---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-base-A
  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-base-A

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: 0.0554
- Precision: 0.9085
- Recall: 0.9353
- F1: 0.9217
- Accuracy: 0.9838

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0668        | 0.13  | 257  | 0.0703          | 0.8962    | 0.8377 | 0.8660 | 0.9767   |
| 0.0511        | 0.25  | 514  | 0.0652          | 0.8348    | 0.9211 | 0.8758 | 0.9760   |
| 0.0536        | 0.38  | 771  | 0.0541          | 0.8800    | 0.8998 | 0.8898 | 0.9802   |
| 0.0392        | 0.5   | 1028 | 0.0552          | 0.8712    | 0.9226 | 0.8961 | 0.9797   |
| 0.0433        | 0.63  | 1285 | 0.0538          | 0.8711    | 0.9242 | 0.8968 | 0.9799   |
| 0.0411        | 0.75  | 1542 | 0.0502          | 0.8850    | 0.9258 | 0.9049 | 0.9807   |
| 0.0341        | 0.88  | 1799 | 0.0473          | 0.9021    | 0.9166 | 0.9093 | 0.9828   |
| 0.042         | 1.0   | 2056 | 0.0475          | 0.9111    | 0.9154 | 0.9133 | 0.9827   |
| 0.0277        | 1.13  | 2313 | 0.0486          | 0.9132    | 0.9166 | 0.9149 | 0.9828   |
| 0.026         | 1.25  | 2570 | 0.0484          | 0.9056    | 0.9250 | 0.9152 | 0.9831   |
| 0.0259        | 1.38  | 2827 | 0.0504          | 0.8986    | 0.9291 | 0.9136 | 0.9824   |
| 0.031         | 1.5   | 3084 | 0.0518          | 0.8889    | 0.9352 | 0.9115 | 0.9819   |
| 0.0269        | 1.63  | 3341 | 0.0492          | 0.8993    | 0.9338 | 0.9162 | 0.9828   |
| 0.022         | 1.75  | 3598 | 0.0496          | 0.9029    | 0.9307 | 0.9166 | 0.9831   |
| 0.0228        | 1.88  | 3855 | 0.0494          | 0.9101    | 0.9296 | 0.9198 | 0.9835   |
| 0.0166        | 2.0   | 4112 | 0.0514          | 0.9095    | 0.9316 | 0.9204 | 0.9835   |
| 0.0162        | 2.13  | 4369 | 0.0533          | 0.9041    | 0.9329 | 0.9183 | 0.9833   |
| 0.0144        | 2.26  | 4626 | 0.0545          | 0.9074    | 0.9319 | 0.9195 | 0.9835   |
| 0.0126        | 2.38  | 4883 | 0.0538          | 0.9044    | 0.9360 | 0.9199 | 0.9836   |
| 0.013         | 2.51  | 5140 | 0.0551          | 0.9085    | 0.9332 | 0.9207 | 0.9834   |
| 0.0138        | 2.63  | 5397 | 0.0565          | 0.9054    | 0.9351 | 0.9200 | 0.9833   |
| 0.0144        | 2.76  | 5654 | 0.0544          | 0.9065    | 0.9357 | 0.9209 | 0.9838   |
| 0.0136        | 2.88  | 5911 | 0.0554          | 0.9085    | 0.9353 | 0.9217 | 0.9838   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0