
# Model Trained

- Problem type: Multi-class Classification
- Model ID: 47871116935
- CO2 Emissions (in grams): 0.3718

## Validation Metrics

- Loss: 1.388
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000


## 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/Mulik/Classify-2.0_Stable
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Mulik/autotrain-classify-2.0-47871116935", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Mulik/Classify-2.0_Stable", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["autotrain", "text-classification"], "datasets": ["Mulik/autotrain-data-classify-2.0"], "metrics": ["accuracy"], "widget": [{"text": "Tv for Television"}], "co2_eq_emissions": {"emissions": 0.3718001513913416}, "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification autotrain en dataset:Mulik/autotrain-data-classify-2.0 license:mit co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Mulik/autotrain-data-classify-2.0 #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained

- Problem type: Multi-class Classification
- Model ID: 47871116935
- CO2 Emissions (in grams): 0.3718

## Validation Metrics

- Loss: 1.388
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained

- Problem type: Multi-class Classification
- Model ID: 47871116935
- CO2 Emissions (in grams): 0.3718 ## Validation Metrics

- Loss: 1.388
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Mulik/autotrain-data-classify-2.0 #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained

- Problem type: Multi-class Classification
- Model ID: 47871116935
- CO2 Emissions (in grams): 0.3718 ## Validation Metrics

- Loss: 1.388
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Mulik/autotrain-data-classify-2.0 #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained

- Problem type: Multi-class Classification
- Model ID: 47871116935
- CO2 Emissions (in grams): 0.3718## Validation Metrics

- Loss: 1.388
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000## Usage

You can use cURL to access this model:



Or Python API: