Update README.md

README.md

@@ -1,3 +1,184 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- ur
+base_model:
+- facebook/fasttext-km-vectors
+tags:
+- art
+---
+---
+# Card Metadata (Optional but Recommended)
+# You can fill these out directly in the Hugging Face UI or here.
+# Language: ur (Urdu)
+# Tasks:
+# - word-embeddings
+# Library:
+# - fasttext
+# Datasets:
+# - [Specify your dataset name here, e.g., your-dataset-name-on-hf, or just 'Custom Corpus']
+# Tags:
+# - urdu
+# - word-vectors
+# - embeddings
+# - fasttext
+# - unsupervised
+# - urdu-nlp
+# License: [Specify your license here, e.g., mit, apache-2.0, cc-by-4.0]
+---
+
+# Urdu Word Embeddings (fastText)
+
+## Model Description
+
+This is an unsupervised word embedding model for the Urdu language, trained using the fastText library. It generates high-dimensional vectors for Urdu words, capturing semantic and syntactic relationships based on their context in the training data.
+
+Unlike traditional Word2Vec, this fastText model was trained with character n-grams (`minn=[Your minn]`, `maxn=[Your maxn]`), which is particularly beneficial for morphologically rich languages like Urdu. This allows the model to:
+- Learn representations for subword units.
+- Generate meaningful vectors for words it hasn't seen during training (Out-of-Vocabulary or OOV words) by composing vectors from their character n-grams.
+
+The model outputs vectors of dimension `[Your vector_size]`.
+
+## Intended Use
+
+This model is intended for use in various Urdu Natural Language Processing (NLP) tasks, including:
+- Measuring semantic similarity between Urdu words.
+- Using word vectors as features for downstream tasks such as text classification, clustering, or named entity recognition.
+- Exploring word relationships and patterns within the vocabulary learned from the training corpus.
+- Obtaining vector representations for potentially unseen words based on their subword components.
+
+## Training Data
+
+This model was trained on a custom text corpus of Urdu sentences.
+
+-   **Dataset Source:** [Specify the source of your training data here. For example: "Collected from the COUNTER (COrpus of Urdu News TExt Reuse) dataset" or "A custom corpus gathered from [mention sources or domain]"].
+-   **Data Format:** The training data was processed into a single text file (`train.txt`) where each line represented a sentence or document, and words were separated by spaces.
+-   **Preprocessing:** Basic preprocessing was applied, including replacing common punctuation marks with spaces and normalizing whitespace. [Mention any other specific preprocessing steps you performed, e.g., lowercasing (less common for Urdu), handling numbers, removing specific symbols].
+
+[If your training data is publicly available or derived from a public source, provide a link or instructions on how others can access it.]
+[If the data is private, state that the data itself cannot be shared but the resulting model is being released.]
+
+## Training Procedure
+
+The model was trained using the unsupervised capabilities of the fastText library.
+
+-   **Algorithm:** Continuous Bag of Words (CBOW) model (`model=cbow`). [If you used `skipgram`, specify that instead and briefly explain why, e.g., "Skip-gram model (`model=skipgram`), often better for capturing representations of rare words."]
+-   **Parameters:** The following parameters were used during training:
+    -   `dim`: `[Your vector_size]` (Vector dimensionality)
+    -   `ws`: `[Your window_size]` (Context window size)
+    -   `minCount`: `[Your min_word_count]` (Minimum word frequency to be included in vocabulary)
+    -   `epoch`: `[Your epochs]` (Number of training epochs)
+    -   `neg`: `[Your negative_samples]` (Number of negative samples)
+    -   `minn`: `[Your minn]` (Minimum character n-gram length)
+    -   `maxn`: `[Your maxn]` (Maximum character n-gram length)
+    -   `thread`: 4 (Number of threads used)
+    -   [List any other significant parameters you modified]
+
+-   **Training Environment:** The training was performed in a Google Colab environment.
+
+## How to Use
+
+You can load and use this model using the fastText Python library.
+
+First, make sure you have fastText installed:
+```bash
+pip install fasttext
+import fasttext
+import numpy as np # For calculating cosine similarity
+
+# Path to the downloaded .bin model file
+model_path = "path/to/your/downloaded/urdu_fasttext.bin"
+
+# Load the fastText model
+try:
+    model = fasttext.load_model(model_path)
+    print("Model loaded successfully!")
+except ValueError as e:
+    print(f"Error loading model: {e}")
+    print("Ensure the file exists and is a valid fastText binary model.")
+    model = None # Set model to None if loading fails
+
+
+if model:
+    # --- Get Word Vector ---
+    word = "پاکستان" # Example Urdu word
+    print(f"\nVector for '{word}':")
+    try:
+        vector = model.get_word_vector(word)
+        print(f"Shape: {vector.shape}")
+        print(f"First 10 dimensions: {vector[:10]}")
+    except ValueError as e:
+        print(f"Error getting vector for '{word}': {e}. Word might be too short or have no valid subwords.")
+
+
+    # --- Find Nearest Neighbors (Similar Words) ---
+    word_for_neighbors = "اردو" # Example Urdu word
+    print(f"\nWords similar to '{word_for_neighbors}':")
+    try:
+        # Get top 10 most similar words
+        neighbors = model.get_nearest_neighbors(word_for_neighbors, k=10)
+        if neighbors:
+            print(neighbors)
+        else:
+            print(f"No similar words found for '{word_for_neighbors}'.")
+    except ValueError as e:
+        print(f"Error finding similar words for '{word_for_neighbors}': {e}. Word might not be valid.")
+
+
+    # --- Calculate Similarity Between Two Words (Manual Cosine Similarity) ---
+    word1 = "علم" # Example word 1
+    word2 = "روشنی" # Example word 2
+    print(f"\nSimilarity between '{word1}' and '{word2}':")
+    try:
+        vec1 = model.get_word_vector(word1)
+        vec2 = model.get_word_vector(word2)
+
+        # Calculate cosine similarity
+        norm1 = np.linalg.norm(vec1)
+        norm2 = np.linalg.norm(vec2)
+
+        if norm1 > 0 and norm2 > 0:
+            cosine_similarity = np.dot(vec1, vec2) / (norm1 * norm2)
+            print(f"Cosine similarity: {cosine_similarity}")
+        else:
+            print("Cannot compute similarity: zero vector detected for one or both words.")
+    except ValueError as e:
+        print(f"Error calculating similarity between '{word1}' and '{word2}': {e}. One or both words might not be valid.")
+
+    # --- Using the .vec file (Optional) ---
+    # The .vec file contains just the word vectors for words in the vocabulary.
+    # It can be loaded by other libraries like Gensim or spaCy.
+    # Note: This method *does not* utilize fastText's subword capabilities for OOV words.
+    # For fastText specific features, use the .bin file.
+    # Example (using gensim - requires gensim installation):
+    # from gensim.models import KeyedVectors
+    # vec_file_path = "path/to/your/downloaded/urdu_fasttext.vec"
+    # try:
+    #     # Load vectors in Word2Vec text format
+    #     word_vectors = KeyedVectors.load_word2vec_format(vec_file_path, binary=False)
+    #     print(f"\nLoaded {len(word_vectors.key_to_index)} vectors from .vec file using Gensim.")
+    #     # Example: Find similar words using Gensim
+    #     # print(word_vectors.most_similar("اردو"))
+    # except Exception as e:
+    #      print(f"Error loading .vec file with Gensim: {e}")
+
+
+else:
+    print("\nModel could not be loaded. Usage examples are skipped.")
+
+
+**Steps after creating the Model Card content:**
+
+1.  **Create a Model Repository on Hugging Face:** Go to huggingface.co, log in, click your profile picture -> "New model".
+2.  **Name your Model:** Choose a descriptive name (e.g., `urdu-fasttext-word-embeddings`).
+3.  **Set Visibility:** Choose Public or Private.
+4.  **Create Model:** This creates an empty repository.
+5.  **Upload Files:** Go to the "Files" tab of your new repository. You can either:
+    *   Click "Add file" and upload `urdu_fasttext.bin`, `urdu_fasttext.vec`, and your training script file.
+    *   Or, clone the repository locally and push the files using Git.
+6.  **Edit Model Card:** Go to the "Model card" tab. This is where you paste and format the content prepared above. You can edit it directly in the browser using Markdown.
+7.  **Fill in Placeholders:** Go through the content and replace all `[ ... ]` placeholders with your specific details (vector size, epochs, dataset source, license, your name, etc.).
+8.  **Format with Markdown:** Use the formatting options (headers, bold, code blocks) to make the card readable.
+9.  **Save Model Card:** Save the changes.
+
+Your model will then be available on Hugging Face with the documentation you've provided.