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 training_args.bin from danielsaggau/slbert_scotus_classsification: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://danielsaggau/slbert_scotus_classsification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/training_args.bin
3.44 kB
- Xet hash:
- b8bc54301ab0b911e46396f9f7476c3d41c93459fe9d25e6525d311021928c67
- Size of remote file:
- 3.44 kB
- SHA256:
- 52dedb2235ebeb45aea0c056e4ccfa2a923283e697b831b7e78b176cb34b360a
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