Instructions to use darkdwine/news_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darkdwine/news_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="darkdwine/news_classification_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("darkdwine/news_classification_model") model = AutoModelForSequenceClassification.from_pretrained("darkdwine/news_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from darkdwine/news_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/darkdwine/news_classification_model/resolve/main/tf_model.h5
- Command line
-
hf download hf://darkdwine/news_classification_model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/darkdwine/news_classification_model/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 57c01bd4885708029d9c9833c5d1839ce017c304ed9b2b6661eaee21f9d796d5
- Size of remote file:
- 268 MB
- SHA256:
- a5d973f02894fd53fe2a6e7dae12820b1ffa668bdcc65233933d81ebcff30016
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