Instructions to use Mikelium5/DoctorIntentClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mikelium5/DoctorIntentClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mikelium5/DoctorIntentClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mikelium5/DoctorIntentClassifier") model = AutoModelForSequenceClassification.from_pretrained("Mikelium5/DoctorIntentClassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07f9202ba84b9933b592d1f34964fe01ad8677864504eb1b2cfb402c1633d592
- Size of remote file:
- 499 MB
- SHA256:
- 4e02e99f2b6d7b37790ad7a508d0473176dc12c8b1d98e504141708869aa5c7a
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