Text Classification
Transformers
PyTorch
English
bert
FHIR
Healthcare
Questions
text-embeddings-inference
Instructions to use fhirfly/medicalquestions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fhirfly/medicalquestions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fhirfly/medicalquestions")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fhirfly/medicalquestions") model = AutoModelForSequenceClassification.from_pretrained("fhirfly/medicalquestions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fhirfly/medicalquestions: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/fhirfly/medicalquestions/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fhirfly/medicalquestions/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fhirfly/medicalquestions/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- b428da8620d76f4e6f6c04eb12ecc4fd2d62c8ab0481d9ec3f16f82ab0599c6d
- Size of remote file:
- 433 MB
- SHA256:
- 2aea71a69545e5b3b307ffeaa43add64752883ce0589d7ba15fd013016f4d547
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