---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: doc-topic-model_eval-03_train-04
  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. -->

# doc-topic-model_eval-03_train-04

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: 0.0381
- Accuracy: 0.9878
- F1: 0.6369
- Precision: 0.7080
- Recall: 0.5788

## 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: 4
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0934        | 0.4929 | 1000  | 0.0901          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0778        | 0.9857 | 2000  | 0.0701          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0618        | 1.4786 | 3000  | 0.0566          | 0.9828   | 0.1667 | 0.8182    | 0.0928 |
| 0.0535        | 1.9714 | 4000  | 0.0490          | 0.9844   | 0.3358 | 0.7954    | 0.2128 |
| 0.0473        | 2.4643 | 5000  | 0.0453          | 0.9855   | 0.4636 | 0.7390    | 0.3377 |
| 0.0436        | 2.9571 | 6000  | 0.0424          | 0.9860   | 0.4959 | 0.7477    | 0.3710 |
| 0.0389        | 3.4500 | 7000  | 0.0405          | 0.9865   | 0.5302 | 0.7462    | 0.4111 |
| 0.0376        | 3.9428 | 8000  | 0.0398          | 0.9865   | 0.5573 | 0.7123    | 0.4577 |
| 0.0339        | 4.4357 | 9000  | 0.0390          | 0.9868   | 0.5566 | 0.7355    | 0.4478 |
| 0.0337        | 4.9285 | 10000 | 0.0381          | 0.9871   | 0.5750 | 0.7445    | 0.4683 |
| 0.0295        | 5.4214 | 11000 | 0.0375          | 0.9873   | 0.6044 | 0.7152    | 0.5234 |
| 0.0305        | 5.9142 | 12000 | 0.0383          | 0.9872   | 0.5948 | 0.7223    | 0.5055 |
| 0.0254        | 6.4071 | 13000 | 0.0371          | 0.9875   | 0.6109 | 0.7222    | 0.5293 |
| 0.0273        | 6.9000 | 14000 | 0.0374          | 0.9877   | 0.6181 | 0.7280    | 0.5370 |
| 0.0228        | 7.3928 | 15000 | 0.0376          | 0.9876   | 0.6113 | 0.7279    | 0.5269 |
| 0.0235        | 7.8857 | 16000 | 0.0376          | 0.9874   | 0.6297 | 0.6903    | 0.5789 |
| 0.0208        | 8.3785 | 17000 | 0.0377          | 0.9876   | 0.6323 | 0.7035    | 0.5742 |
| 0.0204        | 8.8714 | 18000 | 0.0381          | 0.9878   | 0.6369 | 0.7080    | 0.5788 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1