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Runtime error
feat: writes basic pipeline for psychologist model
Browse files
psy.py
CHANGED
@@ -1,5 +1,8 @@
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from datasets import load_dataset
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from transformers import AutoTokenizer
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DATA_SEED = 9843203
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QUICK_TEST = True
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@@ -17,5 +20,37 @@ tokenised_dataset = dataset.map(tokenise_function, batched=True)
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# Different sized datasets will allow for different training times
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train_dataset =
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test_dataset =
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from datasets import load_dataset
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
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import numpy as np
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import evaluate
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DATA_SEED = 9843203
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QUICK_TEST = True
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# Different sized datasets will allow for different training times
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train_dataset = tokenised_datasets["train"].shuffle(seed=DATA_SEED).select(range(1000)) if QUICK_TEST else tokenised_datasets["train"].shuffle(seed=DATA_SEED)
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test_dataset = tokenised_datasets["test"].shuffle(seed=DATA_SEED).select(range(1000)) if QUICK_TEST else tokenised_datasets["test"].shuffle(seed=DATA_SEED)
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# Each of our Mtbi types has a specific label here
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model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Meta-Llama-3-8B", num_labels=16)
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# Using default hyperparameters at the moment
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training_args = TrainingArguments(output_dir="test_trainer")
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# A default metric for checking accuracy
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metric = evaluate.load("accuracy")
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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predictions = np.argmax(logits, axis=-1)
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return metric.compute(predictions=predictions, references=labels)
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# Extract arguments from training
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training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
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# Builds a training object using previously defined data
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=test_dataset,
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compute_metrics=compute_metrics,
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)
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# Finally, fine-tune!
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if __name__ == "__main__":
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trainer.train()
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