | 0.0569 | 0.0355    | 0.1429 |
| No log        | 1.64  | 18   | 0.6477          | 0.6392   | 0.1005 | 0.0621    | 0.2619 |
| No log        | 1.82  | 20   | 0.6448          | 0.7015   | 0.2419 | 0.1503    | 0.6190 |
| No log        | 2.0   | 22   | 0.6435          | 0.7161   | 0.2922 | 0.1808    | 0.7619 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3