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

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: 0.0981
- F1: 0.8316
- Precision: 0.8202
- Recall: 0.8432
- Accuracy: 0.9856

## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.0012        | 1.0   | 734  | 0.0583          | 0.8017 | 0.7887    | 0.8151 | 0.9837   |
| 0.0002        | 2.0   | 1468 | 0.0625          | 0.8136 | 0.7975    | 0.8303 | 0.9846   |
| 0.0003        | 3.0   | 2202 | 0.0674          | 0.8111 | 0.7841    | 0.84   | 0.9838   |
| 0.0           | 4.0   | 2936 | 0.0715          | 0.8281 | 0.8155    | 0.8411 | 0.9854   |
| 0.0031        | 5.0   | 3670 | 0.0794          | 0.8297 | 0.8196    | 0.84   | 0.9856   |
| 0.0001        | 6.0   | 4404 | 0.0796          | 0.8320 | 0.8160    | 0.8486 | 0.9854   |
| 0.0           | 7.0   | 5138 | 0.0868          | 0.8262 | 0.8149    | 0.8378 | 0.9855   |
| 0.0001        | 8.0   | 5872 | 0.0911          | 0.8292 | 0.8116    | 0.8476 | 0.9857   |
| 0.0001        | 9.0   | 6606 | 0.0957          | 0.8321 | 0.8182    | 0.8465 | 0.9857   |
| 0.0001        | 10.0  | 7340 | 0.0981          | 0.8316 | 0.8202    | 0.8432 | 0.9856   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1