Instructions to use akdeniz27/bert-base-turkish-cased-ner-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akdeniz27/bert-base-turkish-cased-ner-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="akdeniz27/bert-base-turkish-cased-ner-quantized")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner-quantized") model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner-quantized") - Notebooks
- Google Colab
- Kaggle
Turkish Named Entity Recognition (NER) Quantized Model
This model is the dynamically quantized version of the model (https://akdeniz27/bert-base-turkish-cased-ner)
How to use:
# First install "optimum[onnxruntime]":
!pip install "optimum[onnxruntime]"
# and import "ORTModelForTokenClassification":
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForTokenClassification
model = ORTModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner-quantized", file_name="model_quantized.onnx")
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner-quantized")
ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first")
ner("your text here")
Pls refer (https://github.com/akdeniz27/dynamic_quantization) for details of quantization.
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