LSTM Text Generation Model

This model was trained using TensorFlow/Keras for financial article generation tasks.

Model Details

Model Type: LSTM
Framework: TensorFlow/Keras
Task: Text Generation
Vocabulary Size: 41376
Architecture: Long Short-Term Memory (LSTM)

Usage

from huggingface_hub import snapshot_download
import tensorflow as tf
import json
import pickle
import numpy as np

# Download model files
model_path = snapshot_download(repo_id="firobeid/L4_LSTM_financial_article_generator")

# Load the LSTM model
model = tf.keras.models.load_model(f"{model_path}/lstm_model")

# Load tokenizer
try:
    # Try JSON format first
    with open(f"{model_path}/tokenizer.json", 'r', encoding='utf-8') as f:
        tokenizer_json = f.read() 
    tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_json)
except FileNotFoundError:
    # Fallback to pickle format
    with open(f"{model_path}/tokenizer.pkl", 'rb') as f:
        tokenizer = pickle.load(f)

# Text generation function
def generate_text(input_text, num_words=10):
    # Preprocess input
    X = np.array(tokenizer.texts_to_sequences([input_text])) - 1
    
    # Generate predictions
    output_text = []
    for i in range(num_words):
        y_proba = model.predict(X, verbose=0)[0]
        pred_word_ind = np.argmax(y_proba, axis=-1) + 1
        pred_word = tokenizer.index_word[pred_word_ind[-1]]
        
        input_text += ' ' + pred_word
        output_text.append(pred_word)
        X = np.array(tokenizer.texts_to_sequences([input_text])) - 1
    
    return ' '.join(output_text)

# Example usage
# Start with these tags: <business>, <entertainment>, <politics>, <sport>, <tech>
result = generate_text("<tech> The future of artificial intelligence", num_words=15)
print(result)

Training

This model was trained on text data using LSTM architecture for next-word prediction.

Limitations

Model performance depends on training data quality and size
Generated text may not always be coherent for longer sequences
Model architecture is optimized for the specific vocabulary it was trained on