| | --- |
| | 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. --> |
| |
|
| | # FakevsRealNews |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on "Fake and real news dataset" dataset. |
| |
|
| | Link to Dataset : https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset |
| |
|
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0006 |
| | - Accuracy: 0.6309 |
| | - F1: 0.7677 |
| | - Precision: 0.6233 |
| | - Recall: 0.9992 |
| |
|
| | ## Model description |
| |
|
| | Finetuned Distilbert |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | The data was split into train-dev-test sets on a ratio of 80:10:10 |
| |
|
| | ## Training procedure |
| |
|
| | ### 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: 5 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.0176 | 1.0 | 1956 | 0.0009 | 0.9616 | 0.9695 | 0.9409 | 1.0 | |
| | | 0.0014 | 2.0 | 3912 | 0.0015 | 0.9864 | 0.9890 | 0.9783 | 1.0 | |
| | | 0.0011 | 3.0 | 5868 | 0.0008 | 0.7611 | 0.8363 | 0.7188 | 0.9996 | |
| | | 0.0008 | 4.0 | 7824 | 0.0008 | 0.7872 | 0.8514 | 0.7418 | 0.9992 | |
| | | 0.0006 | 5.0 | 9780 | 0.0006 | 0.6309 | 0.7677 | 0.6233 | 0.9992 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.18.0 |
| | - Pytorch 1.10.0+cu111 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.12.1 |
| | |
