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

# aptner_deberta

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: 0.2929
- Precision: 0.5550
- Recall: 0.5835
- F1: 0.5689
- Accuracy: 0.9205

## 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: 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6136        | 0.59  | 500  | 0.3298          | 0.5007    | 0.5287 | 0.5143 | 0.9172   |
| 0.308         | 1.19  | 1000 | 0.2929          | 0.5550    | 0.5835 | 0.5689 | 0.9205   |
| 0.2428        | 1.78  | 1500 | 0.3124          | 0.5330    | 0.6192 | 0.5729 | 0.9177   |
| 0.2088        | 2.37  | 2000 | 0.3204          | 0.5356    | 0.6440 | 0.5848 | 0.9147   |
| 0.1783        | 2.97  | 2500 | 0.3319          | 0.5432    | 0.6760 | 0.6024 | 0.9149   |
| 0.1434        | 3.56  | 3000 | 0.3371          | 0.5640    | 0.6494 | 0.6037 | 0.9203   |
| 0.1352        | 4.15  | 3500 | 0.3827          | 0.5425    | 0.6249 | 0.5808 | 0.9135   |
| 0.1135        | 4.74  | 4000 | 0.3862          | 0.5360    | 0.6760 | 0.5979 | 0.9136   |
| 0.0987        | 5.34  | 4500 | 0.3978          | 0.5439    | 0.6497 | 0.5921 | 0.9141   |
| 0.0942        | 5.93  | 5000 | 0.3738          | 0.5791    | 0.6425 | 0.6091 | 0.9225   |
| 0.0746        | 6.52  | 5500 | 0.4269          | 0.5490    | 0.6479 | 0.5943 | 0.9161   |
| 0.0727        | 7.12  | 6000 | 0.4236          | 0.5579    | 0.6437 | 0.5977 | 0.9171   |
| 0.0661        | 7.71  | 6500 | 0.4239          | 0.5650    | 0.6479 | 0.6036 | 0.9200   |
| 0.0578        | 8.3   | 7000 | 0.4485          | 0.5579    | 0.6332 | 0.5932 | 0.9175   |
| 0.0505        | 8.9   | 7500 | 0.4553          | 0.5546    | 0.6353 | 0.5922 | 0.9163   |
| 0.0513        | 9.49  | 8000 | 0.4629          | 0.5587    | 0.6425 | 0.5977 | 0.9171   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1