
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474

## Validation Metrics

- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265


## 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/cjbarrie/autotrain-masress-medcrit-binary-5-937130980
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["cjbarrie/autotrain-data-masress-medcrit-binary-5"], "widget": [{"text": "\u0627\u0644\u0643\u0644 \u064a\u0646\u062a\u0642\u062f \u0627\u0644\u0631\u0626\u064a\u0633 \u0639\u0644\u0649 \u0625\u062e\u0641\u0627\u0642\u0627\u062a\u0647"}], "co2_eq_emissions": 0.01017487638098474}
transformers pytorch bert text-classification autotrain unk dataset:cjbarrie/autotrain-data-masress-medcrit-binary-5 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-cjbarrie/autotrain-data-masress-medcrit-binary-5 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474

## Validation Metrics

- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474 ## Validation Metrics

- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-cjbarrie/autotrain-data-masress-medcrit-binary-5 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474 ## Validation Metrics

- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-cjbarrie/autotrain-data-masress-medcrit-binary-5 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474## Validation Metrics

- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265## Usage

You can use cURL to access this model:



Or Python API: