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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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model-index: |
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- name: distilbert-base-uncased_fakenews_identification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-uncased_fakenews_identification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the below dataset. |
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https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0059 |
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- Accuracy: 0.999 |
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- F1: 0.9990 |
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## Label Description |
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LABEL_0 - Fake News |
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LABEL_1 - Real News |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 32 |
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- eval_batch_size: 32 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.0014 | 1.0 | 1000 | 0.0208 | 0.9965 | 0.9965 | |
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| 0.0006 | 2.0 | 2000 | 0.0041 | 0.9994 | 0.9994 | |
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| 0.0006 | 3.0 | 3000 | 0.0044 | 0.9992 | 0.9993 | |
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| 0.0 | 4.0 | 4000 | 0.0059 | 0.999 | 0.9990 | |
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### Framework versions |
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- Transformers 4.16.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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