Instructions to use BraveOni/2ch-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BraveOni/2ch-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BraveOni/2ch-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BraveOni/2ch-text-classification") model = AutoModelForSequenceClassification.from_pretrained("BraveOni/2ch-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_input.pkl from BraveOni/2ch-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 2.85 kB
-
https://huggingface.co/BraveOni/2ch-text-classification/resolve/refs%2Fpr%2F1/sample_input.pkl
- Command line
-
hf download hf://BraveOni/2ch-text-classification@refs/pr/1/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/BraveOni/2ch-text-classification/resolve/refs%2Fpr%2F1/sample_input.pkl
2.85 kB
- Xet hash:
- 98f937f010f8369fd19e86f337f82f6bfe051bc23d34faf7f5adcdc4c0ce3bc3
- Size of remote file:
- 2.85 kB
- SHA256:
- 4430482114ba0bf1fedee99f64da4b5a3376237d0146a24b25af5a407f75b912
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