Instructions to use tmills/roberta_sfda_sharpseed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmills/roberta_sfda_sharpseed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmills/roberta_sfda_sharpseed")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmills/roberta_sfda_sharpseed") model = AutoModelForSequenceClassification.from_pretrained("tmills/roberta_sfda_sharpseed") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 015d7020212dc9ded8a3db801dc916356139ad93ba9476a4d0a03aa763a6a3f0
- Size of remote file:
- 499 MB
- SHA256:
- 70082c9ee71956a63e022004bd1f7c8f910c81dd48646b216589417707faab9d
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