Instructions to use waddledee/158b_news_title_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waddledee/158b_news_title_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="waddledee/158b_news_title_classification", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("waddledee/158b_news_title_classification", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("waddledee/158b_news_title_classification", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from waddledee/158b_news_title_classification: direct link, hf CLI and curl.
- Browser
- Download file 914 kB
-
https://huggingface.co/waddledee/158b_news_title_classification/resolve/main/tokenizer.model
- Command line
-
hf download hf://waddledee/158b_news_title_classification/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/waddledee/158b_news_title_classification/resolve/main/tokenizer.model
914 kB
- Xet hash:
- 328196d4143bb2c1fc5f4cda19ba28f1bfefeacdc6d2e94b64ae308b49ababf9
- Size of remote file:
- 914 kB
- SHA256:
- c877c5ca885bad5c19d1b1706a2703f8b30de90f03c1f834f8bdb9faf79821e8
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