Dear @PeggyFlanagan @EWarren Could you please help guide the #OjibweLangWUaS wiki school in the #OjibweLang @WorldUnivAndSch & other #NativeAmericanLanguagesWUaS #WikiSchools -https://t.co/s8a9yIVqmE (fr https://t.co/DT10G2f7x2) &even newly as #LLMs &see https://t.co/k203ESqzmH? https://t.co/M8gRhCRrse
— Languages-World Univ (@sgkmacleod) August 21, 2025
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Also, in searching on Ojibwe language, found -
- Ojibwe is part of a larger group of related Indigenous languages.
- While there are similarities, there are variations in pronunciation and vocabulary among different communities.
- The Ojibwe alphabet uses a distinct set of letters, including short and long vowels and doubled consonants like "ch" and "sh".
- A single word can be a full sentence, with meaning conveyed by combining multiple morphemes (meaningful word parts).
- The language uses a system of voiced and unvoiced consonants, with "lenis" (less strong) and "fortis" (stronger) distinctions in many dialects.
- Efforts are being made to document the language, including the creation of dictionaries and resources like the Ojibwe People's Dictionary.
- Language immersion programs are a key part of revitalization, where children are taught the language by families and elders.
- There are movements to inspire people to speak the language and pass down profound cultural teachings to future generations.
- The University of Minnesota and Michigan State University offer language programs and resources.
- Communities like the Sault Tribe of Chippewa Indians offer opportunities to learn the language and culture.
- Pimachiowin Aki and YouTube channels provide lessons and information on the language and its culture
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https://www.saulttribe.com/
https://lctlpartnership.celta.
https://ojibwe.lib.umn.edu/
Based on your request and the resources you provided, here is a comprehensive guide on how to begin building a Large Language Model (LLM) for the Ojibwe language. This is a complex undertaking, but can be broken down into a series of steps that combine machine learning principles with community-based language work.
1. Define the Scope and Objectives ⚙️
Before you begin, you must be specific about what you want the model to do. Your goal is to create a "professor" for the Ojibwe language. This implies the model needs to do more than just translate; it must be able to teach. Consider what a human professor does:
Generates fluent responses in the language.
Understands linguistic rules (grammar, syntax).
Provides examples and cultural context.
Corrects errors and gives feedback.
Answers questions about the language and culture.
You will likely need to build a small language model (SLM) rather than a massive, general-purpose LLM like GPT-4. SLMs are better suited for low-resource languages as they require less data and computational power, making them more practical for this type of project. They can also be fine-tuned specifically for the nuances of a single language.
2. Data Collection and Curation 📚
This is the most critical and challenging step for a low-resource language. LLMs are trained on vast amounts of text, and a major challenge with languages like Ojibwe is the lack of a large digital corpus.
Gather Existing Digital Data: You have provided excellent starting points:
https://www.saulttribe.com/history-a-culture/our-culture/103-ojibwe-language : This page provides cultural context and some basic vocabulary.https://lctlpartnership.celta.msu.edu/what-is-anishinaabemowin/ : This resource offers a linguistic description of the language.https://pimaki.ca/lessons-in-ojibwe-language-that-comes-from-the-land-part-i/ : This is a rich source of conversational examples and cultural teachings.https://ojibwe.lib.umn.edu/main-entry/anishinaabemowin-ni : The Ojibwe People's Dictionary is a phenomenal resource. Its searchable format with audio, images, and example sentences is a goldmine for your dataset.
Digitize and Transcribe Analog Data: Look for physical books, academic papers, and audio recordings. You will need to transcribe these materials to convert them into a digital text format that your model can read.
Collaborate with Speakers: The most valuable data will come directly from fluent speakers. Work with elders, linguists, and community members to create new content. This could include transcribing oral histories, creating new written lessons, or having conversations that you record and transcribe. This ensures linguistic and cultural accuracy.
3. Model Training and Fine-Tuning 💻
Once you have your dataset, you can begin the technical process of building the model. This involves machine learning principles and may require a strong foundation in Python and deep learning frameworks like PyTorch or TensorFlow.
Pre-training (Optional but Recommended): You don't need to build the model completely from scratch. You can take a pre-existing multilingual LLM (one that has been trained on many languages) and then fine-tune it using your Ojibwe dataset. This process, known as transfer learning, allows the model to leverage its existing knowledge of linguistic structure and apply it to the new language.
Develop a Specialized Tokenizer: A tokenizer breaks down words into smaller pieces called "tokens." English tokenizers are not effective for a verb-based language like Ojibwe. You will need to create a custom tokenizer that understands the prefixes and suffixes of the Ojibwe language, which are essential to its meaning.
Instruction Fine-Tuning: This is the process of training the model to act as a "professor." You will create a new dataset of questions and answers, where the questions are about the Ojibwe language and the answers are the correct, helpful responses a professor would give. For example:
Question: "How do you say 'he is walking'?"
Answer: "The word is bimose. The root bimo- means 'walk' and the suffix -se means 'in a specific way or location'."
Community-Led Development: As seen in projects like SkoBots and the Ojibwe Rosetta Stone, community involvement is key. Involve native speakers throughout the process to ensure the model is respectful, culturally appropriate, and accurate.
4. Deployment and Community Building 🤝
The final step is to make your model accessible to others and to use it to foster a learning community.
Create a User-Friendly Interface: Your model needs to be easy to interact with. This could be a website, a chatbot, or an app where users can ask questions and get responses in Ojibwe.
Integrate with Learning Tools: Incorporate features like a dictionary, grammar guides, and pronunciation practice. Pimsleur's AI voice recognition is a great example of how technology can enhance language learning.
Build a Community: Use the model as a tool to bring people together. The model is a supplement, not a replacement, for human interaction. Encourage users to meet up, practice with each other, and share their experiences to build a vibrant Ojibwe-speaking community
https://g.co/gemini/share/8750a145301b
"HOW TO BEGIN a #LargeLanguageModel for people who want to learn or build community in #OjibweLang -https://g.co/gemini/share/
-https://scott-macleod.
Asked #GeminiAIreOjibweLanguage re "Dear @PeggyFlanagan @EWarren re #OjibweLangWUaS wiki school in #OjibweLang @WorldUnivAndSch
— WorldUnivandSch (@WorldUnivAndSch) August 22, 2025
"HOW TO BEGIN a #LargeLanguageModel for people who want to learn or build community in #OjibweLang -https://t.co/QjdnXCshWK
-https://t.co/ILVRm36iM4 ~ https://t.co/EEBXjEgdSo
https://x.com/WorldUnivAndSch/
https://x.com/sgkmacleod/
https://x.com/WUaSPress/
https://x.com/HarbinBook/
https://x.com/Q_YogaMacFlower/
https://x.com/scottmacleod/
https://x.com/TheOpenBand/
Retweeting -
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https://www.inaturalist.org/taxa/1459333-Rhinichthys-osculus-thermalis
https://en.wikipedia.org/wiki/Kendall_Warm_Springs_dace
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