R3iwan/kazakh-sentiment-bert

Kazakh Sentiment Analysis Model

A sentiment analysis model for Kazakh text, fine-tuned on a dataset of entertainment reviews.

Model Description

This model is based on bert-base-multilingual-cased and fine-tuned for sentiment classification of Kazakh text into three classes:

• —positive (positive sentiment)
• —neutral (neutral sentiment)
• —negative (negative sentiment)

Usage

Using transformers pipeline

from transformers import pipeline

classifier = pipeline("text-classification", model="R3iwan/kazakh-sentiment-bert")

text = "Бұл фильм маған ұнамады"
result = classifier(text)
print(result)

Direct model usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "R3iwan/kazakh-sentiment-bert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Бұл фильм маған ұнамады"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

predicted_class = torch.argmax(predictions, dim=-1).item()
labels = ["negative", "neutral", "positive"]
print(f"Predicted: {labels[predicted_class]}")
print(f"Confidence: {predictions[0][predicted_class].item():.2%}")

Training

The model was trained on the R3iwan/entertainment-reviews-kazakh dataset with the following parameters:

• —Base Model: bert-base-multilingual-cased
• —Epochs: 2
• —Batch Size: 8
• —Learning Rate: 2.5e-5 (with linear decay)
• —Train/Validation/Test Split: ~80/10/10

Metrics

• —Accuracy: 100% on test set
• —Task: Text Classification (Sentiment Analysis)

Limitations

The model is trained on a limited dataset of entertainment reviews and may perform better on similar texts. For other domains, additional fine-tuning may be required.

Author

R3iwan

License

Apache 2.0