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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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# Identify Clickbait Articles |
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This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on a synthetic dataset with 65% factual article titles and 35% clickbait articles. |
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Built to demonstrate the use of synthetic data following, see the article [here](https://towardsdatascience.com/fine-tune-smaller-transformer-models-text-classification-77cbbd3bf02b). |
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## Model description |
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Built to identify factual vs clickbait titles. |
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## Intended uses & limitations |
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Use it on any title to understand how the model is interpreting the title, whether it is factual or clickbait. |
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Go ahead and try a few of your own. |
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Here are a few examples: |
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**Title:** A Comprehensive Guide for Getting Started with Hugging Face |
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**Output:** Factual |
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**Title:** OpenAI GPT-4o: The New Best AI Model in the World. Like in the Movies. For Free |
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**Output:** Clickbait |
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**Title:** GPT4 Omni — So much more than just a voice assistant |
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**Output:** Clickbait |
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**Title:** Building Vector Databases with FastAPI and ChromaDB |
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**Output:** Factual |
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## Training and evaluation data |
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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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## 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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- 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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### 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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