
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924

## Validation Metrics

- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890088
- Macro Precision: 0.9540948884759232
- Micro Precision: 0.9697853317600073
- Weighted Precision: 0.9694186941924757
- Macro Recall: 0.9428467518468838
- Micro Recall: 0.9697853317600073
- Weighted Recall: 0.9697853317600073


## 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": "Bu ürün gerçekten güzel çıktı"}' https://api-inference.huggingface.co/models/emre/turkish-sentiment-analysis
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("emre/turkish-sentiment-analysis", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("emre/turkish-sentiment-analysis", use_auth_token=True)

inputs = tokenizer("Bu ürün gerçekten güzel çıktı", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "tr", "tags": "autotrain", "datasets": ["emre/autotrain-data-turkish-sentiment-analysis"], "widget": [{"text": "Bu \u00fcr\u00fcn ger\u00e7ekten g\u00fczel \u00e7\u0131kt\u0131"}], "co2_eq_emissions": 120.82460124309924}
transformers pytorch safetensors bert text-classification autotrain tr dataset:emre/autotrain-data-turkish-sentiment-analysis co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #tr #dataset-emre/autotrain-data-turkish-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924

## Validation Metrics

- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890088
- Macro Precision: 0.9540948884759232
- Micro Precision: 0.9697853317600073
- Weighted Precision: 0.9694186941924757
- Macro Recall: 0.9428467518468838
- Micro Recall: 0.9697853317600073
- Weighted Recall: 0.9697853317600073


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924 ## Validation Metrics

- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890088
- Macro Precision: 0.9540948884759232
- Micro Precision: 0.9697853317600073
- Weighted Precision: 0.9694186941924757
- Macro Recall: 0.9428467518468838
- Micro Recall: 0.9697853317600073
- Weighted Recall: 0.9697853317600073 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #tr #dataset-emre/autotrain-data-turkish-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924 ## Validation Metrics

- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890088
- Macro Precision: 0.9540948884759232
- Micro Precision: 0.9697853317600073
- Weighted Precision: 0.9694186941924757
- Macro Recall: 0.9428467518468838
- Micro Recall: 0.9697853317600073
- Weighted Recall: 0.9697853317600073 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #tr #dataset-emre/autotrain-data-turkish-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924## Validation Metrics

- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890088
- Macro Precision: 0.9540948884759232
- Micro Precision: 0.9697853317600073
- Weighted Precision: 0.9694186941924757
- Macro Recall: 0.9428467518468838
- Micro Recall: 0.9697853317600073
- Weighted Recall: 0.9697853317600073## Usage

You can use cURL to access this model:



Or Python API: