---
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-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-00_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: 0.0381
- Accuracy: 0.9878
- F1: 0.6228
- Precision: 0.7288
- Recall: 0.5437

## 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  | 0.0895          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0764        | 0.9862 | 2000  | 0.0700          | 0.9815   | 0.0    | 0.0       | 0.0    |
| 0.0621        | 1.4793 | 3000  | 0.0567          | 0.9821   | 0.0730 | 0.8925    | 0.0381 |
| 0.0542        | 1.9724 | 4000  | 0.0497          | 0.9841   | 0.2891 | 0.8391    | 0.1747 |
| 0.0468        | 2.4655 | 5000  | 0.0465          | 0.9853   | 0.4216 | 0.7739    | 0.2897 |
| 0.0441        | 2.9586 | 6000  | 0.0435          | 0.9861   | 0.4879 | 0.7667    | 0.3578 |
| 0.0395        | 3.4517 | 7000  | 0.0417          | 0.9862   | 0.5322 | 0.7197    | 0.4222 |
| 0.0384        | 3.9448 | 8000  | 0.0401          | 0.9866   | 0.5600 | 0.7182    | 0.4589 |
| 0.0343        | 4.4379 | 9000  | 0.0393          | 0.9870   | 0.5789 | 0.7217    | 0.4833 |
| 0.0337        | 4.9310 | 10000 | 0.0378          | 0.9873   | 0.5907 | 0.7358    | 0.4934 |
| 0.0305        | 5.4241 | 11000 | 0.0375          | 0.9875   | 0.5960 | 0.7457    | 0.4963 |
| 0.0295        | 5.9172 | 12000 | 0.0378          | 0.9874   | 0.6050 | 0.7213    | 0.5210 |
| 0.0271        | 6.4103 | 13000 | 0.0376          | 0.9877   | 0.6048 | 0.7457    | 0.5087 |
| 0.0257        | 6.9034 | 14000 | 0.0379          | 0.9875   | 0.6068 | 0.7269    | 0.5208 |
| 0.0234        | 7.3964 | 15000 | 0.0377          | 0.9876   | 0.6246 | 0.7108    | 0.5571 |
| 0.0241        | 7.8895 | 16000 | 0.0381          | 0.9878   | 0.6228 | 0.7288    | 0.5437 |


### Framework versions

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