本文目录一览：
- 1、openai能当爬虫使吗
- 2、如何下载gpt
- 3、怎么在国内使用gpt？
- 4、openai哪里下载
- 5、怎么用openai写论文
openai能当爬虫使吗
你好，可以的，Spinning Up是OpenAI开源的面向初学者的深度强化学习资料，其中列出了105篇深度强化学习领域非常经典的文章， 见 Spinning Up：
博主使用Python爬虫自动爬取了所有文章，而且爬下来的文章也按照网页的分类自动分类好。
见下载资源：Spinning Up Key Papers
源码如下：
import os
import time
import urllib.request as url_re
import requests as rq
from bs4 import BeautifulSoup as bf
'''Automatically download all the key papers recommended by OpenAI Spinning Up.
See more info on:
Dependency:
bs4, lxml
'''
headers = {
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36'
}
spinningup_url = ''
paper_id = 1
def download_pdf(pdf_url, pdf_path):
"""Automatically download PDF file from Internet
Args:
pdf_url (str): url of the PDF file to be downloaded
pdf_path (str): save routine of the downloaded PDF file
"""
if os.path.exists(pdf_path): return
try:
with url_re.urlopen(pdf_url) as url:
pdf_data = url.read()
with open(pdf_path, "wb") as f:
f.write(pdf_data)
except: # fix link at [102]
pdf_url = r""
with url_re.urlopen(pdf_url) as url:
pdf_data = url.read()
with open(pdf_path, "wb") as f:
f.write(pdf_data)
time.sleep(10) # sleep 10 seconds to download next
def download_from_bs4(papers, category_path):
"""Download papers from Spinning Up
Args:
papers (bs4.element.ResultSet): 'a' tags with paper link
category_path (str): root dir of the paper to be downloaded
"""
global paper_id
print("Start to ownload papers from catagory {}...".format(category_path))
for paper in papers:
paper_link = paper['href']
if not paper_link.endswith('.pdf'):
if paper_link[8:13] == 'arxiv':
# paper_link = ""
paper_link = paper_link[:18] + 'pdf' + paper_link[21:] + '.pdf' # arxiv link
elif paper_link[8:18] == 'openreview': # openreview link
# paper_link = ""
paper_link = paper_link[:23] + 'pdf' + paper_link[28:]
elif paper_link[14:18] == 'nips': # neurips link
paper_link = ""
else: continue
paper_name = '[{}] '.format(paper_id) + paper.string + '.pdf'
if ':' in paper_name:
paper_name = paper_name.replace(':', '_')
if '?' in paper_name:
paper_name = paper_name.replace('?', '')
paper_path = os.path.join(category_path, paper_name)
download_pdf(paper_link, paper_path)
print("Successfully downloaded {}!".format(paper_name))
paper_id += 1
print("Successfully downloaded all the papers from catagory {}!".format(category_path))
def _save_html(html_url, html_path):
"""Save requested HTML files
Args:
html_url (str): url of the HTML page to be saved
html_path (str): save path of HTML file
"""
html_file = rq.get(html_url, headers=headers)
with open(html_path, "w", encoding='utf-8') as h:
h.write(html_file.text)
def download_key_papers(root_dir):
"""Download all the key papers, consistent with the categories listed on the website
Args:
root_dir (str): save path of all the downloaded papers
"""
# 1. Get the html of Spinning Up
spinningup_html = rq.get(spinningup_url, headers=headers)
# 2. Parse the html and get the main category ids
soup = bf(spinningup_html.content, 'lxml')
# _save_html(spinningup_url, 'spinningup.html')
# spinningup_file = open('spinningup.html', 'r', encoding="UTF-8")
# spinningup_handle = spinningup_file.read()
# soup = bf(spinningup_handle, features='lxml')
category_ids = []
categories = soup.find(name='div', attrs={'class': 'section', 'id': 'key-papers-in-deep-rl'}).\
find_all(name='div', attrs={'class': 'section'}, recursive=False)
for category in categories:
category_ids.append(category['id'])
# 3. Get all the categories and make corresponding dirs
category_dirs = []
if not os.path.exitis(root_dir):
os.makedirs(root_dir)
for category in soup.find_all(name='h2'):
category_name = list(category.children)[0].string
if ':' in category_name: # replace ':' with '_' to get valid dir name
category_name = category_name.replace(':', '_')
category_path = os.path.join(root_dir, category_name)
category_dirs.append(category_path)
if not os.path.exists(category_path):
os.makedirs(category_path)
# 4. Start to download all the papers
print("Start to download key papers...")
for i in range(len(category_ids)):
category_path = category_dirs[i]
category_id = category_ids[i]
content = soup.find(name='div', attrs={'class': 'section', 'id': category_id})
inner_categories = content.find_all('div')
if inner_categories != []:
for category in inner_categories:
category_id = category['id']
inner_category = category.h3.text[:-1]
inner_category_path = os.path.join(category_path, inner_category)
if not os.path.exists(inner_category_path):
os.makedirs(inner_category_path)
content = soup.find(name='div', attrs={'class': 'section', 'id': category_id})
papers = content.find_all(name='a',attrs={'class': 'reference external'})
download_from_bs4(papers, inner_category_path)
else:
papers = content.find_all(name='a',attrs={'class': 'reference external'})
download_from_bs4(papers, category_path)
print("Download Complete!")
if __name__ == "__main__":
root_dir = "key-papers"
download_key_papers(root_dir)
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如何下载gpt
在官网即可下载。
您可以到OpenAI官网上去下载GPT-3.5。操作步骤如下：1.访问OpenAI官网；2.在页面中找到“Download GPT-3.5”的按钮并点击；3. 根据提示进行下载即可。
全局唯一标识分区表（GUID Partition Table，缩写：GPT）是指全局唯一标示磁盘分区表格式。
怎么在国内使用gpt？
您可以通过访问OpenAI的官方网站，或者通过一些基于ChatGPT开发的应用程序来使用ChatGPT。但需要注意的是，在中国网络是不允许访问OpenAI官方网站的。
推荐使用“小Ai助理”微信公众号，该公众号可以提供类似ChatGPT的对话生成和自然语言处理等功能，并且可以在中国网络环境下正常使用。
openai哪里下载
openai百度文库下载。
先把你下载的openal32.dll删掉，也就是c:\wiindows\system32 文件夹中的openai32.dll和游戏文件夹中的openai32.dll 。然后下载OpenAL 最后再装上OpenAL 这就行了。
入口点函数只应执行简单的初始化任务，不应调用任何其他 DLL 加载函数或终止函数。例如，在入口点函数中，不应直接或间接调用 LoadLibrary 函数或 LoadLibraryEx 函数。此外，不应在进程终止时调用 FreeLibrary 函数。
DLL 故障排除工具：
可以使用多个工具来帮助您解决 DLL 问题。以下是其中的部分工具。 Dependency WalkerDependency Walker 工具可以递归扫描以寻找程序所使用的所有依赖 DLL。
当您在 Dependency Walker 中打开程序时，Dependency Walker 会执行下列检查： Dependency Walker 检查是否丢失 DLL。 Dependency Walker 检查是否存在无效的程序文件或 DLL。
怎么用openai写论文
要使用openai写论文首先是要安装好al小助手，要下载al text generator 的插件，然后安装并且配置好ai小助手，接着是要生成和管理apl的密钥了，也就是登录的密码，然后在使用ai编辑器编辑文件文本，最后通过数据元方式输出就可以了。
