| | --- |
| | 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](https://huggingface.co/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: |
| | |
| | - https://huggingface.co/DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not |
| | - https://huggingface.co/DunnBC22/deberta-v3-small-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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | | 0.3598 | 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 |
