---
tags:
- autotrain
- text-classification
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
---

# 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)
```