
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 4021083

## Validation Metrics

- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089


## 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/idrimadrid/autonlp-creator_classifications-4021083
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("idrimadrid/autonlp-creator_classifications-4021083", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("idrimadrid/autonlp-creator_classifications-4021083", use_auth_token=True)

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

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


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 4021083

## Validation Metrics

- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 4021083 ## Validation Metrics

- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-idrimadrid/autonlp-data-creator_classifications #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 4021083 ## Validation Metrics

- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-idrimadrid/autonlp-data-creator_classifications #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 4021083## Validation Metrics

- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089## Usage

You can use cURL to access this model:



Or Python API: