---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: deberta-v3-ft-news-sentiment-analisys
  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. -->

# deberta-v3-ft-news-sentiment-analisys

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: 0.1107
- Precision: 0.9780
- Recall: 0.9780
- Accuracy: 0.9780
- F1: 0.9780

## 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: 32
- eval_batch_size: 64
- 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 | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 64   | 0.4196          | 0.7885    | 0.7885 | 0.7885   | 0.7885 |
| No log        | 2.0   | 128  | 0.2166          | 0.9515    | 0.9515 | 0.9515   | 0.9515 |
| No log        | 3.0   | 192  | 0.1431          | 0.9604    | 0.9604 | 0.9604   | 0.9604 |
| No log        | 4.0   | 256  | 0.1107          | 0.9780    | 0.9780 | 0.9780   | 0.9780 |
| No log        | 5.0   | 320  | 0.1292          | 0.9692    | 0.9692 | 0.9692   | 0.9692 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0