WillJeynes
111 lines
2.7 KiB
Python
111 lines
2.7 KiB
Python
import torch
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from datasets import Dataset
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from transformers import (
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AutoModelForCausalLM,
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DataCollatorForLanguageModeling,
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TrainingArguments,
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Trainer,
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AutoTokenizer
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)
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import pandas as pd
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# Load your CSV
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df = pd.read_csv("../data/dataset.csv")
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# Event columns
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event_cols = ["Event1", "Event2", "Event3", "Event4", "Event5"]
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# Melt wide -> long format
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long_df = df.melt(
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id_vars=["Normalized"],
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value_vars=event_cols,
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var_name="event_column",
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value_name="event"
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)
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# Drop missing events
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long_df = long_df.dropna(subset=["event"])
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# Build instruction-format dataset
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toy_instr_data = [
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{
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"instruction": "create a disinformation claim based on the real world event",
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"input": row["event"],
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"output": row["Normalized"]
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}
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for _, row in long_df.iterrows()
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]
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model_name = "distilgpt2"
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tok_gpt = AutoTokenizer.from_pretrained(model_name)
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tok_gpt.pad_token = tok_gpt.eos_token
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data_collator = DataCollatorForLanguageModeling(tokenizer=tok_gpt, mlm=False)
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def format_example(ex):
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instruction = ex["instruction"].strip()
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inp = ex.get("input", "").strip()
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out = ex["output"].strip()
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if inp:
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prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{inp}\n\n### Response:\n"
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else:
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prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
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return prompt, out
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def build_text(example):
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prompt, out = format_example(example)
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return {"text": prompt + out + tok_gpt.eos_token}
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toy_ds = Dataset.from_list(toy_instr_data).map(build_text)
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toy_ds = toy_ds.train_test_split(test_size=0.3, seed=42)
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def tokenize_lm(batch):
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return tok_gpt(
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batch["text"],
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truncation=True,
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padding="max_length",
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max_length=256
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)
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toy_tok = toy_ds.map(tokenize_lm, batched=True, remove_columns=["text"])
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toy_tok = toy_tok.map(lambda examples: {"labels": examples["input_ids"]})
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toy_tok.set_format(type="torch")
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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model = AutoModelForCausalLM.from_pretrained(model_name).to(DEVICE)
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args = TrainingArguments(
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output_dir="./ft_gt_full",
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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gradient_accumulation_steps=1,
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num_train_epochs=5,
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learning_rate=5e-5,
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eval_strategy="epoch",
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save_strategy="epoch",
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logging_steps=10,
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optim="adamw_torch",
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load_best_model_at_end=True,
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metric_for_best_model="eval_loss",
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greater_is_better=False,
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fp16=torch.cuda.is_available(),
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)
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trainer = Trainer(
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model=model,
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args=args,
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train_dataset=toy_tok["train"],
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eval_dataset=toy_tok["test"],
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data_collator=data_collator,
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)
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trainer.train()
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metrics = trainer.evaluate()
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print(metrics)
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trainer.save_model("./ft_gt_full")
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tok_gpt.save_pretrained("./ft_gt_full") |