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:
- e86e61dfe9a3b85cf82c05d1c9cf4b95ebfe3d997acd7fb08216ad268841698a
- Size of remote file:
- 438 MB
- SHA256:
- f72c0a1e42045f682f2250c703fb1d2fb04d1d8790c18c662780337d184cdb9b
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