Instructions to use dima806/data-science-article-titles-engagement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/data-science-article-titles-engagement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/data-science-article-titles-engagement")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/data-science-article-titles-engagement") model = AutoModelForSequenceClassification.from_pretrained("dima806/data-science-article-titles-engagement") - Notebooks
- Google Colab
- Kaggle
See https://www.kaggle.com/code/dima806/medium-ds-article-engaging-titles for more details.
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Model tree for dima806/data-science-article-titles-engagement
Base model
distilbert/distilbert-base-cased