transformers tensorboard safetensors deberta-v2 text-classification generated_from_trainer dataset:emotion base_model:microsoft/deberta-v3-base base_model:finetune:microsoft/deberta-v3-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/deberta-v3-base
datasets:
- emotion
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: deberta-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: f1
      value: 0.9352884200987154
      name: F1
---

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# deberta-emotion

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1592
- F1: 0.9353

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1784        | 1.0   | 250  | 0.1746          | 0.9325 |
| 0.1273        | 2.0   | 500  | 0.1672          | 0.9332 |
| 0.1008        | 3.0   | 750  | 0.1592          | 0.9353 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
