ai-address-lib 0.4

Library for address model predictions

Custom ALBERT Text Classification with Keras

This project provides a custom implementation of text classification using ALBERT, LSTM, and custom Keras layers. It includes model loading, tokenization, predictions, and custom layers for handling class names and ALBERT embeddings.

Files Overview

model_loader.py

This module is responsible for loading a pre-trained Keras model with custom objects.

Functions:

• load_custom_model(model_path): Loads the model with custom layers ClassNamesLayer and BertLayer.

tokenizer_utils.py

This module provides utilities for loading a pre-trained ALBERT tokenizer and tokenizing input texts.

Functions:

• load_tokenizer(): Loads the ALBERT tokenizer (albert-base-v2).
• tokenize_texts(texts, tokenizer, max_len): Tokenizes input texts and returns token IDs and attention masks.

predictions.py

This module handles model predictions and formatting the output. It provides utilities to make predictions and process them for output.

Functions:

• get_classes_from_model(model): Retrieves class names from the ClassNamesLayer.
• make_predictions(model, tokenizer, texts, max_len): Tokenizes the input texts and generates predictions from the model.
• process_predictions(text, predictions, tokenizer, classes, write_func): Processes the predictions and writes the formatted output using the specified write function.
• write_current_word(classes, write_func, pred_values, idx, current_word): Writes the predicted class for the current word to the output.
• write_predictions_to_file(file, text, predictions, tokenizer, classes): Writes predictions to a file.
• print_predictions(text, predictions, tokenizer, classes): Prints predictions to the console.

class_names_layer.py

This module defines the custom ClassNamesLayer used to store class names in the Keras model.

Class:

• ClassNamesLayer: A custom Keras layer to handle class names. It stores the class names and can be serialized and restored.

bert_layer.py

This module defines the custom BertLayer used to integrate the pre-trained ALBERT model into a Keras model.

Class:

• BertLayer: A custom Keras layer that loads and uses the ALBERT model (albert-base-v2) for embeddings.

Example Usage

from transformers import AlbertTokenizer
from model_loader import load_custom_model
from predictions import get_classes_from_model, make_predictions, print_predictions
from tokenizer_utils import load_tokenizer

# Load the pre-trained model
model = load_custom_model('path_to_model.keras')

# Load the ALBERT tokenizer
tokenizer = load_tokenizer()

# Example texts to classify
texts = ["This is a test sentence for classification"]

# Get class names from the model
classes = get_classes_from_model(model)

# Make predictions
predictions = make_predictions(model, tokenizer, texts, max_len=128)

# Print predictions to console
print_predictions(texts[0], predictions, tokenizer, classes)

Installation

To install the necessary dependencies, use the following command:

pip install ai_address_lib

License

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