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metadata
license: unknown
datasets:
  - anilguven/turkish_product_reviews_sentiment
language:
  - tr
metrics:
  - accuracy
  - f1
  - recall
  - precision
tags:
  - turkish
  - product
  - electra
  - bert
  - review

Model Info

This model was developed/finetuned for product review task for Turkish Language. Model was finetuned via hepsiburada.com product review dataset.

Model Sources

How to Get Started with the Model

from transformers import pipeline

pipe = pipeline("text-classification", model="anilguven/electra_tr_turkish_product_reviews")

or

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("anilguven/electra_tr_turkish_product_reviews")

model = AutoModelForSequenceClassification.from_pretrained("anilguven/electra_tr_turkish_product_reviews")

Preprocessing

You must apply removing stopwords, stemming, or lemmatization process for Turkish.

Results

Accuracy: %92.54

Citation

BibTeX:

@INPROCEEDINGS{9559007, author={Guven, Zekeriya Anil}, booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)}, title={The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews}, year={2021}, volume={}, number={}, pages={629-632}, keywords={Computer science;Sentiment analysis;Analytical models;Computational modeling;Bit error rate;Time factors;Random forests;Sentiment Analysis;Language Model;Product Review;Machine Learning;E-commerce}, doi={10.1109/UBMK52708.2021.9559007}}

APA:

Guven, Z. A. (2021, September). The effect of bert, electra and albert language models on sentiment analysis for turkish product reviews. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 629-632). IEEE.