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Model: soynade-research/Oolel-Corrector
Source: Original Platform

README.md

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+---
+license: agpl-3.0
+datasets:
+- soynade-research/Wolof-Non-Standard-Orthography
+language:
+- wo
+base_model:
+- soynade-research/Oolel-Small-v0.1
+tags:
+- wolof
+- orthography
+- text-normalization
+- fine-tuned
+- causal-lm
+pipeline_tag: text-generation
+---
+
+# Wolof-Spelling-Corrector
+
+A fine-tuned version of [Oolel-Small-v0.1](https://huggingface.co/soynade-research/Oolel-Small-v0.1) trained to convert informal, social-media Wolof into standard  orthography.
+
+Wolof is primarily an oral language and most speakers are not formally taught the written standard. 
+Text on platforms like YouTube, WhatsApp, or Facebook is spelled phonetically, heavily influenced by French orthographic conventions, 
+and frequently mixes Wolof with French or English. 
+This means that raw social-media data is essentially unusable for training or evaluating NLP systems without a normalization step.
+
+This model converts informal Wolof text into formal standard orthography while leaving code-switched French and English segments untouched.
+
+# Usage
+## 1. HuggingFace pipeline
+```python
+from transformers import pipeline
+import torch
+
+pipe = pipeline(
+    "text-generation",
+    model="soynade-research/Oolel-Corrector",
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+input = "Normalize this text: Kou guem ni biss dina niew nga am dom bou goor toude ko seydina mouhamat rek lalal bouton j'aime"
+messages = [{"role": "user", "content": input}]
+
+result = pipe(
+    messages,
+    max_new_tokens=512,
+
+)
+
+print(result[0]["generated_text"][-1]["content"])
+# Ku gëm ni bés dina ñëw nga am doom bu góor tudde ko Seydina Muhamed rekk laalal butoŋ j'aime.
+```
+
+## 1. With AutoModel for more control
+
+You can this with system prompts when you want to control the model's behaviour more explicitly, for example to enforce specific output formatting or language instructions.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "soynade-research/Oolel-Corrector"
+device = "cuda"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+SYSTEM_PROMPT = (
+        "Fix the orthography of this Wolof social media text. "
+        "Apply standard rules: correct vowels with diacritics, restore geminates, fix French-influenced spellings. "
+        "Keep mixed French/English as-is. Reply only with <CORRECTION>corrected text</CORRECTION>."
+    )
+
+def correct(text, system_prompt=SYSTEM_PROMPT, max_new_tokens=512, temperature=0.1):
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user",   "content": text}
+    ]
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    inputs = tokenizer([prompt], return_tensors="pt").to(device)
+    generated_ids = model.generate(
+        inputs.input_ids,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature
+    )
+    generated_ids = [
+        output[len(inp):]
+        for inp, output in zip(inputs.input_ids, generated_ids)
+    ]
+    return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+text = "man mom khawma sakh li loumou done. Niom gneup dem naniou dakar ngir vacances scolaires yi"
+print(correct(text))
+# <CORRECTION>Man moom xawma sax li lu mu doon. Ñoom ñépp dem nañu Dakar ngir vacances scolaires yi.</CORRECTION>
+```
+
+
+# Where could you use Oolel-Corrector:
+
+- **Dataset creation and cleaning.** Raw Wolof social-media corpora can be normalized at scale before being used for downstream training, annotation, or evaluation.
+- **Processing Layer.** Any pipeline that operates on Wolof text: sentiment analysis, topic classification, machine translation will perform more 
+consistently on standardized input. This model can serve as a preprocessing step.
+- **Keyboard and writing tools.** Integrated into a mobile or web interface, the model can suggest standardized spelling to users writing in Wolof, helping close the gap between informal and standard written usage.
+
+## Limitations
+
+- **Correction only.** This model normalizes orthography, it does not translate. If you want to convert informal spoken-style Wolof directly into French or English, you need to pair this model with a translation model: normalize first, then translate.
+