diff --git a/.gitattributes b/.gitattributes index 55cab133643a2a73e083373d2106533678d0edd5..181440f8bc34a181a26713a21039a06b188cc202 100644 --- a/.gitattributes +++ b/.gitattributes @@ -53,6 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.jpg filter=lfs diff=lfs merge=lfs -text *.jpeg filter=lfs diff=lfs merge=lfs -text *.webp filter=lfs diff=lfs merge=lfs -text -# Video files - compressed -*.mp4 filter=lfs diff=lfs merge=lfs -text -*.webm filter=lfs diff=lfs merge=lfs -text +tasks.csv filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..65ff92e871942803a199f275a5404be238499ae2 --- /dev/null +++ b/README.md @@ -0,0 +1,67 @@ +--- +license: apache-2.0 +language: +- ru +dataset_info: + features: + - name: images + sequence: image + - name: text + dtype: string + - name: answer + dtype: string + - name: tags + dtype: string +--- +# The First Multimodal Chain of Thoughts Dataset for Physics Problems + +### Dataset Overview + +This dataset consists of a diverse collection of physics problems, categorized into different domains: + +- **1000 problems on Kinematics** +- **600 problems on Electricity and Circuits** +- **500 problems on Thermodynamics** + +### Data Sources + +All data has been extracted from open sources, ensuring a wide variety of problem types and difficulty levels. + +### Structure + +Each problem is designed to test and enhance problem-solving skills in physics, making use of multimodal inputs for a comprehensive understanding. + +--- + +This dataset is suitable for educational purposes, research in multimodal AI, and the development of advanced problem-solving models. + +# Первый Мультимодальный Chain of Thoughts Датасет для Задач по Физике + +### Обзор Датасета + +Этот датасет содержит разнообразную коллекцию задач по физике, распределённых по различным областям: + +- **1000 задач по кинематике** +- **600 задач на электричество и цепи** +- **500 задач по термодинамике** + +### Источники Данных + +Все данные были извлечены из открытых источников, что обеспечивает широкий спектр типов задач и уровней сложности. + +### Структура + +Каждая задача предназначена для проверки и улучшения навыков решения задач по физике, используя мультимодальные входные данные для всестороннего понимания. + +--- + +Этот датасет подходит для образовательных целей, исследований в области мультимодального ИИ, а также для разработки продвинутых моделей решения задач. + + +Cite: +@dataset{, + title={Physics Big}, + author={Zaharov Timur and Konstantin Korolev and Aleksandr Nikolich}, + year={2024}, + url={https://huggingface.co/datasets/Vikhrmodels/physics_big} +} \ No newline at end of file diff --git a/data/00000.parquet b/data/00000.parquet new file mode 100644 index 0000000000000000000000000000000000000000..98c6970b878dfddd99819de8659d4c1e36da9df0 --- /dev/null +++ b/data/00000.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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初始化一个空的DataFrame，用来存储筛选出的样本 +combined_df = pd.DataFrame() + +# 遍历指定目录中的所有文件 +for i in range(1, 128): # 文件名为00001--00127.parquet + file_name = f'{i:05d}.parquet' + file_path = os.path.join(data_dir, file_name) + + # 读取当前的parquet文件 + df = pd.read_parquet(file_path) + + # 筛选出images字段不为空的样本 + non_empty_images_df = df[df['images'].apply(lambda x: len(x) > 0)] + + # 只取前3个样本 + selected_samples = non_empty_images_df.head(3) + + # 将筛选出的样本添加到合并的DataFrame中 + combined_df = pd.concat([combined_df, selected_samples], ignore_index=True) + +# 保存合并后的结果到一个新的Parquet文件 +output_file_path = os.path.join(data_dir, 'combined_images_non_empty.parquet') +combined_df.to_parquet(output_file_path) + +print(f"已将筛选出的样本保存到 {output_file_path}") diff --git a/read.py b/read.py new file mode 100644 index 0000000000000000000000000000000000000000..b6bac4316b1dc7e8a4b874e6ff4d9594629f4d07 --- /dev/null +++ b/read.py @@ -0,0 +1,40 @@ +# -*- encoding: UTF-8 -*- +import pandas as pd + +# 指定parquet文件的路径 +file_path = '/home/yiyangai/stephenqs/datasets/physics_big/data/combined_images_non_empty.parquet' + +# 读取parquet文件 +df = pd.read_parquet(file_path) + +# 获取所有的列名（假设这些列名代表子类） +columns = df.columns + +# 遍历所有子类并输出前五条消息 +for column in columns: + print(f"--- {column} (前五条消息) ---") + print(df[column].head()) + print("\n") + +# +## Replace 'file_path' with the path to your Parquet file +#file_path = '/home/yiyangai/stephenqs/datasets/physics_big/data/00001.parquet' +# +## Read the Parquet file +#df = pd.read_parquet(file_path, engine='pyarrow') # or use 'fastparquet' as engine +# +## Display the entire DataFrame +#print("Full DataFrame:") +#print(df) +# +## Display the first 5 rows of the DataFrame +#print("\nFirst 5 rows:") +#print(df.head()) +# +## Display basic information about the DataFrame +#print("\nDataFrame Info:") +#print(df.info()) +# +## Display summary statistics of the DataFrame +#print("\nSummary Statistics:") +#print(df.describe()) diff --git a/submit.py b/submit.py new file mode 100644 index 0000000000000000000000000000000000000000..86e00bc68e3beabc2926c38744ee8687310371a1 --- /dev/null +++ b/submit.py @@ -0,0 +1,16 @@ +from huggingface_hub import HfApi + +# 定义数据集路径和目标URL +dataset_dir = "/home/yiyangai/stephenqs/datasets/physics_big" +repo_url = "StarThomas1002/my-phy-dataset" + +# 创建HfApi实例 +api = HfApi() + +# 上传临时目录中的所有文件到 Hugging Face 数据集库 +api.upload_folder( + folder_path=dataset_dir, + repo_id=repo_url, + repo_type="dataset" +) + diff --git a/translate.py b/translate.py new file mode 100644 index 0000000000000000000000000000000000000000..c65e324951fde6bb37204c87d19cc88b0df0edda --- /dev/null +++ b/translate.py @@ -0,0 +1,60 @@ +import pandas as pd +import openai +from utils.openai import load_client + +client = load_client('') + +# 初始化OpenAI API密钥 +openai.api_key = 'dE6qTITLy3WCoMPMCPr8tUFBHBaec5wN' + +# 指定parquet文件的路径 +file_path = '/home/yiyangai/stephenqs/datasets/physics_big/data/combined_images_non_empty.parquet' + +# 读取parquet文件 +df = pd.read_parquet(file_path) + +# 定义翻译函数，调用GPT-4 API +def translate_text(text): + if isinstance(text, list): # 如果是列表，递归处理每个元素 + return [translate_text(item) for item in text] + elif isinstance(text, dict): # 如果是字典，递归处理字典中的值 + return {key: translate_text(value) for key, value in text.items()} + elif isinstance(text, str): # 如果是字符串，翻译 + response = openai.ChatCompletion.create( + model="gpt-4o-mini", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": f"Translate this Russian text to English: {text}"} + ] + ) + return response.choices[0].message['content'].strip() + else: + return text + +# 初始化一个空的DataFrame来存储翻译后的结果 +df_translated = pd.DataFrame(columns=df.columns) + +# 每次翻译20条记录 +batch_size = 20 + +# 逐批次翻译数据 +for i in range(0, len(df), batch_size): + print(f"正在翻译样本 {i+1} 到 {min(i+batch_size, len(df))}...") + batch = df.iloc[i:i+batch_size].copy() + + # 对批次内的每一列进行翻译 + for column in batch.columns: + batch[column] = batch[column].apply(translate_text) + + # 将翻译后的批次数据添加到翻译结果的DataFrame中 + df_translated = pd.concat([df_translated, batch], ignore_index=True) + + # 保存中间结果（可选，以防脚本中途失败） + df_translated.to_parquet('/home/yiyangai/stephenqs/datasets/physics_big/data/partial_translation.parquet') + +# 最终保存完整的翻译结果到新的Parquet文件 +output_file_path = '/home/yiyangai/stephenqs/datasets/physics_big/data/translated_combined_images_non_empty.parquet' +df_translated.to_parquet(output_file_path) + +print(f"翻译完成，已将结果保存到 {output_file_path}") +