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--- |
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tags: |
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- autotrain |
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- text-classification |
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- healthcare |
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- sdoh |
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- social determinants of health |
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language: |
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- en |
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widget: |
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- text: The Patient is homeless |
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- text: The pt misuses prescription medicine |
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- text: The patient often goes hungry because they can't afford enough food |
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- text: >- |
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The patient's family is struggling to pay the rent and is at risk of being |
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evicted from their apartment |
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- text: The patient lives in a neighborhood with poor public transportation options |
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- text: >- |
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The patient was a victim of exploitation of dependency, causing them to feel |
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taken advantage of and vulnerable |
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- text: >- |
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The patient's family has had to move in with relatives due to financial |
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difficulties |
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- text: >- |
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The patient's insurance plan has annual limits on certain preventive care |
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services, such as screenings and vaccines. |
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- text: >- |
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The depression may be provoking the illness or making it more difficult to |
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manage |
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- text: >- |
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Due to the language barrier, the patient is having difficulty communicating |
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their medical history to the healthcare provider. |
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datasets: |
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- reachosen/autotrain-data-sdohv7 |
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co2_eq_emissions: |
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emissions: 0.01134763220649804 |
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pipeline_tag: text-classification |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: 3701198597 |
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- CO2 Emissions (in grams): 0.0113 |
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## Validation Metrics |
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- Loss: 0.057 |
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- Accuracy: 0.990 |
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- Macro F1: 0.990 |
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- Micro F1: 0.990 |
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- Weighted F1: 0.990 |
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- Macro Precision: 0.990 |
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- Micro Precision: 0.990 |
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- Weighted Precision: 0.991 |
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- Macro Recall: 0.990 |
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- Micro Recall: 0.990 |
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- Weighted Recall: 0.990 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True) |
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inputs = tokenizer("The Patient is homeless", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
