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---
tags: autotrain
language: tr
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ridvan9/autotrain-data-rdv-senti-analys-v2
co2_eq_emissions: 4.95702490470204
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 791424369
- CO2 Emissions (in grams): 4.95702490470204

## Validation Metrics

- Loss: 0.21698406338691711
- Accuracy: 0.9337298215802888
- Macro F1: 0.9339734231139484
- Micro F1: 0.9337298215802888
- Weighted F1: 0.9340497563679602
- Macro Precision: 0.934733314676483
- Micro Precision: 0.9337298215802888
- Weighted Precision: 0.9348373701161897
- Macro Recall: 0.9336931241452828
- Micro Recall: 0.9337298215802888
- Weighted Recall: 0.9337298215802888


## 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/ridvan9/autotrain-rdv-senti-analys-v2-791424369
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ridvan9/autotrain-rdv-senti-analys-v2-791424369", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ridvan9/autotrain-rdv-senti-analys-v2-791424369", use_auth_token=True)

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

outputs = model(**inputs)
```