| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: FakevsRealNews |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # Coding challenge |
| | The challenge involved building a fake news classifier using the huggingface library. |
| |
|
| | This final model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an fake-and-real-news dataset. The link to the dataset is https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset. |
| |
|
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0000 |
| | - Accuracy: 1.0 |
| | - F1: 1.0 |
| | - Precision: 1.0 |
| | - Recall: 1.0 |
| |
|
| | ## Model description |
| |
|
| | Finetuned Distilbert |
| | |
| | ## Training and evaluation data |
| |
|
| | The training data was split into train-dev-test in the ratio 80-10-10. |
| |
|
| | ## Training procedure |
| | The title and text of each news story was concatenated to form each datapoint. Then a model was finetuned to perform single label classification on each datapoint. The final prediction is the class with the highest probability. |
| |
|
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 3 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.0503 | 1.0 | 1956 | 0.0025 | 0.9995 | 0.9995 | 0.9995 | 0.9995 | |
| | | 0.001 | 2.0 | 3912 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0007 | 3.0 | 5868 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.18.0 |
| | - Pytorch 1.10.0+cu111 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.12.1 |
| | |
