create README.md

README.md

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+---
+language: dje
+tags:
+- fasttext
+- word-embeddings
+- zarma
+- nlp
+license: apache-2.0
+datasets:
+- 27Group/noisy_zarma
+---
+
+## Description
+This repository contains a pre-trained FastText model for the Zarma language. The model generates word embeddings for Zarma text, capturing semantic and contextual information for various NLP tasks.
+
+
+## Tasks
+- **Word Embeddings**: Generate vector representations for Zarma words.
+- **Part-of-Speech (POS) Tagging**: Provide features for POS tagging models.
+- **Text Classification**: Use embeddings for sentiment analysis or topic classification.
+- **Semantic Similarity**: Compute similarity between Zarma words or phrases.
+
+## Usage Examples
+
+### 1. Word Embeddings
+Load the FastText model to get word embeddings for Zarma text.
+
+```python
+import fasttext
+
+model = fasttext.load_model('zarma_fasttext.bin')
+
+word = "ay"
+embedding = model.get_word_vector(word)
+print(f"Embedding for '{word}': {embedding[:5]}...")
+```
+### 2. Semantic Similarity
+```python
+import fasttext
+import numpy as np
+
+model = fasttext.load_model('zarma_fasttext.bin')
+
+word1 = "ay"
+word2 = "ni"
+vec1 = model.get_word_vector(word1)
+vec2 = model.get_word_vector(word2)
+
+similarity = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2) + 1e-8)
+print(f"Similarity between '{word1}' and '{word2}': {similarity:.4f}")
+```
+
+## How to Use
+Install FastText: **pip install fasttext**
+
+Download **zarma_fasttext.bin** from this repository.
+
+Use the code snippets above to integrate the model into your NLP pipeline.
+
+## How to cite
+If you use this model in your work, please cite:
+```
+@misc{zarma_fasttext,
+  title     = {Pre-trained FastText Embeddings for Zarma},
+  author    = {Mamadou K. Keita and Christopher Homan},
+  year      = {2025},
+  howpublished = {\url{https://huggingface.co/27Group/zarma_fasttext}}
+}
+```