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 pytorch_model.bin from BraveOni/2ch-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/BraveOni/2ch-text-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BraveOni/2ch-text-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BraveOni/2ch-text-classification/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- a622a777d7929164a2d9536033ddac2cb2e049c3144143136334db077c6bc956
- Size of remote file:
- 1.33 GB
- SHA256:
- edb1fcdb4b77026207870129c30e997b3c7a29c4792a5c9d490bd818b9bc88a1
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