
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863

## Validation Metrics

- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497


## 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/ysslang/autonlp-test-459011902
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ysslang/autonlp-test-459011902", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ysslang/autonlp-test-459011902", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "zh", "tags": "autonlp", "datasets": ["ysslang/autonlp-data-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.9230691350863}
transformers pytorch bert text-classification autonlp zh dataset:ysslang/autonlp-data-test co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-ysslang/autonlp-data-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863

## Validation Metrics

- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863 ## Validation Metrics

- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863 ## Validation Metrics

- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863## Validation Metrics

- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497## Usage

You can use cURL to access this model:



Or Python API: