---
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-01_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-01_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: 0.0382
- Accuracy: 0.9878
- F1: 0.6398
- Precision: 0.7120
- Recall: 0.5810

## 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  | 0.0902          | 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  | 0.0565          | 0.9828   | 0.1749 | 0.8221    | 0.0978 |
| 0.0535        | 1.9714 | 4000  | 0.0488          | 0.9842   | 0.3301 | 0.7895    | 0.2087 |
| 0.0473        | 2.4643 | 5000  | 0.0452          | 0.9856   | 0.4668 | 0.7510    | 0.3386 |
| 0.0436        | 2.9571 | 6000  | 0.0424          | 0.9860   | 0.4963 | 0.7467    | 0.3717 |
| 0.0389        | 3.4500 | 7000  | 0.0403          | 0.9865   | 0.5326 | 0.7503    | 0.4128 |
| 0.0376        | 3.9428 | 8000  | 0.0396          | 0.9865   | 0.5587 | 0.7128    | 0.4594 |
| 0.0339        | 4.4357 | 9000  | 0.0388          | 0.9867   | 0.5583 | 0.7351    | 0.4500 |
| 0.0337        | 4.9285 | 10000 | 0.0385          | 0.9871   | 0.5737 | 0.7467    | 0.4658 |
| 0.0295        | 5.4214 | 11000 | 0.0377          | 0.9871   | 0.6013 | 0.7109    | 0.5210 |
| 0.0305        | 5.9142 | 12000 | 0.0383          | 0.9871   | 0.5951 | 0.7187    | 0.5078 |
| 0.0254        | 6.4071 | 13000 | 0.0373          | 0.9874   | 0.6115 | 0.7197    | 0.5316 |
| 0.0273        | 6.9000 | 14000 | 0.0378          | 0.9876   | 0.6175 | 0.7268    | 0.5367 |
| 0.0228        | 7.3928 | 15000 | 0.0379          | 0.9875   | 0.6101 | 0.7257    | 0.5262 |
| 0.0235        | 7.8857 | 16000 | 0.0380          | 0.9872   | 0.6269 | 0.6861    | 0.5772 |
| 0.0208        | 8.3785 | 17000 | 0.0382          | 0.9877   | 0.6348 | 0.7077    | 0.5756 |
| 0.0204        | 8.8714 | 18000 | 0.0382          | 0.9878   | 0.6398 | 0.7120    | 0.5810 |


### Framework versions

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