
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023

## Validation Metrics

- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842


## 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 AutoTrain"}' https://api-inference.huggingface.co/models/lucafrost/ClaimBuster-DeBERTaV2
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucafrost/ClaimBuster-DeBERTaV2", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucafrost/ClaimBuster-DeBERTaV2", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["lucafrost/autotrain-data-claimbuster"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 23.102349586537482}}
transformers pytorch deberta-v2 text-classification autotrain en dataset:lucafrost/autotrain-data-claimbuster co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucafrost/autotrain-data-claimbuster #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023

## Validation Metrics

- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023 ## Validation Metrics

- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucafrost/autotrain-data-claimbuster #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023 ## Validation Metrics

- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucafrost/autotrain-data-claimbuster #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023## Validation Metrics

- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842## Usage

You can use cURL to access this model:



Or Python API: