cortexinc
/

cortex45m-text-correction

@@ -1,140 +1,125 @@
 ---
 license: mit
 language:
 - tr
 ---
-<div align="center">🧠 Cortex45M
-Turkish Text Correction Model
-A TuncerLab AI Research Project
-Purpose-built for Turkish. Focused on text correction.
-<br/>"License: MIT" (https://img.shields.io/badge/License-MIT-3DA639?style=for-the-badge)
-"Framework: TensorFlow" (https://img.shields.io/badge/Framework-TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white)
-"Language: Turkish" (https://img.shields.io/badge/Language-Turkish-E30A17?style=for-the-badge)
-"Parameters: 45M" (https://img.shields.io/badge/Parameters-45M-7C3AED?style=for-the-badge)
-</div>---
-🌌 About Cortex45M
-Cortex45M is a Turkish-language AI model developed by TuncerLab, specifically trained for the task of text correction.
-Built with TensorFlow and approximately 45 million parameters, Cortex45M is designed around a Turkish-only language focus rather than multilingual general-purpose language modeling.
-The model was trained using a corpus totaling approximately 10 GB, with 28,000 training steps dedicated to its training process.
-Because Cortex45M targets a specific task—text correction—its training schedule was designed with that objective in mind, using fewer training steps than might be selected for a broader language-model training run.
-«🇹🇷 One language. One focused task. A dedicated model architecture.»
-📊 Model Specifications
-Specification| Details
-Model name| Cortex45M
-Developer| TuncerLab
-Parameter count| 45 million
-Training corpus| Approximately 10 GB
-Training steps| 28,000
-Primary language| Turkish only
-Primary task| Text correction
 Framework| TensorFlow
-Model category| Language / NLP
-License| MIT
-🎯 Intended Use
-Cortex45M is intended for Turkish text-processing applications, including:
-- ✍️ Correcting spelling mistakes in Turkish text
-- 📝 Improving text-writing quality
-- 🔤 Processing common Turkish character and orthographic errors
-- 🧪 Experimenting with task-specific Turkish NLP
-- 🛠️ Integrating text correction into Turkish-language applications
-The quality of corrections may vary depending on the input, context, and types of errors encountered.
-🇹🇷 Turkish-First Design
-Cortex45M is designed specifically for Turkish-language processing.
-Its language focus includes the Turkish alphabet and characters such as:
-"ç · ğ · ı · İ · ö · ş · ü"
-A Turkish-only focus allows the project to concentrate its training and evaluation on the linguistic characteristics of Turkish rather than explicitly targeting multilingual operation.
-🏋️ Training
-Training overview
-Cortex45M was trained with a corpus of approximately 10 GB and completed 28,000 training steps.
-The training process was task-oriented, focusing on text correction rather than attempting to maximize the number of steps for a general-purpose language model.
-Training summary
-Training property| Value
-Corpus size| ~10 GB
-Parameters| 45M
-Training steps| 28,000
-Language scope| Turkish
-Objective| Text correction
-Framework| TensorFlow
-Important: Corpus size describes the size of the training corpus; it does not, by itself, establish the number of tokens processed, dataset quality, or model accuracy. Those require separate measurements.
-⚙️ Framework
-Cortex45M uses TensorFlow as its training framework.
-Inference requirements depend on the repository's actual model implementation, tokenizer, checkpoint format, and configuration files. Consult the repository files before selecting a loading procedure.
-No unverified compatibility with Hugging Face Transformers or other inference frameworks is claimed here.
-📈 Evaluation
-Model performance should be measured using a representative Turkish text-correction test set.
-Recommended metrics include:
-- Character Error Rate (CER): Character-level correction errors.
-- Word Error Rate (WER): Word-level differences.
-- Exact-match accuracy: Percentage of outputs matching the reference correction.
-- Meaning preservation: Whether the correction retains the original intended meaning.
-- Overcorrection rate: How often the model changes text that was already correct.
-Benchmark scores will be added when measured results are available.
-⚖️ License
-Cortex45M is released under the MIT License, subject to the rights and licensing conditions applicable to the distributed materials.
-You may use, modify, distribute, sublicense, and sell copies of materials covered by this license, provided that the copyright and permission notice are included.
-Third-party datasets and external components may remain subject to their own licenses.
-See the ""LICENSE"" (LICENSE) file for the full license text.
-👨‍💻 Developed by TuncerLab
-TuncerLab — Independent AI Research & Development
-Cortex45M is part of the TuncerLab effort to explore dedicated AI models and Turkish-language technology.
-⚠️ Disclaimer
-Cortex45M is a task-specific AI research project. Its outputs may contain spelling, grammatical, contextual, or semantic errors. Outputs should be reviewed before use in critical applications.
 ---
-<div align="center">🧠 CORTEX45M
-45M Parameters · ~10 GB Corpus · 28K Training Steps
-Turkish-first AI, developed by TuncerLab.
-</div>

