---
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
---

# 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)

# Extract logits from the outputs
logits = outputs.logits

# Apply softmax to get probabilities
probabilities = F.softmax(logits, dim=1)

# get class mapping
configs_fp = "path to model files" + "config.json"
with open(configs_fp, 'r') as configs_file:
    class_mapping = json.load(configs_file)['id2label']

# Create the class_probs dictionary
class_probs = {class_mapping[str(i)]: probabilities[0][i].item() for i in range(len(probabilities[0]))}


```