Instructions to use apps1/hash_nano_complete_student_model_updated_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apps1/hash_nano_complete_student_model_updated_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apps1/hash_nano_complete_student_model_updated_v2", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("apps1/hash_nano_complete_student_model_updated_v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9d5091ed53778480218f2a460cc264c3698b2614a148ae6bd38af5569bf9193
- Size of remote file:
- 5.27 kB
- SHA256:
- 8d64176ded3e893eb3173645129081f03bc47574987ac198ce04e75f938f046d
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