Instructions to use rcade/cohortb_model_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcade/cohortb_model_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcade/cohortb_model_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcade/cohortb_model_learning") model = AutoModelForSequenceClassification.from_pretrained("rcade/cohortb_model_learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e599688f11e05f94fa582a03c7e97712a424c3800fd0727f5f6b1bcf675d0d7
- Size of remote file:
- 5.18 kB
- SHA256:
- 1daaf4e56c7e9d15c4d77375032a9e0605c6f002b2b215922cced720d65a99cf
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