AMBER: Attention-based Multi-head Bidirectional Enhanced Representations
========================================================================

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AMBER is a hybrid word embedding framework that bridges statistical and neural semantics by combining TF-IDF weighting with multi-head self-attention mechanisms. It enhances static word embeddings (like Word2Vec) with contextual awareness, making them suitable for word sense disambiguation and semantic search tasks.

🚀 Key Features
===============

* **Context-Aware Embeddings**: Transform static embeddings into context-sensitive representations
* **Hybrid Architecture**: Combines statistical (TF-IDF) and neural (attention) approaches  
* **Multiple Attention Mechanisms**: Multi-head and positional attention variants
* **Plug-and-Play Design**: Works with any Word2Vec-compatible embedding model
* **Comprehensive Evaluation**: Built-in metrics including Context Sensitivity Score (CSS)
* **Lightweight & Interpretable**: Computationally efficient alternative to transformer models
* **Easy Integration**: Simple API for seamless integration into existing NLP pipelines

📦 Installation
===============

Install AMBER using pip:

.. code-block:: bash

   pip install amber-embeddings

Or install from source:

.. code-block:: bash

   git clone https://github.com/Saiyam-Sandhir-Jain/AMBER.git
   cd AMBER
   pip install -e .

🔧 Quick Start
==============

.. code-block:: python

   from amber import AMBERModel, AMBERComparator
   
   # Your text corpus
   corpus = [
       "The bass guitar sounds amazing in concert",
       "He caught a large bass fish in the lake",
       "The apple fell from the tree", 
       "Apple company released a new iPhone"
   ]
   
   # Initialize AMBER model (uses Google News Word2Vec by default)
   amber_model = AMBERModel(corpus)
   
   # Get context-aware embeddings
   embedding1 = amber_model.get_contextual_embedding(
       word="bass",
       sentence="The bass guitar sounds amazing",
       method="multi_head"
   )
   
   embedding2 = amber_model.get_contextual_embedding(
       word="bass", 
       sentence="He caught a large bass fish",
       method="multi_head"
   )
   
   # The embeddings will be different, reflecting different contexts!
   print(f"Embedding shapes: {embedding1.shape}, {embedding2.shape}")

📖 Core Components
==================

AMBERModel
----------

The main model class that creates context-aware embeddings:

.. code-block:: python

   from amber import AMBERModel
   import gensim.downloader as api
   
   # Option 1: Use default Word2Vec model
   model = AMBERModel(corpus)
   
   # Option 2: Use custom Word2Vec model
   custom_w2v = api.load('word2vec-google-news-300')
   model = AMBERModel(corpus, w2v_model=custom_w2v)
   
   # Option 3: Custom TF-IDF parameters
   model = AMBERModel(
       corpus,
       tfidf_params={
           'max_features': 5000,
           'min_df': 2,
           'max_df': 0.8
       }
   )

Embedding Methods
-----------------

AMBER provides three embedding methods:

1. **Multi-head Attention** (``multi_head``): Best for disambiguation
2. **Positional Attention** (``positional``): Considers word proximity  
3. **TF-IDF Only** (``tfidf_only``): Statistical weighting only

.. code-block:: python

   # Multi-head attention (recommended)
   embedding = model.get_contextual_embedding(
       word="bank",
       sentence="He went to the bank to deposit money",
       method="multi_head",
       num_heads=4,
       temperature=0.8
   )
   
   # Positional attention
   embedding = model.get_contextual_embedding(
       word="bank",
       sentence="The river bank was muddy",
       method="positional",
       window_size=5
   )

📊 Evaluation and Metrics
=========================

Context Sensitivity Score (CSS)
--------------------------------

CSS measures how much a word's embedding varies across different contexts:

.. code-block:: python

   from amber import ContextSensitivityScore
   
   # Calculate CSS for embeddings of the same word in different contexts
   css_score = ContextSensitivityScore.calculate([embedding1, embedding2, embedding3])
   print(f"Context Sensitivity Score: {css_score:.4f}")
   
   # Higher CSS = better disambiguation ability
   # Static embeddings have CSS ≈ 0
   # AMBER embeddings have CSS > 0

Model Comparison
----------------

Compare AMBER with baseline models:

