---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_keras_callback
model-index:
- name: Maxnotmarx/diaster_detection_model
  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. -->

# Maxnotmarx/diaster_detection_model

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1107
- Train Accuracy: 0.9695
- Epoch: 7

## 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': 2375, '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 | Epoch |
|:----------:|:--------------:|:-----:|
| 0.4378     | 0.8814         | 0     |
| 0.3348     | 0.9232         | 1     |
| 0.2532     | 0.9518         | 2     |
| 0.1790     | 0.9639         | 3     |
| 0.1294     | 0.9695         | 4     |
| 0.1132     | 0.9696         | 5     |
| 0.1130     | 0.9695         | 6     |
| 0.1107     | 0.9695         | 7     |


### Framework versions

- Transformers 4.44.0
- TensorFlow 2.16.1
- Datasets 2.21.0
- Tokenizers 0.19.1