
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905

## Validation Metrics

- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552


## 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/palakagl/autotrain-PersonalAssitant-717221783
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221783", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.014567637985425905}
transformers pytorch roberta text-classification autotrain en dataset:palakagl/autotrain-data-PersonalAssitant co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905

## Validation Metrics

- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905 ## Validation Metrics

- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905 ## Validation Metrics

- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221783
- CO2 Emissions (in grams): 0.014567637985425905## Validation Metrics

- Loss: 0.38848456740379333
- Accuracy: 0.9180509413067552
- Macro F1: 0.9157418163085091
- Micro F1: 0.9180509413067552
- Weighted F1: 0.9185290137253468
- Macro Precision: 0.9189981206383326
- Micro Precision: 0.9180509413067552
- Weighted Precision: 0.9221607328493303
- Macro Recall: 0.9158232837734661
- Micro Recall: 0.9180509413067552
- Weighted Recall: 0.9180509413067552## Usage

You can use cURL to access this model:



Or Python API: