---
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-00_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-00_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: 0.0393
- Accuracy: 0.9874
- F1: 0.6311
- Precision: 0.6927
- Recall: 0.5796

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0932        | 0.4931 | 1000  | 0.0863          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0742        | 0.9862 | 2000  | 0.0662          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0596        | 1.4793 | 3000  | 0.0549          | 0.9826   | 0.1370 | 0.8442    | 0.0746 |
| 0.053         | 1.9724 | 4000  | 0.0491          | 0.9849   | 0.3899 | 0.7714    | 0.2608 |
| 0.0467        | 2.4655 | 5000  | 0.0452          | 0.9857   | 0.4527 | 0.7816    | 0.3186 |
| 0.044         | 2.9586 | 6000  | 0.0427          | 0.9864   | 0.5022 | 0.7753    | 0.3714 |
| 0.039         | 3.4517 | 7000  | 0.0409          | 0.9867   | 0.5505 | 0.7410    | 0.4379 |
| 0.037         | 3.9448 | 8000  | 0.0390          | 0.9870   | 0.5589 | 0.7507    | 0.4452 |
| 0.0337        | 4.4379 | 9000  | 0.0383          | 0.9875   | 0.5772 | 0.7737    | 0.4603 |
| 0.0337        | 4.9310 | 10000 | 0.0375          | 0.9875   | 0.5917 | 0.7530    | 0.4873 |
| 0.0293        | 5.4241 | 11000 | 0.0375          | 0.9877   | 0.6105 | 0.7380    | 0.5205 |
| 0.0297        | 5.9172 | 12000 | 0.0375          | 0.9876   | 0.6050 | 0.7390    | 0.5122 |
| 0.0263        | 6.4103 | 13000 | 0.0372          | 0.9879   | 0.6160 | 0.7472    | 0.5240 |
| 0.0265        | 6.9034 | 14000 | 0.0377          | 0.9876   | 0.6178 | 0.7208    | 0.5406 |
| 0.0235        | 7.3964 | 15000 | 0.0378          | 0.9878   | 0.6238 | 0.7303    | 0.5444 |
| 0.0237        | 7.8895 | 16000 | 0.0379          | 0.9878   | 0.6255 | 0.7242    | 0.5505 |
| 0.0205        | 8.3826 | 17000 | 0.0383          | 0.9878   | 0.6324 | 0.7159    | 0.5664 |
| 0.0208        | 8.8757 | 18000 | 0.0393          | 0.9874   | 0.6311 | 0.6927    | 0.5796 |


### Framework versions

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