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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9da375a13d29c70806f8ef8b23bf0ed7b8ebc7e73434373de1931619fe86c11a
- Size of remote file:
- 14.6 kB
- SHA256:
- 1f3b9440ddebd5723aad02a9f7bda79a7a720dacd84102cfcb95d15cf251078b
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