
# 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)
```
{"language": ["en"], "tags": ["autotrain", "text-classification", "healthcare", "sdoh", "social determinants of health"], "datasets": ["reachosen/autotrain-data-sdohv7"], "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."}], "co2_eq_emissions": {"emissions": 0.01134763220649804}, "pipeline_tag": "text-classification"}
transformers pytorch deberta-v2 text-classification autotrain healthcare sdoh social determinants of health en dataset:reachosen/autotrain-data-sdohv7 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #healthcare #sdoh #social determinants of health #en #dataset-reachosen/autotrain-data-sdohv7 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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:



Or Python API:


# 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:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #healthcare #sdoh #social determinants of health #en #dataset-reachosen/autotrain-data-sdohv7 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #healthcare #sdoh #social determinants of health #en #dataset-reachosen/autotrain-data-sdohv7 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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:



Or Python API: