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:
- 01c0ccf21bed92cba2955cfc7c9871f85591f6aae30f47c9cf1ba020d9247716
- Size of remote file:
- 501 MB
- SHA256:
- b2af9c98f6fffb979fc579c86f9bb8742c32fca3e5d62f3dac9c9bf6e183a349
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.