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:
- 8d4309b0b0d24d39e0b59c1de1833c9b58f6f5b6a7a4a10bfac6bb51b64babf8
- Size of remote file:
- 1.19 kB
- SHA256:
- 30bce00fe7afeaed1e196ece66d30f5324e887ddbe934ac7a8c4b6d7a07da3f6
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