
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355

## Validation Metrics

- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808


## 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/jk-gjom/autotrain-jk123-1105140277
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("jk-gjom/autotrain-jk123-1105140277", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("jk-gjom/autotrain-jk123-1105140277", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["jk-gjom/autotrain-data-jk123"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.1863935648335355}
transformers pytorch bert text-classification autotrain unk dataset:jk-gjom/autotrain-data-jk123 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-jk-gjom/autotrain-data-jk123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355

## Validation Metrics

- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355 ## Validation Metrics

- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-jk-gjom/autotrain-data-jk123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355 ## Validation Metrics

- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-jk-gjom/autotrain-data-jk123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355## Validation Metrics

- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808## Usage

You can use cURL to access this model:



Or Python API: