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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:_
## 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. |