Text Classification
Transformers
Safetensors
Burmese
xlm-roberta
aspect-based-sentiment-analysis
absa
sentiment-analysis
burmese
myanmar
text-embeddings-inference
Instructions to use Fixaro/myanmar-absa-sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fixaro/myanmar-absa-sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fixaro/myanmar-absa-sentiment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fixaro/myanmar-absa-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("Fixaro/myanmar-absa-sentiment-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Myanmar ABSA - Stage 2 (Aspect-Level Sentiment Classification)
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This model is a fine-tuned version of `xlm-roberta-
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## Sentiment Classes:
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- **`0`**: `Negative`
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# Myanmar ABSA - Stage 2 (Aspect-Level Sentiment Classification)
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This model is a fine-tuned version of `xlm-roberta-base` trained on Burmese customer reviews formatted as sentence pairs (`review_text`, `target_aspect`). It predicts the sentiment polarity for a given target aspect.
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## Sentiment Classes:
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- **`0`**: `Negative`
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