Text Classification
Transformers
PyTorch
Safetensors
English
bert
biology
medical
veterinary
clinical
text-embeddings-inference
Instructions to use SAVSNET/PetBERT_ICD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAVSNET/PetBERT_ICD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SAVSNET/PetBERT_ICD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SAVSNET/PetBERT_ICD") model = AutoModelForSequenceClassification.from_pretrained("SAVSNET/PetBERT_ICD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88076446f63d000f390e81f487a2e289fb3e3d3473374fcb5d82e72b35add473
- Size of remote file:
- 627 Bytes
- SHA256:
- a275e608dcb0c497024b05608e3705d633b9b9c98a81b1ddcc59e8c31427f50f
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