Text Classification
Transformers
PyTorch
English
bert
medical
cardiology
emergency-medicine
classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use monajm36/ohca-classifier-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monajm36/ohca-classifier-v9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="monajm36/ohca-classifier-v9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("monajm36/ohca-classifier-v9") model = AutoModelForSequenceClassification.from_pretrained("monajm36/ohca-classifier-v9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c44a4883f340f625f514da741ef401759268a041e5a88630c5e9412fdf957a88
- Size of remote file:
- 438 MB
- SHA256:
- 5c6e7ff05e4e2f8ceaa71ab2bd7d5d286c0f33c41f45e89b83d16bb005cc8934
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