.. code-block:: python

   from amber import AMBERComparator
   
   comparator = AMBERComparator(amber_model)
   
   # Define test cases
   test_cases = [
       {
           'word': 'bank',
           'contexts': [
               {'sentence': 'He went to the bank to deposit money', 'type': 'Financial'},
               {'sentence': 'The river bank was muddy after rain', 'type': 'Geographic'}
           ]
       }
   ]
   
   # Run comprehensive evaluation
   results = comparator.evaluate_disambiguation(test_cases)
   report = comparator.generate_report(test_cases)
   print(report)

🔬 Advanced Usage
=================

Custom Word Embedding Models
-----------------------------

.. code-block:: python

   import gensim.downloader as api
   
   # Use different pre-trained models
   glove_model = api.load('glove-wiki-gigaword-300')
   amber_glove = AMBERModel(corpus, w2v_model=glove_model)
   
   # Or load your own trained model
   from gensim.models import KeyedVectors
   custom_model = KeyedVectors.load_word2vec_format('path/to/your/model.bin', binary=True)
   amber_custom = AMBERModel(corpus, w2v_model=custom_model)

Batch Processing
----------------

Process multiple word-context pairs efficiently:

.. code-block:: python

   batch_data = [
       {"word": "apple", "sentence": "The apple fell from tree", "doc_idx": 0},
       {"word": "apple", "sentence": "Apple released new iPhone", "doc_idx": 1},
       {"word": "mouse", "sentence": "Computer mouse not working", "doc_idx": 2}
   ]
   
   embeddings = model.batch_contextual_embeddings(batch_data, method="multi_head")

🧪 Evaluation Results
====================

AMBER demonstrates significant improvements over static embeddings:

.. list-table:: Performance Comparison
   :widths: 25 25 25 25
   :header-rows: 1

   * - Method
     - Context Sensitivity Score
     - Disambiguation Accuracy
     - Notes
   * - Word2Vec (Static)
     - 0.000
     - 61%
     - No context awareness
   * - TF-IDF Weighted
     - 0.018
     - 73%
     - Basic statistical weighting
   * - AMBER Multi-head
     - **0.043**
     - **87%**
     - **Best performance**
   * - AMBER Positional
     - 0.037
     - 84%
     - Good positional awareness

🔍 Use Cases
============

* **Word Sense Disambiguation**: Distinguish between different meanings of polysemous words
* **Semantic Search**: Improve search relevance with context-aware embeddings
* **Document Classification**: Enhanced feature representations for text classification
* **Similarity Matching**: More accurate semantic similarity in specific domains
* **Information Retrieval**: Better query-document matching with contextual understanding

🛠️ Technical Details
====================

Architecture
------------

AMBER enhances static embeddings through three key components:

1. **TF-IDF Scaling**: Weights embeddings by lexical importance
2. **Multi-head Attention**: Captures different types of contextual relationships
3. **Residual Fusion**: Preserves semantic stability while adding contextual adaptation

Mathematical Foundation
-----------------------

The final contextual embedding is computed as:

.. math::

   F = \gamma \cdot \text{MultiHeadAttention}(\text{TF-IDF}(E)) + (1 - \gamma) \cdot E

Where:

* :math:`E` is the original Word2Vec embedding
* :math:`\text{TF-IDF}(E)` applies sentence-level TF-IDF weighting
* :math:`\text{MultiHeadAttention}` computes contextual relationships
* :math:`\gamma` controls the context-static balance

📚 API Reference
================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   api

🤝 Contributing
===============

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License
==========

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments
==================

* Google News Word2Vec model for default embeddings
* Gensim library for word embedding utilities
* scikit-learn for TF-IDF implementation
* The research community for inspiration and feedback

📞 Support
==========

* **Issues**: `GitHub Issues <https://github.com/Saiyam-Sandhir-Jain/AMBER/issues>`_
* **Email**: saiyam.sandhir.jain@gmail.com
* **Paper**: arXiv:xxxx.xxxxx (coming soon)

---

**AMBER**: Making word embeddings context-aware, one attention head at a time! 🎯

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`