Datasets:

Sediba-AI
/

eduintel-faiss

license: cc-by-nc-sa-4.0
language:
  - nso
  - en
task_categories:
  - text-retrieval
  - question-answering
size_categories:
  - 1K<n<10K
pretty_name: EduIntel FAISS Index v1
tags:
  - sepedi
  - sesotho-sa-leboa
  - northern-sotho
  - south-africa
  - low-resource
  - rag
  - faiss
  - vector-search
  - embeddings
  - eduintel

EduIntel FAISS Index v1

A FAISS vector index of Sepedi (Sesotho sa Leboa) text chunks for the EduIntel RAG pipeline, built by Sediba AI.

This is not a model — it is a retrieval index. It stores chunked Sepedi text and their embeddings so that a retriever can find relevant Sepedi content for a query, which is then fed to a generative model (e.g. Sedibaai/SedibaLM) for answer generation.

File	Purpose
`eduintel.index`	The FAISS index (flat or IVF vector store)
`chunks.pkl`	The chunked Sepedi text aligned to the index rows
`model.txt`	Embedding model used: `Sediba-AI/xlmr-sepedi` (XLM-RoBERTa-base, further pre-trained on Sepedi)
`.gitattributes`	Git LFS pointer

Usage

import faiss, pickle

index = faiss.read_index("eduintel.index")
chunks = pickle.load(open("chunks.pkl", "rb"))

# embed your query with the SAME model listed in model.txt
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained("Sediba-AI/xlmr-sepedi")
mdl = AutoModel.from_pretrained("Sediba-AI/xlmr-sepedi")
inputs = tok("Your Sepedi query", return_tensors="pt", padding=True, truncation=True)
emb = mdl(**inputs).last_hidden_state.mean(dim=1).detach().numpy()

D, I = index.search(emb, k=5)
retrieved = [chunks[i] for i in I[0]]

Important: you must embed with the same model recorded in model.txt (Sediba-AI/xlmr-sepedi). A different embedding model produces vectors in an incompatible space and the retrieval silently returns garbage.

What this feeds

This index is the retrieval layer of the EduIntel application — the teacher-facing education intelligence surface of the TST platform. The flow is:

Sepedi query → xlmr-sepedi embed → FAISS retrieval → Sepedi chunks → SedibaLM → response

Licence

CC BY-NC-SA 4.0 — attribution, non-commercial, share-alike.

This is the interim licence. The chunked text derives from the Sediba Sepedi training corpora (Sediba-AI/sepedi-training-v1), which are governed by NOODL (Northern-Sotho Open Data Licence) pending legal review. The FAISS index is a derivative work of that corpus and inherits its licence obligations.

Commercial use requires a separate agreement with Sediba AI NPC.

Limitations

No formal data-licence audit has been completed on the underlying corpus — see the sepedi-training-v1 dataset card.
Chunk quality is not scored. The index mixes curated, scraped and synthetic material.
Embedding model is a research artefact (xlmr-sepedi has no published evaluation).
Not a knowledge base — it is a retrieval index. It cannot answer questions by itself.

About

Built by Sediba AI — sovereign AI for South African languages, starting with Sepedi (~4.7 million speakers), Mankweng, Limpopo, South Africa.