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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from SAVSNET/PetBERT_ICD: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/SAVSNET/PetBERT_ICD/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SAVSNET/PetBERT_ICD/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SAVSNET/PetBERT_ICD/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 89a8f56e237633af21dbdf9f3030164cc0d6c1133b387669d110453766706ee8
- Size of remote file:
- 438 MB
- SHA256:
- 796d735d71d023a448c35de6588575227709e79379e58ba85611ab2f96c459f4
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