Text Classification
Transformers
Safetensors
Turkish
bert
sentiment-analysis
aspect-based-sentiment-analysis
text-embeddings-inference
Instructions to use Sengil/ABSA-Turkish-bert-based-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sengil/ABSA-Turkish-bert-based-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sengil/ABSA-Turkish-bert-based-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sengil/ABSA-Turkish-bert-based-small") model = AutoModelForSequenceClassification.from_pretrained("Sengil/ABSA-Turkish-bert-based-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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pipeline_tag: text-classification
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datasets:
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- Sengil/Turkish-ABSA-Wsynthetic
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widget:
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- text: "Yemekler güzel ama fiyatlar pahalı."
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output:
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- aspect: Yemekler
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label: POSITIVE
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score: 0.8
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- aspect: fiyat
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label: NEGATIVE
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score: 0.2
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pipeline_tag: text-classification
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datasets:
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- Sengil/Turkish-ABSA-Wsynthetic
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