
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 6711455

## Validation Metrics

- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388


## 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/ybybybybybybyb/autonlp-revanalysis-6711455
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ybybybybybybyb/autonlp-revanalysis-6711455", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ybybybybybybyb/autonlp-revanalysis-6711455", use_auth_token=True)

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

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


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 6711455

## Validation Metrics

- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 6711455 ## Validation Metrics

- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #funnel #text-classification #autonlp #ko #dataset-ybybybybybybyb/autonlp-data-revanalysis #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 6711455 ## Validation Metrics

- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #funnel #text-classification #autonlp #ko #dataset-ybybybybybybyb/autonlp-data-revanalysis #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 6711455## Validation Metrics

- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388## Usage

You can use cURL to access this model:



Or Python API: