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SDOHv7
ClinicalNLP/SDOHv7
SDOHv7 is a text classification model from ClinicalNLP. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
- Problem type: Multi-class Classification - Model ID: 3701198597 - CO2 Emissions (in grams): 0.0113
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- deberta-v2
- Access
- Public
- Created
- Feb 24, 2023
- Updated
- Apr 27, 2024
- SHA
- a446dfb6
Try a prompt
Datasets
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3701198597
- CO2 Emissions (in grams): 0.0113
Validation Metrics
- Loss: 0.057
- Accuracy: 0.990
- Macro F1: 0.990
- Micro F1: 0.990
- Weighted F1: 0.990
- Macro Precision: 0.990
- Micro Precision: 0.990
- Weighted Precision: 0.991
- Macro Recall: 0.990
- Micro Recall: 0.990
- Weighted Recall: 0.990
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/reachosen/autotrain-sdohv7-3701198597
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True)
inputs = tokenizer("The Patient is homeless", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- apache-2.0
- Languages
- en
- Created
- Feb 24, 2023
- Updated
- Apr 27, 2024