
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048

## Validation Metrics

- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761


## 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/agnihotri/autotrain-contract_type-809725368
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["agnihotri/autotrain-data-contract_type"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07610944071640048}
transformers pytorch roberta text-classification autotrain en dataset:agnihotri/autotrain-data-contract_type co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-agnihotri/autotrain-data-contract_type #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048

## Validation Metrics

- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048 ## Validation Metrics

- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-agnihotri/autotrain-data-contract_type #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048 ## Validation Metrics

- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-agnihotri/autotrain-data-contract_type #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048## Validation Metrics

- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761## Usage

You can use cURL to access this model:



Or Python API: