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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: albert-base-v2-Tweet_About_Disaster_Or_Not |
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results: [] |
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language: |
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- en |
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--- |
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# albert-base-v2-Tweet_About_Disaster_Or_Not |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2899 |
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- Accuracy: 0.8989 |
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- F1: 0.7784 |
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- Recall: 0.8523 |
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- Precision: 0.7163 |
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## Model description |
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This is a binary classification model to determine if tweet input samples are about a disaster or not. |
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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 |
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### Associated Projects |
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This project is part of a comparison of multiple transformers. The others can be found at the following links: |
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- https://huggingface.co/DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not |
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- https://huggingface.co/DunnBC22/deberta-v3-small-Tweet_About_Disaster_Or_Not |
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- https://huggingface.co/DunnBC22/electra-base-emotion-Tweet_About_Disaster_Or_Not |
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- https://huggingface.co/DunnBC22/ernie-2.0-base-en-Tweet_About_Disaster_Or_Not |
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- https://huggingface.co/DunnBC22/distilbert-base-uncased-Tweet_About_Disaster_Or_Not |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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The main limitation is the quality of the data source. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/vstepanenko/disaster-tweets |
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_Input Word Length By Class:_ |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.3598 | 1.0 | 143 | 0.3025 | 0.8795 | 0.7495 | 0.8650 | 0.6613 | |
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| 0.234 | 2.0 | 286 | 0.2899 | 0.8989 | 0.7784 | 0.8523 | 0.7163 | |
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| 0.1557 | 3.0 | 429 | 0.3424 | 0.9156 | 0.7904 | 0.7637 | 0.8190 | |
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| 0.0871 | 4.0 | 572 | 0.4189 | 0.9182 | 0.7901 | 0.7384 | 0.8495 | |
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| 0.0517 | 5.0 | 715 | 0.4396 | 0.9200 | 0.8043 | 0.7890 | 0.8202 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.12.1 |
