Negative Notr Positive
---
tags: autotrain
language: tr
widget:
- text: "Bu ürün gerçekten güzel çıktı"
datasets:
- emre/autotrain-data-turkish-sentiment-analysis
co2_eq_emissions: 120.82460124309924
---

# 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)
```