LABEL_0 LABEL_1 LABEL_2 LABEL_3
---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-synthetic-multi-class
  results: []
---

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# deberta-v3-large-finetuned-synthetic-multi-class

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0223
- F1: 0.9961
- Precision: 0.9961
- Recall: 0.9961

## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.0278        | 1.0   | 10953 | 0.0352          | 0.9936 | 0.9935    | 0.9936 |
| 0.0143        | 2.0   | 21906 | 0.0252          | 0.9952 | 0.9952    | 0.9953 |
| 0.0014        | 3.0   | 32859 | 0.0267          | 0.9955 | 0.9955    | 0.9955 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
