
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927

## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592


## 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/vinaydngowda/autonlp-case-classify-xlnet-496213536
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["vinaydngowda/autonlp-data-case-classify-xlnet"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 19.964760910364927}
transformers pytorch bert text-classification autonlp en dataset:vinaydngowda/autonlp-data-case-classify-xlnet co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-vinaydngowda/autonlp-data-case-classify-xlnet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927

## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927 ## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-vinaydngowda/autonlp-data-case-classify-xlnet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927 ## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-vinaydngowda/autonlp-data-case-classify-xlnet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592## Usage

You can use cURL to access this model:



Or Python API: