---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- multi-label text classification
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta_classifier
  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. -->

# deberta_classifier

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Accuracy: 0.9955
- F1: 0.6062
- Precision: 0.8225
- Recall: 0.4799

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6159        | 0.1169 | 100  | 0.5955          | 0.7621   | 0.0288 | 0.0148    | 0.4839 |
| 0.3536        | 0.2338 | 200  | 0.3085          | 0.9753   | 0.1645 | 0.1091    | 0.3341 |
| 0.1166        | 0.3507 | 300  | 0.0917          | 0.9931   | 0.4124 | 0.5429    | 0.3325 |
| 0.0456        | 0.4676 | 400  | 0.0375          | 0.9931   | 0.4124 | 0.5429    | 0.3325 |
| 0.0308        | 0.5845 | 500  | 0.0270          | 0.9931   | 0.4124 | 0.5429    | 0.3325 |
| 0.0249        | 0.7013 | 600  | 0.0234          | 0.9942   | 0.4459 | 0.7407    | 0.3189 |
| 0.0231        | 0.8182 | 700  | 0.0211          | 0.9953   | 0.5983 | 0.7970    | 0.4789 |
| 0.0213        | 0.9351 | 800  | 0.0196          | 0.9953   | 0.5989 | 0.7998    | 0.4787 |
| 0.0197        | 1.0520 | 900  | 0.0187          | 0.9954   | 0.6029 | 0.8168    | 0.4778 |
| 0.0205        | 1.1689 | 1000 | 0.0183          | 0.9955   | 0.6062 | 0.8225    | 0.4799 |
| 0.017         | 1.2858 | 1100 | 0.0175          | 0.9959   | 0.6610 | 0.8426    | 0.5437 |
| 0.018         | 1.4027 | 1200 | 0.0170          | 0.9960   | 0.6653 | 0.8685    | 0.5392 |
| 0.0177        | 1.5196 | 1300 | 0.0165          | 0.9961   | 0.6722 | 0.8732    | 0.5464 |
| 0.0189        | 1.6365 | 1400 | 0.0162          | 0.9962   | 0.6752 | 0.8910    | 0.5435 |
| 0.0179        | 1.7534 | 1500 | 0.0159          | 0.9964   | 0.6898 | 0.9151    | 0.5535 |
| 0.0169        | 1.8703 | 1600 | 0.0158          | 0.9964   | 0.6928 | 0.9030    | 0.5620 |
| 0.0172        | 1.9871 | 1700 | 0.0156          | 0.9964   | 0.6909 | 0.9130    | 0.5557 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1