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---
language: ht
license: mit
tags:
- haitian-creole
- tokenization
- part-of-speech-tagging
- dependency-parsing
- spacy
---
# ht_core_news_sm
**Language:** Haitian Creole (ht)
**Type:** spaCy pipeline (tokenizer, POS tagger, dependency parser)
**Size:** Small (optimized for efficiency)
## Training
- Trained on ~3,300 manually corrected CoNLL-U sentences following Universal Dependencies guidelines.
- Data includes formal Haitian Creole texts (e.g., articles, religious texts, educational material).
- No pretrained word vectors were used (pure end-to-end pipeline).
- CoNLL-U Data: https://github.com/JephteyAdolphe/UD_Haitian_Creole-Adolphe
## Capabilities
- Tokenization (including contractions and informal forms common in Haitian Creole)
- Part-of-Speech (POS) tagging based on Universal POS tags
- Dependency parsing (basic syntactic parsing)
## Intended Use
- NLP research on low-resource languages
- Language technology development for Haitian Creole
- Educational or linguistic applications
## Limitations
- No named entity recognition (NER) currently included.
- Trained primarily on formal Haitian Creole; performance may vary on very informal or highly dialectal texts.
- Small dataset: best for prototyping, research, and early-stage projects.
## Example Usage
```python
import spacy
texts = [
"Si'm ka vini, m'ap pale ak li.", "M ap teste model lan (pou kounye a).",
"Map manje gato a pandan map gade televizyon lem lakay mwen.",
"M ap pale ak ou le w vini demen.", "M'ap vini, eske wap la avek lajan'm? Si ou, di'l non pou fre'w."
]
nlp = spacy.load("ht_core_news_sm")
for text in texts:
doc = nlp(text)
# Tokenization, POS tagging, Lemmatization, Dependency parsing
print("Tokens, NORM, POS (tag), Dependency:")
print(len(doc))
for token in doc:
print(f"{token.text} | {token.norm_} | {token.tag_} | {token.dep_}")
print("\n")
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