metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: albert-base-v2-Tweet_About_Disaster_Or_Not
    results: []
language:
  - en

albert-base-v2-Tweet_About_Disaster_Or_Not

This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2899
Accuracy: 0.8989
F1: 0.7784
Recall: 0.8523
Precision: 0.7163

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-%20ALBERT.ipynb

Associated Projects

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

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
0.3598	1.0	143	0.3025	0.8795	0.7495	0.8650	0.6613
0.234	2.0	286	0.2899	0.8989	0.7784	0.8523	0.7163
0.1557	3.0	429	0.3424	0.9156	0.7904	0.7637	0.8190
0.0871	4.0	572	0.4189	0.9182	0.7901	0.7384	0.8495
0.0517	5.0	715	0.4396	0.9200	0.8043	0.7890	0.8202

Framework versions

Transformers 4.26.1
Pytorch 1.13.1
Datasets 2.9.0
Tokenizers 0.12.1