Text Classification
Transformers
PyTorch
English
deberta-v2
Trained with AutoTrain
healthcare
sdoh
social determinants of health
text-embeddings-inference
Instructions to use ClinicalNLP/SDOHv7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClinicalNLP/SDOHv7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ClinicalNLP/SDOHv7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ClinicalNLP/SDOHv7") model = AutoModelForSequenceClassification.from_pretrained("ClinicalNLP/SDOHv7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ClinicalNLP/SDOHv7: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/ClinicalNLP/SDOHv7/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://ClinicalNLP/SDOHv7@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ClinicalNLP/SDOHv7/resolve/refs%2Fpr%2F2/tokenizer.json
8.66 MB
- Xet hash:
- 1ad83d01d03e5da3c52cded7347509b5f19130fa83375df053824a0a25d6e08b
- Size of remote file:
- 8.66 MB
- SHA256:
- 4cc5a4f5b0be46bb77c12eeae3959316af6f2f567f647f0ea3af9434a4adf372
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