Instructions to use danielsaggau/slbert_scotus_classsification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielsaggau/slbert_scotus_classsification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danielsaggau/slbert_scotus_classsification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danielsaggau/slbert_scotus_classsification") model = AutoModelForSequenceClassification.from_pretrained("danielsaggau/slbert_scotus_classsification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from danielsaggau/slbert_scotus_classsification: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://danielsaggau/slbert_scotus_classsification@refs/pr/1/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/pytorch_model.bin
167 MB
- Xet hash:
- 8e112d281fead3ad675534461cf7797a1f642835c2111dc4e8209fbf548da9ba
- Size of remote file:
- 167 MB
- SHA256:
- bded61ca8e74c6dbbed152d79f4887c4492ea3a67a48bbd54a8ee167f475db24
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