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---
tags:
- autotrain
- text-classification
- healthcare
- sdoh
- social determinants of health
language:
- en
widget:
- text: The Patient is homeless
- text: The pt misuses prescription medicine
- text: The patient often goes hungry because they can't afford enough food
- text: >-
The patient's family is struggling to pay the rent and is at risk of being
evicted from their apartment
- text: The patient lives in a neighborhood with poor public transportation options
- text: >-
The patient was a victim of exploitation of dependency, causing them to feel
taken advantage of and vulnerable
- text: >-
The patient's family has had to move in with relatives due to financial
difficulties
- text: >-
The patient's insurance plan has annual limits on certain preventive care
services, such as screenings and vaccines.
- text: >-
The depression may be provoking the illness or making it more difficult to
manage
- text: >-
Due to the language barrier, the patient is having difficulty communicating
their medical history to the healthcare provider.
datasets:
- reachosen/autotrain-data-sdohv7
co2_eq_emissions:
emissions: 0.01134763220649804
pipeline_tag: text-classification
license: apache-2.0
---
# 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)
``` |