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--- |
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license: apache-2.0 |
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base_model: albert/albert-base-v2 |
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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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- precision |
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- recall |
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model-index: |
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- name: classify-clickbait-titll |
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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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# classify-clickbait-titll |
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This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0173 |
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- Accuracy: 0.9951 |
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- F1: 0.9951 |
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- Precision: 0.9951 |
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- Recall: 0.9951 |
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- Accuracy Label Clickbait: 0.9866 |
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- Accuracy Label Factual: 1.0 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Clickbait | Accuracy Label Factual | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------------------:|:----------------------:| |
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| 0.0561 | 0.4831 | 100 | 0.0488 | 0.9927 | 0.9927 | 0.9927 | 0.9927 | 0.9933 | 0.9923 | |
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| 0.0037 | 0.9662 | 200 | 0.0097 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9967 | 1.0 | |
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| 0.0012 | 1.4493 | 300 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0012 | 1.9324 | 400 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0433 | 2.4155 | 500 | 0.0020 | 0.9988 | 0.9988 | 0.9988 | 0.9988 | 0.9967 | 1.0 | |
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| 0.0003 | 2.8986 | 600 | 0.0167 | 0.9951 | 0.9951 | 0.9951 | 0.9951 | 0.9866 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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