---
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")