Instructions to use dima806/medium-article-titles-engagement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/medium-article-titles-engagement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/medium-article-titles-engagement")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/medium-article-titles-engagement") model = AutoModelForSequenceClassification.from_pretrained("dima806/medium-article-titles-engagement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dima806/medium-article-titles-engagement: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/dima806/medium-article-titles-engagement/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dima806/medium-article-titles-engagement/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dima806/medium-article-titles-engagement/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- eadfdff5abfd106e0893d48f2626ea81f1d9a9a09487831a592f0011bf032482
- Size of remote file:
- 263 MB
- SHA256:
- db5195e284b7d153a6a9c939be2c6bcbc6a1423852fab2febadbfed2986c28c2
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