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data.py
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data.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import json
import random
from dataclasses import dataclass
from typing import Dict, List, Optional
from datasets import load_dataset
import pandas as pd
# for language modeling problems how long to use the prefix as
PREFIX_LENGTH: int = 100
@dataclass
class EvaluationExample:
input: str
output: str
class DatasetFormat:
CHAT_FORMAT: str = "chat_format"
CNN_DM_SUMMARIZATION: str = "cnn_dm_summarization"
CNN_DM_LM: str = "cnn_dm_lm"
XSUM_SUMMARIZATION: str = "xsum_summarization"
HUMAN_EVAL: str = "human_eval"
CUSTOM_JSONL: str = "custom_jsonl"
def LowercaseProcessingFunction(input: str) -> str:
return input.lower()
# TODO: fix or remove TOPv2 benchmarking
def prepare_evaluation_examples_chat_format(data_path: str) -> List[EvaluationExample]:
SINGLE_TURN_TEMPLATE: str = "\n[{role}]\n{message}\n[/{role}]"
evaluation_data_points = []
def stringify_conversation(conversation: List[Dict[str, str]]) -> str:
return "".join(
[
SINGLE_TURN_TEMPLATE.format(role=x["role"], message=x["message"])
for x in conversation
]
)
for line in open(data_path):
json_line = json.loads(line)
i: int = 0
while i < len(json_line["data"]):
if json_line["data"][i]["role"] == "PARSER":
evaluation_data_points.append(
EvaluationExample(
input=stringify_conversation(json_line["data"][1:i])
+ "\n[PARSER]\n",
output=stringify_conversation([json_line["data"][i]]),
)
)
i += 1
return evaluation_data_points
def prepare_cnn_dm_lm_format() -> List[EvaluationExample]:
evaluation_data_points = []
for data_point in load_dataset("cnn_dailymail", "3.0.0")["test"]:
words = data_point["article"].split()
evaluation_data_points.append(
EvaluationExample(
input=" ".join(words[:PREFIX_LENGTH]),
output=" ".join(words[PREFIX_LENGTH:]),
)
)
return evaluation_data_points
def prepare_cnn_dm_summarization_format(n_shot: int = 0, seed: int = 42) -> List[EvaluationExample]:
prompt_shots = ""
if n_shot > 0:
prompt_keys=["article", "highlights"]
shots = load_dataset("cnn_dailymail", name="3.0.0", split="train").shuffle(seed=seed).select(range(n_shot))
for i in range(n_shot):
prompt = "Article: " + shots[i][prompt_keys[0]] + "\nSummary: " + shots[i][prompt_keys[1]].replace("\n", "") + "\n"
prompt_shots += prompt
prompt_shots += "\n"
evaluation_data_points = []
for data_point in load_dataset("cnn_dailymail", name="3.0.0", split="test"):
article = data_point["article"]
highlights = data_point["highlights"]
evaluation_data_points.append(
EvaluationExample(
input=prompt_shots + f"Article: {article}\nSummary:",
output=f" {highlights}",
)
)
return evaluation_data_points
def prepare_xsum_summarization_format(n_shot: int = 0, seed: int = 42) -> List[EvaluationExample]:
prompt_shots = ""
if n_shot > 0:
prompt_keys=["document", "summary"]
shots = load_dataset("xsum", split="train").shuffle(seed=seed).select(range(n_shot))
for i in range(n_shot):
prompt = "Article: " + shots[i][prompt_keys[0]] + "\nSummary: " + shots[i][prompt_keys[1]].replace("\n", "") + "\n"
prompt_shots += prompt
prompt_shots += "\n"
evaluation_data_points = []
for data_point in load_dataset('xsum', split='test'):
article = data_point["document"]
highlights = data_point["summary"]
evaluation_data_points.append(
EvaluationExample(
input=prompt_shots + f"Article: {article}\nSummary:",
output=f" {highlights}",
)
)
return evaluation_data_points
def prepare_human_eval() -> List[EvaluationExample]:
evaluation_data_points = []
for data_point in load_dataset('openai_humaneval', split='test'):
evaluation_data_points.append(
EvaluationExample(
input=data_point["prompt"],
output=data_point["canonical_solution"],
)
)
return evaluation_data_points
def prepare_custom(data_path: str, prompt_field: str = "prompt", response_field: str = "response") -> List[EvaluationExample]:
evaluation_data_points = []
for _, data_point in pd.read_json(data_path, lines=True).iterrows():
evaluation_data_points.append(
EvaluationExample(
input=data_point[prompt_field],
output=data_point[response_field],
)
)
return evaluation_data_points
def get_data(
random_shuffle: bool,
num_samples: int,
dataset: str,
data_path: Optional[str] = None,
n_shot: int = 0,
seed: int = 42,
prompt_field: str = "prompt",
response_field: str = "response",
) -> List[EvaluationExample]:
if dataset == DatasetFormat.CHAT_FORMAT:
evaluation_data_points = prepare_evaluation_examples_chat_format(data_path)
elif dataset == DatasetFormat.CNN_DM_SUMMARIZATION:
evaluation_data_points = prepare_cnn_dm_summarization_format(n_shot=n_shot, seed=seed)
elif dataset == DatasetFormat.XSUM_SUMMARIZATION:
evaluation_data_points = prepare_xsum_summarization_format(n_shot=n_shot, seed=seed)
elif dataset == DatasetFormat.CNN_DM_LM:
evaluation_data_points = prepare_cnn_dm_lm_format()
elif dataset == DatasetFormat.HUMAN_EVAL:
evaluation_data_points = prepare_human_eval()
elif dataset == DatasetFormat.CUSTOM_JSONL:
evaluation_data_points = prepare_custom(data_path, prompt_field=prompt_field, response_field=response_field)
else:
raise NotImplementedError(f"Unknown dataset format {dataset}")
if random_shuffle:
random.shuffle(evaluation_data_points)
if num_samples:
evaluation_data_points = evaluation_data_points[:num_samples]
return evaluation_data_points