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103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
#!/usr/bin/env python
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# # Simple Model Training Example
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#
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# This is a simple example of how to use the LLM model type to train
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# a zero shot classification model. It uses the facebook/opt-350m model
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# as the base LLM model.
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# Import required libraries
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import logging
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import shutil
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import pandas as pd
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import yaml
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from ludwig.api import LudwigModel
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# clean out prior results
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shutil.rmtree("./results", ignore_errors=True)
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review_label_pairs = [
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{"review": "I loved this movie!", "label": "positive"},
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{"review": "The food was okay, but the service was terrible.", "label": "negative"},
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{"review": "I can't believe how rude the staff was.", "label": "negative"},
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{"review": "This book was a real page-turner.", "label": "positive"},
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{"review": "The hotel room was dirty and smelled bad.", "label": "negative"},
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{"review": "I had a great experience at this restaurant.", "label": "positive"},
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{"review": "The concert was amazing!", "label": "positive"},
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{"review": "The traffic was terrible on my way to work this morning.", "label": "negative"},
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{"review": "The customer service was excellent.", "label": "positive"},
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{"review": "I was disappointed with the quality of the product.", "label": "negative"},
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{"review": "The scenery on the hike was breathtaking.", "label": "positive"},
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{"review": "I had a terrible experience at this hotel.", "label": "negative"},
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{"review": "The coffee at this cafe was delicious.", "label": "positive"},
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{"review": "The weather was perfect for a day at the beach.", "label": "positive"},
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{"review": "I would definitely recommend this product.", "label": "positive"},
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{"review": "The wait time at the doctor's office was ridiculous.", "label": "negative"},
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{"review": "The museum was a bit underwhelming.", "label": "neutral"},
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{"review": "I had a fantastic time at the amusement park.", "label": "positive"},
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{"review": "The staff at this store was extremely helpful.", "label": "positive"},
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{"review": "The airline lost my luggage and was very unhelpful.", "label": "negative"},
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{"review": "This album is a must-listen for any music fan.", "label": "positive"},
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{"review": "The food at this restaurant was just okay.", "label": "neutral"},
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{"review": "I was pleasantly surprised by how great this movie was.", "label": "positive"},
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{"review": "The car rental process was quick and easy.", "label": "positive"},
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{"review": "The service at this hotel was top-notch.", "label": "positive"},
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]
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df = pd.DataFrame(review_label_pairs)
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config = yaml.safe_load("""
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model_type: llm
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base_model: facebook/opt-350m
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generation:
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temperature: 0.1
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top_p: 0.75
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top_k: 40
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num_beams: 4
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max_new_tokens: 64
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prompt:
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task: "Classify the sample input as either negative, neutral, or positive."
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input_features:
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-
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name: review
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type: text
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output_features:
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-
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name: label
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type: category
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preprocessing:
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fallback_label: "neutral"
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decoder:
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type: category_extractor
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match:
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"negative":
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type: contains
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value: "positive"
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"neutral":
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type: contains
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value: "neutral"
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"positive":
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type: contains
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value: "positive"
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""")
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# Define Ludwig model object that drive model training
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model = LudwigModel(config=config, logging_level=logging.INFO)
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# initiate model training
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(
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train_stats, # dictionary containing training statistics
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preprocessed_data, # tuple Ludwig Dataset objects of pre-processed training data
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output_directory, # location of training results stored on disk
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) = model.train(
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dataset=df, experiment_name="simple_experiment", model_name="simple_model", skip_save_processed_input=True
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)
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training_set, val_set, test_set, _ = preprocessed_data
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# batch prediction
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preds, _ = model.predict(test_set, skip_save_predictions=False)
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print(preds)
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