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--- |
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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: ernie-2.0-base-en-Tweet_About_Disaster_Or_Not |
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results: [] |
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language: |
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- en |
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license: cc |
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--- |
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# ernie-2.0-base-en-Tweet_About_Disaster_Or_Not |
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This model is a fine-tuned version of [nghuyong/ernie-2.0-base-en](https://huggingface.co/nghuyong/ernie-2.0-base-en) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2292 |
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- Accuracy: 0.9156 |
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- F1: 0.7876 |
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- Recall: 0.8436 |
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- Precision: 0.7386 |
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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-%20ERNIE.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/albert-base-v2-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/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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| 0.347 | 1.0 | 143 | 0.2663 | 0.8777 | 0.7342 | 0.9100 | 0.6154 | |
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| 0.2192 | 2.0 | 286 | 0.2292 | 0.9156 | 0.7876 | 0.8436 | 0.7386 | |
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| 0.132 | 3.0 | 429 | 0.2629 | 0.9129 | 0.7843 | 0.8531 | 0.7258 | |
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| 0.0767 | 4.0 | 572 | 0.3266 | 0.9120 | 0.7807 | 0.8436 | 0.7265 | |
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| 0.0532 | 5.0 | 715 | 0.3622 | 0.9120 | 0.7788 | 0.8341 | 0.7303 | |
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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 |
