Instructions to use Sediba-AI/xlmr-sepedi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sediba-AI/xlmr-sepedi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sediba-AI/xlmr-sepedi")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Sediba-AI/xlmr-sepedi") model = AutoModelForMaskedLM.from_pretrained("Sediba-AI/xlmr-sepedi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlmr-sepedi โ Sepedi masked-language model
XLM-RoBERTa-base, further pre-trained on Sepedi (Sesotho sa Leboa) text by Sediba AI.
This is a masked-language model โ it fills in blanks. It is not a chat or instruction model. For Sepedi text generation see Sedibaai/SedibaLM.
What is actually in this repo
Read from config.json in this repository:
| Architecture | XLMRobertaForMaskedLM |
| Model type | xlm-roberta |
| Hidden size | 768 |
| Layers | 12 |
| Vocab size | 250,002 (stock XLM-R multilingual) |
| Tokenizer | XLMRobertaTokenizer |
| Weights | 1.11 GB (model.safetensors, fp32) |
Usage
from transformers import pipeline
fill = pipeline("fill-mask", model="Sediba-AI/xlmr-sepedi")
fill("Batho ba <mask> Sepedi.")
Or for embeddings / as an NLU encoder:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained("Sediba-AI/xlmr-sepedi")
model = AutoModel.from_pretrained("Sediba-AI/xlmr-sepedi")
Intended use
- Sepedi fill-mask and masked-LM research
- An encoder backbone for downstream Sepedi classification tasks
- Embeddings for Sepedi retrieval and similarity
Limitations โ read before using
- No published evaluation. This model has no benchmark numbers in this card. Perplexity, fill-mask accuracy and downstream transfer were not recorded at publication time. Treat it as a research artefact and evaluate it on your own task before relying on it.
- Stock multilingual tokenizer. The 250,002-token XLM-R vocabulary is not Sepedi-optimised, so Sepedi fragments into more sub-word pieces than necessary.
- Not generative. A masked-LM cannot hold a conversation or follow instructions.
- Training corpus not documented in this card. Provenance is held in the Sediba vault, not restated here; no formal data-licence audit has been completed.
- No safety tuning.
Sediba Sepedi model family
| Model | Type | Use | Status |
|---|---|---|---|
| Sedibaai/SedibaLM | Generative (Qwen2.5-1.5B + QLoRA) | Sepedi text generation, chat | Shipped โ validated vs base |
| xlmr-sepedi (this repo) | Masked LM (XLM-R base) | Fill-mask, NLU encoder | Shipped โ unevaluated |
| xlmr-sepedi-sentiment | Binary classifier | Sepedi sentiment | Shipped โ unevaluated |
| sediba-XLM-R | Masked LM | Sepedi NLU anchor | Shipped |
Licence
Apache 2.0 for these weights, inheriting from XLM-RoBERTa-base upstream.
Training data is not released under this licence โ Sediba corpus material is governed separately (NOODL, in legal review). Open weights, restricted corpus.
About
Built by Sediba AI โ sovereign AI for South African languages, starting with Sepedi (~4.7 million speakers) and extending to the other official languages of South Africa.
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Model tree for Sediba-AI/xlmr-sepedi
Base model
FacebookAI/xlm-roberta-base