Upload LSTM model and tokenizer

.gitattributes

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README.md

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+---
+tags:
+- text-generation
+- lstm
+- tensorflow
+library_name: tensorflow
+pipeline_tag: text-generation
+---
+
+# 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
+
+```python
+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

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