---
license: mit
base_model: philschmid/deberta-v3-xsmall-emotion
tags:
- generated_from_keras_callback
model-index:
- name: ruba2ksa/emore
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# ruba2ksa/emore

This model is a fine-tuned version of [philschmid/deberta-v3-xsmall-emotion](https://huggingface.co/philschmid/deberta-v3-xsmall-emotion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2179
- Train Accuracy: 0.942
- Validation Loss: 0.1882
- Validation Accuracy: 0.9420
- Train Precision: 0.9441
- Train Recall: 0.942
- Train F1: 0.9418
- Epoch: 0

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.2179     | 0.942          | 0.1882          | 0.9420              | 0.9441          | 0.942        | 0.9418   | 0     |


### Framework versions

- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2