abnuel
/

yoruba_sent_boundary

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 ---
+language:
+  - yo
+license: apache-2.0
+base_model: Davlan/bert-base-multilingual-cased-finetuned-yoruba
+tags:
+  - yoruba
+  - low-resource-nlp
+  - sentence-boundary-detection
+  - token-classification
+  - bert
+  - african-nlp
+datasets:
+  - abnuel/yor_punctuation
+pipeline_tag: token-classification
 ---
+# Yoruba Sentence Boundary Detection Model
+A BERT-based token classification model for **sentence boundary detection in Yoruba text**. This model identifies where sentences begin and end in continuous Yoruba text — a foundational step for tokenization, translation, summarization, and other NLP pipelines.
+📄 **Dataset:** [abnuel/yor_punctuation](https://huggingface.co/datasets/abnuel/yor_punctuation) (1M–10M tokens)
+🔗 **Related:** [abnuel/yoruba_sent_boundary_2](https://huggingface.co/abnuel/yoruba_sent_boundary_2) — improved iteration
+## Model Description
+Sentence boundary detection (SBD) in Yoruba presents unique challenges: the language uses tonal diacritics, has complex morphology, and real-world Yoruba text is frequently unpunctuated or inconsistently punctuated. Standard rule-based SBD approaches designed for English fail to capture these linguistic patterns.
+This model takes a sequence labeling approach, tagging each token as a sentence boundary or continuation token, fine-tuned on a large Yoruba corpus.
+- **Base model:** Davlan/bert-base-multilingual-cased-finetuned-yoruba
+- **Task:** Token classification (sentence boundary detection)
+- **Language:** Yoruba (`yo`)
+- **Parameters:** 177.3M
+- **Architecture:** BERT
+## Labels
+| Label | Description |
+|-------|-------------|
+| `O`   | Not a sentence boundary |
+| `B-SENT` | Beginning / end of sentence boundary |
+*(Check `config.json` for the exact `id2label` mapping.)*
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+model_id = "abnuel/yoruba_sent_boundary"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForTokenClassification.from_pretrained(model_id)
+nlp = pipeline("token-classification", model=model, tokenizer=tokenizer)
+# Example: continuous Yoruba text without explicit sentence markers
+text = "Mo jókòó sí ilé Èmi yóò padà wá ní àárọ ọjọ́ kejì àwọn ará ilé mi dúpẹ́"
+result = nlp(text)
+print(result)
+```
 ## Training Details
+- **Fine-tuning approach:** Token classification head on top of Yoruba-adapted multilingual BERT
+- **Dataset:** [abnuel/yor_punctuation](https://huggingface.co/datasets/abnuel/yor_punctuation)
+- **Dataset size:** 1M–10M tokens
+- **Reference:** [arxiv:1910.09700](https://arxiv.org/abs/1910.09700) — Punctuation Restoration using Transformer Models for NLP tasks
+## Model Iterations
+This is the first version. For improved performance, see:
+- [abnuel/yoruba_sent_boundary_2](https://huggingface.co/abnuel/yoruba_sent_boundary_2) — refined training, improved boundary precision
+- [abnuel/yoruba_sent_boundary_3](https://huggingface.co/abnuel/yoruba_sent_boundary_3) — latest iteration
+## Limitations
+- Best suited for standard written Yoruba; performance may degrade on heavily code-switched or dialectal text.
+- Tonal diacritics (e.g., àáâ) should be present for optimal results; the model was not specifically evaluated on diacritic-stripped text.
+- As a pioneering tool for Yoruba SBD, evaluation benchmarks are limited — community evaluation and contributions are welcome.
+## Why This Matters
+Yoruba is spoken by ~45 million people but has minimal NLP infrastructure compared to European languages. Sentence boundary detection is foundational — without it, downstream tasks like machine translation, summarization, and speech-to-text post-processing are significantly impaired. This model is part of a broader effort to build the NLP toolchain for Yoruba and other low-resource African languages.
+## Related Models & Resources
+- [abnuel/yoruba_task1_punctuation_model](https://huggingface.co/abnuel/yoruba_task1_punctuation_model) — Punctuation restoration for Yoruba
+- [abnuel/yoruba_punctuation_2](https://huggingface.co/abnuel/yoruba_punctuation_2) — Updated punctuation model
+- [abnuel/yor_punctuation](https://huggingface.co/datasets/abnuel/yor_punctuation) — Shared training dataset
+## Citation
+```
+@misc{adegunlehin2025yoruba-sbd,
+  author = {Abayomi Adegunlehin},
+  title  = {Yoruba Sentence Boundary Detection Model},
+  year   = {2025},
+  url    = {https://huggingface.co/abnuel/yoruba_sent_boundary}
+}
+```