 ---
 license: mit
+tags:
+- cortex45m
+- turkish
+- text-correction
+- nlp
+- tensorflow
+- tuncerlab
 language:
 - tr
 ---
+Cortex45M — Turkish Text Correction
+<p align="center">""Model" (https://img.shields.io/badge/Model-Cortex45M-2563eb?style=for-the-badge)" (https://huggingface.co/korteks/cortex45m-metin-düzeltme)
+""Parameters" (https://img.shields.io/badge/Parameters-45M-7c3aed?style=for-the-badge)" (https://huggingface.co/korteks/cortex45m-metin-düzeltme)
+""Framework" (https://img.shields.io/badge/Framework-TensorFlow-ff8c00?style=for-the-badge&logo=tensorflow&logoColor=white)" (https://www.tensorflow.org/)
+""License" (https://img.shields.io/badge/License-MIT-16a34a?style=for-the-badge)" (https://opensource.org/license/mit)
+A Turkish-focused neural network model designed for text correction.
+Türkçe metin düzeltme amacıyla geliştirilen, Türkçeye odaklı bir sinir ağı modeli.
+</p>---
+🇹🇷 Türkçe
+Cortex45M nedir?
+Cortex45M, TuncerLab bünyesinde geliştirilen, yaklaşık 45 milyon parametreye sahip bir yapay zekâ modelidir. Model, Türkçe metin düzeltme görevine odaklanır ve TensorFlow tabanlıdır.
+Amaç, Türkçe metinlerdeki hataların düzeltilmesine yönelik deneysel bir model sunmak ve Türkçe doğal dil işleme çalışmalarına katkıda bulunmaktır.
+Model bilgileri
+Özellik| Değer
+Model adı| Cortex45M
+Parametre sayısı| Yaklaşık 45 milyon
+Temel görev| Metin düzeltme
+Dil odağı| Türkçe
 Framework| TensorFlow
+Eğitim veri külliyatı| Yaklaşık 10 GB
+Eğitim adımları| 28.000
+Lisans| MIT
+Temel özellikler
+- Türkçe metin düzeltmeye odaklanır.
+- Yaklaşık 45 milyon parametre içerir.
+- TensorFlow tabanlıdır.
+- Büyük ölçekli dil modellerinden daha küçük bir model boyutuyla deneyler yapılmasını sağlar.
+- Türkçe doğal dil işleme ve model eğitimi çalışmalarında kullanılmak üzere geliştirilmiştir.
+Kullanım amacı
+Cortex45M, Türkçe metin düzeltme üzerine araştırma ve geliştirme çalışmaları için hazırlanmıştır. Gerçek performansı, kullanılan veri setine ve değerlendirme yöntemine göre test edilmelidir.
+Not: Modelin belirli bir doğruluk oranına veya üretim ortamında kullanıma hazır olduğuna dair doğrulanmış bir benchmark burada sunulmamaktadır.
+Eğitim bilgileri
+Modelin geliştirme sürecinde yaklaşık 10 GB büyüklüğünde bir veri külliyatı ve 28.000 eğitim adımı kullanılmıştır.
+Veri miktarı tek başına modelin başarısını göstermez. Veri kalitesi, veri çeşitliliği, ön işleme, eğitim ayarları ve bağımsız değerlendirme sonuçları da önemlidir.
+Lisans
+Model deposunda MIT lisansı belirtilmiştir. Kullanım, dağıtım ve yeniden düzenleme koşulları için depodaki "LICENSE" dosyasını inceleyin. Eğitim verilerinin lisans ve dağıtım koşulları ayrıca değerlendirilmelidir.
+---
+🇬🇧 English
+What is Cortex45M?
+Cortex45M is a TensorFlow-based AI model developed under TuncerLab, with approximately 45 million parameters. It focuses on Turkish text correction.
+The project aims to provide an experimental model for correcting Turkish text and contributing to Turkish natural language processing research and development.
+Model details
+Property| Value
+Model name| Cortex45M
+Parameter count| Approximately 45 million
+Primary task| Text correction
+Language focus| Turkish
+Framework| TensorFlow
+Training corpus| Approximately 10 GB
+Training steps| 28,000
+License| MIT
+Key features
+- Focused on Turkish text correction.
+- Approximately 45 million parameters.
+- Built with TensorFlow.
+- Suitable for experimentation with a smaller model architecture.
+- Developed for Turkish NLP research and model-training experiments.
+Intended use
+Cortex45M is intended for research and development involving Turkish text correction. Its practical performance should be evaluated using suitable test datasets and metrics.
+Note: No verified benchmark or accuracy score is claimed on this model card.
+Training information
+The model was trained using a corpus of approximately 10 GB over 28,000 training steps.
+Corpus size alone does not establish model quality. Dataset quality, diversity, preprocessing, training configuration, and independent evaluation also matter.
+License
+The repository specifies the MIT license. Please review the repository's "LICENSE" file for the applicable terms. The licensing and redistribution terms of the training data should be checked separately.
 ---
+Project
+Cortex45M — TuncerLab
+"View the model repository on Hugging Face" (https://huggingface.co/korteks/cortex45m-metin-düzeltme)
+Developed by TuncerLab.