
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3023686751
- CO2 Emissions (in grams): 1.2718

## Validation Metrics

- Loss: 0.489
- Accuracy: 0.839
- Macro F1: 0.767
- Micro F1: 0.839
- Weighted F1: 0.832
- Macro Precision: 0.782
- Micro Precision: 0.839
- Weighted Precision: 0.845
- Macro Recall: 0.770
- Micro Recall: 0.839
- Weighted Recall: 0.839


## 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 to fly with Turkish Airlines"}' https://api-inference.huggingface.co/models/tkurtulus/TurkishAirlines-SentimentAnalysisModel
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("tkurtulus/TurkishAirlines-SentimentAnalysisModel")

tokenizer = AutoTokenizer.from_pretrained("tkurtulus/TurkishAirlines-SentimentAnalysisModel")

inputs = tokenizer("I love to fly with Turkish Airlines", return_tensors="pt")

outputs = model(**inputs)
```
{"language": ["tr", "en"], "tags": ["autotrain", "text-classification"], "datasets": ["tkurtulus/thycomments"], "metrics": ["accuracy"], "widget": [{"text": "Ald\u0131\u011f\u0131m hizmetten \u00e7ok memnun kald\u0131m... Yeniden g\u00f6klerde g\u00f6r\u00fc\u015fmek \u00fczere..."}], "co2_eq_emissions": {"emissions": 1.2718440164245879}, "pipeline_tag": "text-classification"}
transformers pytorch safetensors convbert text-classification autotrain tr en dataset:tkurtulus/thycomments co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #convbert #text-classification #autotrain #tr #en #dataset-tkurtulus/thycomments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3023686751
- CO2 Emissions (in grams): 1.2718

## Validation Metrics

- Loss: 0.489
- Accuracy: 0.839
- Macro F1: 0.767
- Micro F1: 0.839
- Weighted F1: 0.832
- Macro Precision: 0.782
- Micro Precision: 0.839
- Weighted Precision: 0.845
- Macro Recall: 0.770
- Micro Recall: 0.839
- Weighted Recall: 0.839


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3023686751
- CO2 Emissions (in grams): 1.2718 ## Validation Metrics

- Loss: 0.489
- Accuracy: 0.839
- Macro F1: 0.767
- Micro F1: 0.839
- Weighted F1: 0.832
- Macro Precision: 0.782
- Micro Precision: 0.839
- Weighted Precision: 0.845
- Macro Recall: 0.770
- Micro Recall: 0.839
- Weighted Recall: 0.839 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #convbert #text-classification #autotrain #tr #en #dataset-tkurtulus/thycomments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3023686751
- CO2 Emissions (in grams): 1.2718 ## Validation Metrics

- Loss: 0.489
- Accuracy: 0.839
- Macro F1: 0.767
- Micro F1: 0.839
- Weighted F1: 0.832
- Macro Precision: 0.782
- Micro Precision: 0.839
- Weighted Precision: 0.845
- Macro Recall: 0.770
- Micro Recall: 0.839
- Weighted Recall: 0.839 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #convbert #text-classification #autotrain #tr #en #dataset-tkurtulus/thycomments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3023686751
- CO2 Emissions (in grams): 1.2718## Validation Metrics

- Loss: 0.489
- Accuracy: 0.839
- Macro F1: 0.767
- Micro F1: 0.839
- Weighted F1: 0.832
- Macro Precision: 0.782
- Micro Precision: 0.839
- Weighted Precision: 0.845
- Macro Recall: 0.770
- Micro Recall: 0.839
- Weighted Recall: 0.839## Usage

You can use cURL to access this model:



Or Python API: