
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061

## Validation Metrics

- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.9196871607509906
- Macro Precision: 0.9207759152612094
- Micro Precision: 0.9204545454545454
- Weighted Precision: 0.922177301864802
- Macro Recall: 0.9055002187355129
- Micro Recall: 0.9204545454545454
- Weighted Recall: 0.9204545454545454


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/rodrigogelacio/autonlp-department-classification-534915130
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["rodrigogelacio/autonlp-data-department-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4862856774320061}
transformers pytorch bert text-classification autonlp unk dataset:rodrigogelacio/autonlp-data-department-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-rodrigogelacio/autonlp-data-department-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061

## Validation Metrics

- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.9196871607509906
- Macro Precision: 0.9207759152612094
- Micro Precision: 0.9204545454545454
- Weighted Precision: 0.922177301864802
- Macro Recall: 0.9055002187355129
- Micro Recall: 0.9204545454545454
- Weighted Recall: 0.9204545454545454


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061 ## Validation Metrics

- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.9196871607509906
- Macro Precision: 0.9207759152612094
- Micro Precision: 0.9204545454545454
- Weighted Precision: 0.922177301864802
- Macro Recall: 0.9055002187355129
- Micro Recall: 0.9204545454545454
- Weighted Recall: 0.9204545454545454 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-rodrigogelacio/autonlp-data-department-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061 ## Validation Metrics

- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.9196871607509906
- Macro Precision: 0.9207759152612094
- Micro Precision: 0.9204545454545454
- Weighted Precision: 0.922177301864802
- Macro Recall: 0.9055002187355129
- Micro Recall: 0.9204545454545454
- Weighted Recall: 0.9204545454545454 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-rodrigogelacio/autonlp-data-department-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061## Validation Metrics

- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.9196871607509906
- Macro Precision: 0.9207759152612094
- Micro Precision: 0.9204545454545454
- Weighted Precision: 0.922177301864802
- Macro Recall: 0.9055002187355129
- Micro Recall: 0.9204545454545454
- Weighted Recall: 0.9204545454545454## Usage

You can use cURL to access this model:



Or Python API: