Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,48 @@
+# GaroVec v1.0 — Hybrid English↔Garo Embeddings
+
+## Overview
+**GaroVec v1.0** is the *first publicly documented Latin-script Garo embedding model*.  
+It combines:
+- **FastText embeddings** (English + Garo)
+- **Cross-lingual alignment** using Procrustes rotation
+- **Frequency-based bilingual dictionary** for high-confidence word translations
+
+This hybrid design provides both **semantic embeddings** and **direct dictionary lookups**, making it useful for cross-lingual tasks like translation support, lexicon building, and low-resource NLP research.
+
+---
+
+## Training
+- **Data size**: ~2,500 English ↔ Garo parallel sentences  
+  (synthetic English generated with open models, manually translated by native Garo speakers)  
+- **Method**:
+  - FastText skipgram (300-dimensional vectors, char n-grams 3–6, 25 epochs)
+  - Linear alignment (Procrustes) between English and Garo vector spaces
+  - Frequency-based dictionary extracted from the parallel corpus
+- **Artifacts**:
+  - `garovec_garo.bin` — Garo FastText embeddings
+  - `garovec_english.bin` — English FastText embeddings
+  - `garovec_alignment_matrix.npy` — alignment matrix
+  - `garovec_model.pkl` — final hybrid model with dictionary + embeddings
+
+---
+
+## Usage
+
+```python
+import pickle
+import fasttext
+import numpy as np
+
+# Load hybrid model data
+with open("garovec_model.pkl", "rb") as f:
+    garovec_data = pickle.load(f)
+
+# Load embeddings
+garo_model = fasttext.load_model("garovec_garo.bin")
+english_model = fasttext.load_model("garovec_english.bin")
+W = np.load("garovec_alignment_matrix.npy")
+
+# Example: get nearest Garo words for English word
+vec = english_model.get_word_vector("love")
+aligned_vec = vec @ W
+candidates = [garo_model.get_word_vector(w) for w in garo_model.words[:100]]