
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745

## Validation Metrics

- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709


## 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/MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["MadhurJindalWorkMail/autonlp-data-Gibb-Detect"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 70.95647633212745}
transformers pytorch bert text-classification autonlp en dataset:MadhurJindalWorkMail/autonlp-data-Gibb-Detect co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-MadhurJindalWorkMail/autonlp-data-Gibb-Detect #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745

## Validation Metrics

- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745 ## Validation Metrics

- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-MadhurJindalWorkMail/autonlp-data-Gibb-Detect #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745 ## Validation Metrics

- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-MadhurJindalWorkMail/autonlp-data-Gibb-Detect #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745## Validation Metrics

- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709## Usage

You can use cURL to access this model:



Or Python API: