Hello Tiffany, You can use datasets from platforms like Kaggle (e.g., text data from news articles, books, or social media posts) or GitHub repositories with pre-compiled text data.
For Cleanup, Use Python libraries such as Beautiful Soup for HTML tag removal and NLTK or spaCy for text standardization and tokenization. For example, you might write a script to remove special characters and convert text to lowercase.
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import re
def clean_text(text):
text = re.sub(r'<.*?>', '', text) # Remove HTML tags
text = text.lower() # Convert to lowercase
text = re.sub(r'\W+', ' ', text) # Remove special characters
return text
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TRAINING:
Use frameworks like TensorFlow or PyTorch to train your model. For instance, fine-tune a pre-trained GPT-2 model on your cleaned dataset.
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
# Tokenize and prepare data
train_encodings = tokenizer(dataset['train'], truncation=True, padding=True)
# Define training arguments and train the model
training_args = TrainingArguments(output_dir='./results', num_train_epochs=1, per_device_train_batch_size=2)
trainer = Trainer(model=model, args=training_args, train_dataset=train_encodings)
trainer.train()
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For API Integration, Use Flask to create an API endpoint for your model. Here’s a simple example:
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from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/generate', methods=['POST'])
def generate_text():
data = request.get_json()
prompt = data['prompt']
# Generate text using your trained model
generated_text = model.generate(prompt)
return jsonify({'generated_text': generated_text})
if __name__ == '__main__':
app.run(debug=True)
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Read further here:
https://www.kaggle.com/getting-started
https://www.crummy.com/software/BeautifulSoup/doc/
https://www.tensorflow.org/tutorials
https://flask.palletsprojects.com/en/3.0.x/tutorial/
I hope this helps
