
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148

## Validation Metrics

- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058


## 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/emekaboris/autonlp-txc-17923124
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923124", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923124", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-txc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 133.57087522185148}
transformers pytorch roberta text-classification autonlp en dataset:emekaboris/autonlp-data-txc co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-emekaboris/autonlp-data-txc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148

## Validation Metrics

- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148 ## Validation Metrics

- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148 ## Validation Metrics

- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148## Validation Metrics

- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058## Usage

You can use cURL to access this model:



Or Python API: