wehub-resource-sync
70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
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from copy import deepcopy
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from datasets import load_dataset
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# convert data for distill
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# GSM8K
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dataset = load_dataset("meta-math/GSM8K_zh")["train"]
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dataset.to_json("data/gsm8k_zh/GSM8K_zh.jsonl", force_ascii=False)
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# PaddlePaddle/GSM8K_distilled_zh
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dataset = load_dataset("PaddlePaddle/GSM8K_distilled_zh")
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dataset["train"].to_json("data/gsm8k_distilled_zh/GSM8K_distilled_zh-train.json", force_ascii=False)
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dataset["test"].to_json("data/gsm8k_distilled_zh/GSM8K_distilled_zh-test.json", force_ascii=False)
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# make data for sft
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def process_data_zh(example):
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src = example.get("question_zh", "")
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content = example.get("deepseek_r1_response_zh", "")
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reasoning_content = example.get("deepseek_r1_reasoning_zh", "")
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tgt = reasoning_content + content
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return {"src": src, "tgt": tgt}
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def process_data_en(example):
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src = example.get("question", "")
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content = example.get("deepseek_r1_response", "")
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reasoning_content = example.get("deepseek_r1_reasoning", "")
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tgt = reasoning_content + content
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return {"src": src, "tgt": tgt}
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# construct Chinese sft dataset
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paddlenlp_dataset = deepcopy(dataset)
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paddlenlp_dataset["train"] = paddlenlp_dataset["train"].map(
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process_data_zh, remove_columns=paddlenlp_dataset["train"].column_names
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)
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paddlenlp_dataset["test"] = paddlenlp_dataset["test"].map(
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process_data_zh, remove_columns=paddlenlp_dataset["test"].column_names
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)
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paddlenlp_dataset["train"].to_json("data/gsm8k_distilled_zh_sft/train.json", force_ascii=False)
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paddlenlp_dataset["test"].to_json("data/gsm8k_distilled_zh_sft/dev.json", force_ascii=False)
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# construct English sft dataset
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paddlenlp_dataset = deepcopy(dataset)
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paddlenlp_dataset["train"] = paddlenlp_dataset["train"].map(
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process_data_en, remove_columns=paddlenlp_dataset["train"].column_names
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)
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paddlenlp_dataset["test"] = paddlenlp_dataset["test"].map(
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process_data_en, remove_columns=paddlenlp_dataset["test"].column_names
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)
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paddlenlp_dataset["train"].to_json("data/gsm8k_distilled_en_sft/train.json", force_ascii=False)
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paddlenlp_dataset["test"].to_json("data/gsm8k_distilled_en_sft/dev.json", force_ascii=False)
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