Instructions to use Stremie/bert-base-uncased-clickbait-keywords with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Stremie/bert-base-uncased-clickbait-keywords with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Stremie/bert-base-uncased-clickbait-keywords")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Stremie/bert-base-uncased-clickbait-keywords") model = AutoModelForSequenceClassification.from_pretrained("Stremie/bert-base-uncased-clickbait-keywords") - Notebooks
- Google Colab
- Kaggle
Create README.md
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README.md
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This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data.
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