metadata

license: cc-by-4.0
datasets:
  - issai/kazsandra
language:
  - kk
metrics:
  - f1
pipeline_tag: text-classification
tags:
  - polarity
  - polarity classification
  - sentiment analysis
widget:
  - text: Түк ұнаған жоқ.
    example_title: Negative

Polarity Classification Model for Kazakh

This is a RemBERT model fine-tuned for sentiment analysis on product reviews in Kazakh. It predicts the polarity of a review as positive or negative. The model was fine-tuned on KazSAnDRA.

Model	Accuracy	Precision	Recall	F1
RemBERT	0.89	0.81	0.82	0.81

How to use

You can use this model with the Transformers pipeline for text classification.

from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from transformers import TextClassificationPipeline

model = AutoModelForSequenceClassification.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")
tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")

pipe = TextClassificationPipeline(model = model, tokenizer = tokenizer)

reviews = ["Бұл бейнефильм маған түк ұнамады.", "Осы кітап қызық сияқты."]

for review in reviews:
    print(pipe(review))