---
language: bm
tags:
- bambara
- fasttext
- embeddings
- word-vectors
- african-nlp
- low-resource
license: apache-2.0
datasets:
- bambara-corpus
metrics:
- cosine_similarity
pipeline_tag: feature-extraction
---

# Bambara FastText Embeddings

## Model Description

This model provides FastText word embeddings for the Bambara language (Bamanankan), a Mande language spoken primarily in Mali. The embeddings capture semantic relationships between Bambara words and enable various NLP tasks for this low-resource African language.

**Model Type:** FastText Word Embeddings  
**Language:** Bambara (bm)  
**License:** Apache 2.0  

## Model Details

### Model Architecture
- **Algorithm:** FastText with subword information
- **Vector Dimension:** 300
- **Vocabulary Size:** 9,973 unique Bambara words
- **Training Method:** Skip-gram with negative sampling
- **Subword Information:** Character n-grams (enables handling of out-of-vocabulary words)

### Training Data

The model was trained on Bambara text corpora, building upon the work of [David Ifeoluwa Adelani's PhD dissertation](https://arxiv.org/abs/2507.00297) on natural language processing for African languages. 

### Intended Use

This model is designed for:

- **Semantic similarity tasks** in Bambara
- **Information retrieval** for Bambara documents
- **Cross-lingual research** involving Bambara
- **Cultural preservation** and digital humanities projects
- **Educational applications** for Bambara language learning
- **Foundation for downstream NLP tasks** in Bambara

## Installation

```bash
pip install gensim huggingface_hub scikit-learn numpy
```

## Usage

### Load the Model

```python
import tempfile
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download

model_id = "MALIBA-AI/bambara-fasttext"

# Download model files
model_path = hf_hub_download(repo_id=model_id, filename="bam.bin", cache_dir=tempfile.gettempdir())
vectors_path = hf_hub_download(repo_id=model_id, filename="bam.bin.vectors_ngrams.npy", cache_dir=tempfile.gettempdir())

# Load model
model = KeyedVectors.load(model_path)

print(f"Vocabulary size: {len(model.key_to_index)}")
print(f"Vector dimension: {model.vector_size}")
```

### Get a Word Vector

```python
vector = model["bamako"]
print(f"Shape: {vector.shape}")  # (300,)
```

### Find Similar Words

```python
similar_words = model.most_similar("dumuni", topn=10)
for word, score in similar_words:
    print(f"  {word}: {score:.4f}")
```

### Calculate Similarity Between Two Words

```python
from sklearn.metrics.pairwise import cosine_similarity

vec1 = model["muso"]
vec2 = model["cɛ"]
similarity = cosine_similarity([vec1], [vec2])[0][0]
print(f"Similarity: {similarity:.4f}")
```

### Convert Text to Vector (Average of Word Vectors)

```python
import numpy as np

def text_to_vector(text, model):
    words = text.lower().split()
    vectors = [model[w] for w in words if w in model.key_to_index]
    if not vectors:
        return np.zeros(model.vector_size)
    return np.mean(vectors, axis=0)

text_vec = text_to_vector("Mali ye jamana ɲuman ye", model)
print(f"Shape: {text_vec.shape}")  # (300,)
```

### Search for Similar Texts

```python
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

def search_similar_texts(query, texts, model, top_k=5):
    query_vec = text_to_vector(query, model)
    results = []
    for i, text in enumerate(texts):
        text_vec = text_to_vector(text, model)
        if np.any(text_vec):
            sim = cosine_similarity([query_vec], [text_vec])[0][0]
            results.append((sim, text, i))
    results.sort(key=lambda x: x[0], reverse=True)
    return results[:top_k]

texts = [
    "dumuni ɲuman bɛ here di",
    "bamako ye Mali faaba ye",
    "denmisɛnw bɛ kalan kɛ",
]

results = search_similar_texts("Mali jamana", texts, model)
for score, text, idx in results:
    print(f"  [{score:.4f}] {text}")
```

### Check if a Word Exists in the Vocabulary

```python
word = "bamako"
if word in model.key_to_index:
    print(f"'{word}' is in the vocabulary")
else:
    print(f"'{word}' is not in the vocabulary")
```

## Limitations

- Vocabulary is limited to 9,973 words (though subword information helps with OOV words)
- Performance depends on the quality and coverage of the training corpus
- May not capture domain-specific terminology well
- Embeddings reflect biases present in the training data

## References

```bibtex
@misc{bambara-fasttext,
  author = {MALIBA-AI},
  title = {Bambara FastText Embeddings},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/MALIBA-AI/bambara-fasttext}}
}
@phdthesis{adelani2025nlp,
  title={Natural Language Processing for African Languages},
  author={Adelani, David Ifeoluwa},
  year={2025},
  school={Saarland University},
  note={arXiv:2507.00297}
}
```

## License

This project is licensed under Apache 2.0.

## Contributing

This is a project part of the [MALIBA-AI](https://huggingface.co/MALIBA-AI) initiative with the mission **"No Malian Language Left Behind."**

---

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*"No Malian Language Left Behind"*