README.md

---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: roberta-base-Tweet_About_Disaster_Or_Not
  results: []
language:
- en
---

# roberta-base-Tweet_About_Disaster_Or_Not

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2640
- Accuracy: 0.8989
- F1: 0.7569
- Recall: 0.8211
- Precision: 0.7020

## Model description

This is a binary classification model to determine if tweet input samples are about a disaster or not.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Transformer%20Comparison/Is%20This%20Tweet%20Referring%20to%20a%20Disaster%20or%20Not%3F%20-%20RoBERTa.ipynb

### Associated Projects
This project is part of a comparison of multiple transformers. The others can be found at the following links:

- https://huggingface.co/DunnBC22/deberta-v3-small-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/albert-base-v2-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/electra-base-emotion-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/ernie-2.0-base-en-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/distilbert-base-uncased-Tweet_About_Disaster_Or_Not

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

The main limitation is the quality of the data source.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/vstepanenko/disaster-tweets

_Input Word Length By Class:_

![Length of Input Text (in Words) By Class](https://github.com/DunnBC22/NLP_Projects/raw/main/Binary%20Classification/Transformer%20Comparison/Images/Tweet%20Word%20Lengths%20By%20Class.png)

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.372         | 1.0   | 143  | 0.3067          | 0.8690   | 0.7205 | 0.8807 | 0.6095    |
| 0.2356        | 2.0   | 286  | 0.2640          | 0.8989   | 0.7569 | 0.8211 | 0.7020    |
| 0.165         | 3.0   | 429  | 0.3029          | 0.8997   | 0.7635 | 0.8440 | 0.6970    |
| 0.1118        | 4.0   | 572  | 0.3256          | 0.8971   | 0.7578 | 0.8394 | 0.6906    |
| 0.0766        | 5.0   | 715  | 0.3733          | 0.9024   | 0.7711 | 0.8578 | 0.7004    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.12.1


## License Notice
This model is a fine-tuned derivative of a pretrained model.
Users must comply with the original model license.


## Dataset Notice
This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.