
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191

## Validation Metrics

- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224
- Macro Precision: 0.4527155928883903
- Micro Precision: 0.7973333333333333
- Weighted Precision: 0.7610710955220162
- Macro Recall: 0.4947868561369568
- Micro Recall: 0.7973333333333333
- Weighted Recall: 0.7973333333333333


## 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/kyleinincubated/autonlp-abbb-622117836
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kyleinincubated/autonlp-abbb-622117836", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kyleinincubated/autonlp-abbb-622117836", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "zh", "tags": "autonlp", "datasets": ["kyleinincubated/autonlp-data-abbb"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.22514962526191}
transformers pytorch bert text-classification autonlp zh dataset:kyleinincubated/autonlp-data-abbb co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #zh #dataset-kyleinincubated/autonlp-data-abbb #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191

## Validation Metrics

- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224
- Macro Precision: 0.4527155928883903
- Micro Precision: 0.7973333333333333
- Weighted Precision: 0.7610710955220162
- Macro Recall: 0.4947868561369568
- Micro Recall: 0.7973333333333333
- Weighted Recall: 0.7973333333333333


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191 ## Validation Metrics

- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224
- Macro Precision: 0.4527155928883903
- Micro Precision: 0.7973333333333333
- Weighted Precision: 0.7610710955220162
- Macro Recall: 0.4947868561369568
- Micro Recall: 0.7973333333333333
- Weighted Recall: 0.7973333333333333 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191 ## Validation Metrics

- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224
- Macro Precision: 0.4527155928883903
- Micro Precision: 0.7973333333333333
- Weighted Precision: 0.7610710955220162
- Macro Recall: 0.4947868561369568
- Micro Recall: 0.7973333333333333
- Weighted Recall: 0.7973333333333333 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 622117836
- CO2 Emissions (in grams): 2.22514962526191## Validation Metrics

- Loss: 1.2368708848953247
- Accuracy: 0.7973333333333333
- Macro F1: 0.46009076588978487
- Micro F1: 0.7973333333333333
- Weighted F1: 0.7712349116681224
- Macro Precision: 0.4527155928883903
- Micro Precision: 0.7973333333333333
- Weighted Precision: 0.7610710955220162
- Macro Recall: 0.4947868561369568
- Micro Recall: 0.7973333333333333
- Weighted Recall: 0.7973333333333333## Usage

You can use cURL to access this model:



Or Python API: