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turkish-sentiment-analysis
emre/turkish-sentiment-analysis
turkish-sentiment-analysis is a text classification model from emre. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
- Problem type: Multi-class Classification - Model ID: 870727732 - CO2 Emissions (in grams): 120.82460124309924
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1.5 GB on disk
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Model card
From the Hugging Face model README
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)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 15, 2022
- Updated
- Apr 1, 2025