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:
- fa0f3e7917815ac9b63b77827465589fe41ffb519cbbb8376b4e6ae59b1a4fea
- Size of remote file:
- 3.52 kB
- SHA256:
- 8927ceaf37476b477000a14227235382b9936580ab93eea17858ec17276ba07e
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