According to the "Artificial Intelligence Large Model Experience Report 2.0" released by the China Enterprise Development Research Center of the Xinhua News Agency Research Institute, the eight large models tested were all from technology giants or formal teams developed in cooperation with authoritative institutes. For example, iFlytek Spark was developed by iFlytek, which enjoys the reputation of "AI National Team", while Zhipu AI-ChatGLM was built by a company that transformed technological achievements from the Department of Computer Science of Tsinghua University. In the evaluation results, iFlytek Spark won first place with a total score of 1013 points, only one point behind Benchmark (human). This shows that iFlytek Spark performed well in the evaluation and achieved remarkable results.
The main reason is that the review is really fast. You can successfully make an appointment in five or ten minutes. iFlytek Spark Cognitive Model - AI Large Language Model - Spark Model - iFlytek Spark Cognitive Model, launched by iFlytek A new generation of cognitive intelligence large model, with cross-domain knowledge and language understanding capabilities, able to understand and perform tasks based on natural dialogue, providing language understanding, knowledge Q&A, logical reasoning, mathematical problem solving, code understanding and writing, etc. ability. https://xinghuo.xfyun.cn/?ch=bl_Y95KVn
iFlytek Spark has achieved a higher level of upgrades in multiple rounds of dialogue, logic and mathematical abilities. It not only performs well in terms of IQ, but is also excellent in emotional intelligence. Through extensive learning of human common sense and knowledge, iFlytek Spark better understands human language and behavior, demonstrating rigorous thinking reasoning and powerful analytical decision-making capabilities. In addition, iFlytek Spark has accumulated deep experience and knowledge in professional fields such as medical care, which is also one of its advantages compared with other large models.
iFlytek Spark model version 2.0
Recently , the iFlytek Spark cognitive model has been upgraded to version 2.0 . The biggest upgrade is the substantial improvement in coding capabilities and multi-modal capabilities . Let the blogger take you to experience it.
Multimodal functionality
2.1 What is multimodality?
Multimodal refers to the simultaneous involvement of multiple different perception modalities or information expression methods in a system or a task. Common perception modalities include vision (images, videos), hearing (audio), language (text), touch, etc. Through multi-modal technology, data from different modalities can be fused and interacted to obtain more comprehensive and rich information.
The iFlytek Spark large model can handle multiple different perception modalities at the same time, such as images, speech, and text. This enables it to comprehensively understand and analyze multiple input data, resulting in more comprehensive and accurate information.
I have operated it twice and it feels pretty good, fully in line with expectations and without any flaws.
2.2 Assistant Center
The Assistant Center may be a platform integrating a variety of practical tools and functions designed to help users better use the Spark model. It can provide expansion assistant, copywriting master, and python editor login functions to support users to better understand and apply models. At the same time, the Assistant Center may also provide channels for user feedback and suggestions to improve and optimize model performance.
Copywriting master actual test:
This is simply a benefit for media people. You no longer have to worry about copywriting issues.
Through role setting, you can quickly customize an exclusive assistant. You can also create your own dataset and associate it with your assistant to use it to ask questions. This means you can train an assistant that is entirely your own, customized to your background, business and needs. Ultimately, you will have an assistant that is unique to you.
2.3. Code ability
Code generation: Spark Big Model is able to generate reasonable code snippets based on given requirements and conditions
import requests
from bs4 import BeautifulSoup
import xlwt
import re
def main():
baseurl = "https://movie.douban.com/top250?start="
datalist = getData(baseurl) # 获取数据
savepath = "豆瓣电影Top250.xls"
saveData(datalist, savepath) # 保存数据到Excel文件
def getData(baseurl):
datalist = []
for i in range(0, 10): # 遍历每页
url = baseurl + str(i*25)
html = askURL(url) # 发送请求获取网页内容
soup = BeautifulSoup(html, "html.parser") # 使用BeautifulSoup解析网页
items = soup.find_all('div', class_="item") # 找到电影条目的div标签
for item in items:
data = []
item_link = item.find('a')['href'] # 获取电影详情链接
item_img = item.find('img')['src'] # 获取图片链接
item_title = item.find('span', class_="title").get_text() # 获取影片中文名
item_rating = item.find('span', class_="rating_num").get_text() # 获取评分
item_judge = item.find('span', string=re.compile("人评价")).get_text() # 获取评价数
item_inq = item.find('span', class_="inq") # 获取概况
item_bd = item.find('p', class_="") # 获取相关信息
if item_inq:
item_inq = item_inq.get_text().replace("。", "")
else:
item_inq = ""
if item_bd:
item_bd = item_bd.get_text().strip()
else:
item_bd = ""
data.append(item_link)
data.append(item_img)
data.append(item_title)
data.append(item_rating)
data.append(item_judge)
data.append(item_inq)
data.append(item_bd)
datalist.append(data) # 将电影数据添加到列表中
return datalist
def askURL(url):
try:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36'
}
response = requests.get(url, headers=headers) # 发送请求
response.raise_for_status()
response.encoding = response.apparent_encoding
return response.text # 返回网页内容
except requests.RequestException as e:
print(e)
return ""
def saveData(datalist, savepath):
workbook = xlwt.Workbook(encoding="utf-8") # 创建一个新的Excel文件
worksheet = workbook.add_sheet('豆瓣电影Top250', cell_overwrite_ok=True) # 添加工作表
col = ('电影详情链接', '图片链接', '影片中文名', '评分', '评价数', '概况', '相关信息') # 列名
for i in range(len(col)):
worksheet.write(0, i, col[i]) # 写入列名
for i in range(len(datalist)):
print("第%d条：" %(i+1))
data = datalist[i]
for j in range(len(data)):
worksheet.write(i+1, j, data[j]) # 写入数据
workbook.save(savepath) # 保存Excel文件
if __name__ == "__main__":
main()We found that the code is indeed usable, the crawled content meets the needs, and it is not difficult to see other excellent coding capabilities.
Free trial
The powerful coding capabilities and multi-modal functions of the Spark model have attracted a large number of programmers to use it. How can we experience it for free?
Go to the Spark Model, click the link below, iFlytek Spark Cognitive Model , and register. You can register successfully within a few minutes. If you
If you are a developer, you can also have a higher Spark large model API testing quota, which is 30% more than applying through ordinary channels. You can apply for up to
500w Tokens。
