---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: piiranha-1
  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. -->

# piiranha-1

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0173
- Precision: 0.9316
- Recall: 0.9308
- F1: 0.9312
- Accuracy: 0.9944

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2984        | 0.0983 | 250  | 0.1005          | 0.5446    | 0.6111 | 0.5759 | 0.9702   |
| 0.0568        | 0.1965 | 500  | 0.0464          | 0.7895    | 0.8459 | 0.8167 | 0.9849   |
| 0.0441        | 0.2948 | 750  | 0.0400          | 0.8346    | 0.8669 | 0.8504 | 0.9869   |
| 0.0368        | 0.3931 | 1000 | 0.0320          | 0.8531    | 0.8784 | 0.8656 | 0.9891   |
| 0.0323        | 0.4914 | 1250 | 0.0293          | 0.8779    | 0.8889 | 0.8834 | 0.9903   |
| 0.0287        | 0.5896 | 1500 | 0.0269          | 0.8919    | 0.8836 | 0.8877 | 0.9907   |
| 0.0282        | 0.6879 | 1750 | 0.0276          | 0.8724    | 0.9012 | 0.8866 | 0.9903   |
| 0.0268        | 0.7862 | 2000 | 0.0254          | 0.8890    | 0.9041 | 0.8965 | 0.9914   |
| 0.0264        | 0.8844 | 2250 | 0.0236          | 0.8886    | 0.9040 | 0.8962 | 0.9915   |
| 0.0243        | 0.9827 | 2500 | 0.0232          | 0.8998    | 0.9033 | 0.9015 | 0.9917   |
| 0.0213        | 1.0810 | 2750 | 0.0237          | 0.9115    | 0.9040 | 0.9077 | 0.9923   |
| 0.0213        | 1.1792 | 3000 | 0.0222          | 0.9123    | 0.9143 | 0.9133 | 0.9925   |
| 0.0217        | 1.2775 | 3250 | 0.0222          | 0.8999    | 0.9169 | 0.9083 | 0.9924   |
| 0.0209        | 1.3758 | 3500 | 0.0212          | 0.9111    | 0.9133 | 0.9122 | 0.9928   |
| 0.0204        | 1.4741 | 3750 | 0.0206          | 0.9054    | 0.9203 | 0.9128 | 0.9926   |
| 0.0183        | 1.5723 | 4000 | 0.0212          | 0.9126    | 0.9160 | 0.9143 | 0.9927   |
| 0.0191        | 1.6706 | 4250 | 0.0192          | 0.9122    | 0.9192 | 0.9157 | 0.9929   |
| 0.0185        | 1.7689 | 4500 | 0.0195          | 0.9200    | 0.9191 | 0.9196 | 0.9932   |
| 0.018         | 1.8671 | 4750 | 0.0188          | 0.9136    | 0.9215 | 0.9176 | 0.9933   |
| 0.0183        | 1.9654 | 5000 | 0.0191          | 0.9179    | 0.9212 | 0.9196 | 0.9934   |
| 0.0147        | 2.0637 | 5250 | 0.0188          | 0.9246    | 0.9242 | 0.9244 | 0.9937   |
| 0.0149        | 2.1619 | 5500 | 0.0184          | 0.9188    | 0.9254 | 0.9221 | 0.9937   |
| 0.0143        | 2.2602 | 5750 | 0.0193          | 0.9187    | 0.9224 | 0.9205 | 0.9932   |
| 0.014         | 2.3585 | 6000 | 0.0190          | 0.9246    | 0.9280 | 0.9263 | 0.9936   |
| 0.0146        | 2.4568 | 6250 | 0.0190          | 0.9225    | 0.9277 | 0.9251 | 0.9936   |
| 0.0148        | 2.5550 | 6500 | 0.0175          | 0.9297    | 0.9306 | 0.9301 | 0.9942   |
| 0.0136        | 2.6533 | 6750 | 0.0172          | 0.9191    | 0.9329 | 0.9259 | 0.9938   |
| 0.0137        | 2.7516 | 7000 | 0.0166          | 0.9299    | 0.9312 | 0.9306 | 0.9942   |
| 0.014         | 2.8498 | 7250 | 0.0167          | 0.9285    | 0.9313 | 0.9299 | 0.9942   |
| 0.0128        | 2.9481 | 7500 | 0.0166          | 0.9271    | 0.9326 | 0.9298 | 0.9943   |
| 0.0113        | 3.0464 | 7750 | 0.0171          | 0.9286    | 0.9347 | 0.9316 | 0.9946   |
| 0.0103        | 3.1447 | 8000 | 0.0172          | 0.9284    | 0.9383 | 0.9334 | 0.9945   |
| 0.0104        | 3.2429 | 8250 | 0.0169          | 0.9312    | 0.9406 | 0.9359 | 0.9947   |
| 0.0094        | 3.3412 | 8500 | 0.0166          | 0.9368    | 0.9359 | 0.9364 | 0.9948   |
| 0.01          | 3.4395 | 8750 | 0.0166          | 0.9289    | 0.9387 | 0.9337 | 0.9944   |
| 0.0099        | 3.5377 | 9000 | 0.0162          | 0.9335    | 0.9332 | 0.9334 | 0.9947   |
| 0.0099        | 3.6360 | 9250 | 0.0160          | 0.9321    | 0.9380 | 0.9350 | 0.9947   |
| 0.01          | 3.7343 | 9500 | 0.0168          | 0.9306    | 0.9389 | 0.9347 | 0.9947   |
| 0.0101        | 3.8325 | 9750 | 0.0159          | 0.9339    | 0.9350 | 0.9344 | 0.9947   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1