---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_keras_callback
model-index:
- name: reem442/emo
  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. -->

# reem442/emo

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1355
- Validation Loss: 0.1640
- Train Accuracy: 0.943
- Train Precision: 0.9449
- Train Recall: 0.943
- Train F1: 0.9428
- Epoch: 2

## 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': True, '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 | Validation Loss | Train Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.4849     | 0.2039          | 0.933          | 0.9343          | 0.933        | 0.9326   | 0     |
| 0.1805     | 0.1706          | 0.935          | 0.9370          | 0.935        | 0.9348   | 1     |
| 0.1355     | 0.1640          | 0.943          | 0.9449          | 0.943        | 0.9428   | 2     |


### Framework versions

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