
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3075087647
- CO2 Emissions (in grams): 17.1014

## Validation Metrics

- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000

## 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/eeshan/autotrain-r-nr-categorization-3075087647
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("eeshan/autotrain-r-nr-categorization-3075087647", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("eeshan/autotrain-r-nr-categorization-3075087647", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "license": "apache-2.0", "tags": ["autotrain", "text-classification", "bert", "patent-deberta", "patent-classification"], "datasets": ["eeshan/autotrain-data-r-nr-categorization"], "metrics": ["accuracy"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 17.1013640357776}, "pipeline_tag": "text-classification"}
transformers pytorch deberta text-classification autotrain bert patent-deberta patent-classification en dataset:eeshan/autotrain-data-r-nr-categorization license:apache-2.0 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #bert #patent-deberta #patent-classification #en #dataset-eeshan/autotrain-data-r-nr-categorization #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3075087647
- CO2 Emissions (in grams): 17.1014

## Validation Metrics

- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3075087647
- CO2 Emissions (in grams): 17.1014 ## Validation Metrics

- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #bert #patent-deberta #patent-classification #en #dataset-eeshan/autotrain-data-r-nr-categorization #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3075087647
- CO2 Emissions (in grams): 17.1014 ## Validation Metrics

- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #bert #patent-deberta #patent-classification #en #dataset-eeshan/autotrain-data-r-nr-categorization #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3075087647
- CO2 Emissions (in grams): 17.1014## Validation Metrics

- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000## Usage

You can use cURL to access this model:



Or Python API: