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from datasets import load_dataset
def preprocess_opc_coder(tokenizer, max_length):
ds = load_dataset("OpenCoder-LLM/opc-sft-stage2", "educational_instruct")['train']
def process_sample(sample):
# Tokenize instruction and output separately
instruction_tokens = tokenizer(sample['instruction'], add_special_tokens=False)['input_ids']
output_tokens = tokenizer(sample['output'], add_special_tokens=False)['input_ids']
# Combine instruction and output
input_ids = instruction_tokens + output_tokens
# Pad to max_length
if len(input_ids) < max_length:
input_ids = input_ids + [tokenizer.pad_token_id] * (max_length - len(input_ids))
elif len(input_ids) > max_length:
input_ids = input_ids[:max_length]
# Set prefix_cutoff to the length of the instruction
prefix_cutoff = len(instruction_tokens)
return {
'input_ids': input_ids,
'prefix_cutoff': prefix_cutoff
}
processed_ds = ds.map(process_sample, remove_columns=ds.column_names)
return processed_ds
def preprocess_human_eval(tokenizer, max_length):
ds = load_dataset("openai/openai_humaneval")['test']
def process_sample(sample):
# Tokenize prompt and canonical_solution separately
prompt_tokens = tokenizer(sample['prompt'], add_special_tokens=False)['input_ids']
solution_tokens = tokenizer(sample['canonical_solution'], add_special_tokens=False)['input_ids']
# Combine prompt and solution
input_ids = prompt_tokens + solution_tokens
# Pad to max_length
if len(input_ids) < max_length:
input_ids = input_ids + [tokenizer.pad_token_id] * (max_length - len(input_ids))
elif len(input_ids) > max_length:
input_ids = input_ids[:max_length]
# Set prefix_cutoff to the length of the prompt
prefix_cutoff = len(prompt_tokens)
return {
'input_ids': input_ids,
'prefix_cutoff': prefix_cutoff
}
processed_ds = ds.map(process_sample, remove_columns=ds.column_names)
return processed_ds |