
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964

## Validation Metrics

- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
- Macro Precision: 0.33152557743856437
- Micro Precision: 0.676854818831649
- Weighted Precision: 0.6276125515413322
- Macro Recall: 0.33784302289888885
- Micro Recall: 0.676854818831649
- Weighted Recall: 0.676854818831649


## 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/bitmorse/autonlp-ks-530615016
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("bitmorse/autonlp-ks-530615016", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("bitmorse/autonlp-ks-530615016", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["bitmorse/autonlp-data-ks"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.2247356264808964}
transformers pytorch distilbert text-classification autonlp en dataset:bitmorse/autonlp-data-ks co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-bitmorse/autonlp-data-ks #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964

## Validation Metrics

- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
- Macro Precision: 0.33152557743856437
- Micro Precision: 0.676854818831649
- Weighted Precision: 0.6276125515413322
- Macro Recall: 0.33784302289888885
- Micro Recall: 0.676854818831649
- Weighted Recall: 0.676854818831649


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964 ## Validation Metrics

- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
- Macro Precision: 0.33152557743856437
- Micro Precision: 0.676854818831649
- Weighted Precision: 0.6276125515413322
- Macro Recall: 0.33784302289888885
- Micro Recall: 0.676854818831649
- Weighted Recall: 0.676854818831649 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-bitmorse/autonlp-data-ks #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964 ## Validation Metrics

- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
- Macro Precision: 0.33152557743856437
- Micro Precision: 0.676854818831649
- Weighted Precision: 0.6276125515413322
- Macro Recall: 0.33784302289888885
- Micro Recall: 0.676854818831649
- Weighted Recall: 0.676854818831649 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-bitmorse/autonlp-data-ks #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964## Validation Metrics

- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
- Macro Precision: 0.33152557743856437
- Micro Precision: 0.676854818831649
- Weighted Precision: 0.6276125515413322
- Macro Recall: 0.33784302289888885
- Micro Recall: 0.676854818831649
- Weighted Recall: 0.676854818831649## Usage

You can use cURL to access this model:



Or Python API: