---
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-01
  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-01

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: 0.0398
- Accuracy: 0.9878
- F1: 0.6321
- Precision: 0.7134
- Recall: 0.5675

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0936        | 0.4931 | 1000  | 0.0882          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0754        | 0.9862 | 2000  | 0.0682          | 0.9815   | 0.0006 | 0.5714    | 0.0003 |
| 0.0614        | 1.4793 | 3000  | 0.0561          | 0.9824   | 0.1463 | 0.7360    | 0.0812 |
| 0.053         | 1.9724 | 4000  | 0.0500          | 0.9842   | 0.3207 | 0.7946    | 0.2009 |
| 0.0477        | 2.4655 | 5000  | 0.0463          | 0.9853   | 0.4453 | 0.7381    | 0.3189 |
| 0.0445        | 2.9586 | 6000  | 0.0435          | 0.9859   | 0.4832 | 0.7548    | 0.3553 |
| 0.0385        | 3.4517 | 7000  | 0.0410          | 0.9865   | 0.5406 | 0.7356    | 0.4273 |
| 0.0384        | 3.9448 | 8000  | 0.0400          | 0.9867   | 0.5643 | 0.7201    | 0.4639 |
| 0.0347        | 4.4379 | 9000  | 0.0386          | 0.9870   | 0.5796 | 0.7235    | 0.4834 |
| 0.0336        | 4.9310 | 10000 | 0.0381          | 0.9873   | 0.5971 | 0.7223    | 0.5089 |
| 0.0299        | 5.4241 | 11000 | 0.0374          | 0.9875   | 0.5941 | 0.7483    | 0.4926 |
| 0.0299        | 5.9172 | 12000 | 0.0375          | 0.9874   | 0.5978 | 0.7279    | 0.5071 |
| 0.0265        | 6.4103 | 13000 | 0.0377          | 0.9874   | 0.6035 | 0.7218    | 0.5185 |
| 0.0271        | 6.9034 | 14000 | 0.0379          | 0.9872   | 0.6061 | 0.7061    | 0.5309 |
| 0.0229        | 7.3964 | 15000 | 0.0373          | 0.9877   | 0.6254 | 0.7162    | 0.5550 |
| 0.0245        | 7.8895 | 16000 | 0.0378          | 0.9879   | 0.6295 | 0.7266    | 0.5553 |
| 0.0205        | 8.3826 | 17000 | 0.0376          | 0.9876   | 0.6300 | 0.7041    | 0.5701 |
| 0.0213        | 8.8757 | 18000 | 0.0385          | 0.9878   | 0.6303 | 0.7156    | 0.5631 |
| 0.0183        | 9.3688 | 19000 | 0.0389          | 0.9878   | 0.6300 | 0.7164    | 0.5621 |
| 0.0182        | 9.8619 | 20000 | 0.0398          | 0.9878   | 0.6321 | 0.7134    | 0.5675 |


### Framework versions

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