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noson71/albert-kh-en-codeswitch-32k

AI model by noson71

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Model card & documentation

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This model is a Khmer-English Code-Switching ALBERT based on seanghay/albert-khmer-small, further pre-trained on a bilingual corpus with explicit code-switching data. The model excels at understanding and processing mixed Khmer-English text, named entity recognition, and question answering in code-switched contexts. This model extends the original Khmer ALBERT with enhanced capabilities for: The model was trained on a carefully balanced corpus consisting of: Lower TPC is better; higher percentages are…

noson71/albert-kh-en-codeswitch-32k on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. albert-kh-en-codeswitch-32k
  2. Model Details
  3. Model Description
  4. Training Data
  5. Training Procedure
  6. Preprocessing
  7. Evaluation Results
  8. Complex Word Integrity Breakdown
  9. MLM Performance Examples
  10. How to Use
  11. Load the model
  12. Masked Language Modeling (Inference)

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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

noson71/albert-kh-en-codeswitch-32k is a task-unspecified model repository published by noson71 on Hugging Face. A library was not reported.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
Not reported
Library
Not reported
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-23T06:43:08.000Z
Last modified
2026-10-23T06:43:08.000Z

Compatibility and lineage

Language coverage was not reported.

Base-model lineage was not included.

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Source: Hugging Face Hub. Metadata observed 2026-10-23T06:46:00.441Z. Daily imports are snapshots, not real-time monitoring.

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