---
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-04_train-03
  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-04_train-03

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: 0.9878
- F1: 0.6242
- Precision: 0.7287
- Recall: 0.5459

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0935        | 0.4931 | 1000  | 0.0894          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0764        | 0.9862 | 2000  | 0.0699          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0621        | 1.4793 | 3000  | 0.0567          | 0.9821   | 0.0732 | 0.8832    | 0.0382 |
| 0.0542        | 1.9724 | 4000  | 0.0495          | 0.9840   | 0.2881 | 0.8175    | 0.1749 |
| 0.0468        | 2.4655 | 5000  | 0.0465          | 0.9854   | 0.4302 | 0.7764    | 0.2975 |
| 0.0441        | 2.9586 | 6000  | 0.0433          | 0.9861   | 0.4928 | 0.7613    | 0.3643 |
| 0.0395        | 3.4517 | 7000  | 0.0415          | 0.9862   | 0.5333 | 0.7144    | 0.4254 |
| 0.0384        | 3.9448 | 8000  | 0.0397          | 0.9868   | 0.5643 | 0.7222    | 0.4631 |
| 0.0343        | 4.4379 | 9000  | 0.0389          | 0.9870   | 0.5808 | 0.7188    | 0.4873 |
| 0.0337        | 4.9310 | 10000 | 0.0376          | 0.9875   | 0.5954 | 0.7393    | 0.4985 |
| 0.0305        | 5.4241 | 11000 | 0.0371          | 0.9876   | 0.6006 | 0.7449    | 0.5032 |
| 0.0295        | 5.9172 | 12000 | 0.0375          | 0.9876   | 0.6106 | 0.7257    | 0.5270 |
| 0.0271        | 6.4103 | 13000 | 0.0371          | 0.9878   | 0.6096 | 0.7493    | 0.5138 |
| 0.0257        | 6.9034 | 14000 | 0.0373          | 0.9878   | 0.6171 | 0.7325    | 0.5332 |
| 0.0234        | 7.3964 | 15000 | 0.0373          | 0.9877   | 0.6277 | 0.7127    | 0.5609 |
| 0.0241        | 7.8895 | 16000 | 0.0378          | 0.9878   | 0.6242 | 0.7287    | 0.5459 |


### Framework versions

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