metadata
language:
- kk
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- kazakh
- bert
- multilingual
pipeline_tag: text-classification
datasets:
- R3iwan/entertainment-reviews-kazakh
base_model: google-bert/bert-base-multilingual-cased
model-index:
- name: kazakh-sentiment-bert
results: []
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