
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 14722565

## Validation Metrics

- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro Precision: 0.7745398773006135
- Weighted Precision: 0.7787550922520248
- Macro Recall: 0.7314173610899214
- Micro Recall: 0.7745398773006135
- Weighted Recall: 0.7745398773006135


## 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/serenay/autonlp-Emotion-14722565
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("serenay/autonlp-Emotion-14722565", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("serenay/autonlp-Emotion-14722565", use_auth_token=True)

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

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


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 14722565

## Validation Metrics

- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro Precision: 0.7745398773006135
- Weighted Precision: 0.7787550922520248
- Macro Recall: 0.7314173610899214
- Micro Recall: 0.7745398773006135
- Weighted Recall: 0.7745398773006135


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 14722565 ## Validation Metrics

- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro Precision: 0.7745398773006135
- Weighted Precision: 0.7787550922520248
- Macro Recall: 0.7314173610899214
- Micro Recall: 0.7745398773006135
- Weighted Recall: 0.7745398773006135 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 14722565 ## Validation Metrics

- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro Precision: 0.7745398773006135
- Weighted Precision: 0.7787550922520248
- Macro Recall: 0.7314173610899214
- Micro Recall: 0.7745398773006135
- Weighted Recall: 0.7745398773006135 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 14722565## Validation Metrics

- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro Precision: 0.7745398773006135
- Weighted Precision: 0.7787550922520248
- Macro Recall: 0.7314173610899214
- Micro Recall: 0.7745398773006135
- Weighted Recall: 0.7745398773006135## Usage

You can use cURL to access this model:



Or Python API: