Implement techniques from Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain. This paper presents Mecellem models, a framework for developing specialized language models for the Turkish legal domain through domain adaptation strategies
Scanned 9/9/2026
Install to Claude Code
npx -y skills add ADu2021/skillXiv --skill mecellem-models-turkish-models-trained-from-scratc --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: mecellem-models-turkish-models-trained-from-scratc
title: "Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.16018"
keywords: ["model", "framework"]
description: "Implement techniques from Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain. This paper presents Mecellem models, a framework for developing specialized language models for the Turkish legal domain through domain adaptation strategies"
---
## Overview
This skill implements concepts from the research paper [[2601.16018](https://arxiv.org/abs/2601.16018)].
## When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
## When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
## Key Concepts
The paper addresses: This paper presents Mecellem models, a framework for developing specialized language models for the Turkish legal domain through domain adaptation strategies. We make two contributions: (1)Encoder Model Pre-trained from Scratch: ModernBERT-based bidi...
For detailed methodology, refer to the [full paper](https://arxiv.org/html/2601.16018).
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