| --- |
| license: mit |
| language: |
| - id |
| tags: |
| - nlp |
| - pytorch |
| - xlstm |
| - language-modeling |
| - aspect-based-sentiment-analysis |
| - sequence-labeling |
| - indonesian |
| pipeline_tag: token-classification |
| --- |
| |
| # Bi-xLSTM[7:1] for Indonesian End-to-End ABSA |
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| This repository contains a Bi-xLSTM[7:1] model pretrained on large-scale Indonesian Wikipedia data and fine-tuned for Indonesian End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA). |
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| ## Model Description |
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| The model uses a bidirectional xLSTM-based architecture for contextual language modeling. It was first pretrained on Indonesian Wikipedia data using a forward and backward language modeling objective, then fine-tuned for E2E-ABSA using BIOES sentiment tagging and CRF decoding. |
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| The final task is to extract aspect–sentiment pairs directly from Indonesian review text. |
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| ## Architecture |
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| - Model: Bi-xLSTM[7:1] |
| - Pretraining objective: Bidirectional contextual language modeling |
| - Fine-tuning task: End-to-End Aspect-Based Sentiment Analysis |
| - Decoder: CRF |
| - Labeling scheme: BIOES with sentiment labels |
| - Framework: PyTorch |
| - Language: Indonesian |
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| ## Dataset |
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| The model was pretrained using Indonesian Wikipedia data and fine-tuned on Indonesian review data for aspect-based sentiment analysis. |
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| ## Intended Use |
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| This model is intended for research and academic purposes, especially for: |
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| - Indonesian NLP |
| - Sequence labeling |
| - Aspect-Based Sentiment Analysis |
| - Contextual language modeling |
| - Comparison between xLSTM-based models and Transformer-based models |
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