Text Classification
Transformers
Safetensors
Uzbek
xlm-roberta
sentiment-analysis
uzbek
text-embeddings-inference
Instructions to use Dyanne05/xlmr-uzbek-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dyanne05/xlmr-uzbek-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dyanne05/xlmr-uzbek-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dyanne05/xlmr-uzbek-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Dyanne05/xlmr-uzbek-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Uzbek Sentiment Analysis using XLM-RoBERTa
This model was fine-tuned for Uzbek sentiment classification using multilingual transformer architectures.
Labels
- negative
- neutral
- positive
Base Model
- xlm-roberta-base
Description
The project compares classical machine learning approaches with transformer-based NLP models for low-resource Uzbek language sentiment analysis.
The model was trained using HuggingFace Transformers and PyTorch.
Tasks
- Sentiment Classification
- Uzbek NLP
- Multilingual Text Classification
- Downloads last month
- 11
Model tree for Dyanne05/xlmr-uzbek-sentiment
Base model
FacebookAI/xlm-roberta-base