---
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-02
  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-02

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: 0.0378
- Accuracy: 0.9877
- F1: 0.6237
- Precision: 0.7228
- Recall: 0.5485

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0944        | 0.4931 | 1000  | 0.0898          | 0.9814   | 0.0    | 0.0       | 0.0    |
| 0.0769        | 0.9862 | 2000  | 0.0686          | 0.9815   | 0.0014 | 1.0       | 0.0007 |
| 0.0607        | 1.4793 | 3000  | 0.0560          | 0.9822   | 0.1055 | 0.7889    | 0.0565 |
| 0.0535        | 1.9724 | 4000  | 0.0501          | 0.9844   | 0.3655 | 0.7509    | 0.2415 |
| 0.0466        | 2.4655 | 5000  | 0.0451          | 0.9855   | 0.4766 | 0.7195    | 0.3563 |
| 0.0441        | 2.9586 | 6000  | 0.0422          | 0.9862   | 0.5028 | 0.7586    | 0.3760 |
| 0.0391        | 3.4517 | 7000  | 0.0407          | 0.9864   | 0.5452 | 0.7205    | 0.4385 |
| 0.0372        | 3.9448 | 8000  | 0.0393          | 0.9868   | 0.5492 | 0.7506    | 0.4330 |
| 0.0336        | 4.4379 | 9000  | 0.0385          | 0.9870   | 0.5695 | 0.7416    | 0.4622 |
| 0.0337        | 4.9310 | 10000 | 0.0378          | 0.9873   | 0.5876 | 0.7361    | 0.4889 |
| 0.0297        | 5.4241 | 11000 | 0.0371          | 0.9874   | 0.6048 | 0.7266    | 0.5179 |
| 0.0296        | 5.9172 | 12000 | 0.0379          | 0.9873   | 0.5827 | 0.7472    | 0.4776 |
| 0.0263        | 6.4103 | 13000 | 0.0377          | 0.9875   | 0.6168 | 0.7152    | 0.5422 |
| 0.0272        | 6.9034 | 14000 | 0.0376          | 0.9875   | 0.6209 | 0.7090    | 0.5523 |
| 0.0234        | 7.3964 | 15000 | 0.0377          | 0.9878   | 0.6221 | 0.7277    | 0.5433 |
| 0.0243        | 7.8895 | 16000 | 0.0378          | 0.9877   | 0.6237 | 0.7228    | 0.5485 |


### Framework versions

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