The complexity, cost, and skills required to produce LLMs is immense. Only larger companies and other international groups are able to train LLMs at the size of hundreds of billions of parameters. Given the benefit of LLMs to drive business and society, it is important to learn to use these monster AI models for multiple use in business and social problems. The need for specialized skills in prompt engineering will grow fast as more and and more companies start building their business around LLMs and similar products such as DALL-E 2, MidJourney, Bloom, etc.
Large Language Model(LLMS) can be used for multiple use in business and social problem. The objective of this challenge is to generate prompts for LLMs to extract relevant entities from job descriptions and also to classify news artifacts given only a few examples of human scores
Entity Extraction with Cohere API --notebooks/CohereEntityExtract.ipynb
Connect to Cohere API
import cohere
co = cohere.Client(api_key)
News Artifact scoring Cohere API -- notebooks/CohereNewsScoring.ipynb
co.classify(
model='medium',
inputs=[text], # the string to be classified
examples=examples # a couple of examples - training set
)
Text Analysis with DeepAI API -- notebooks/DeepAITextAnlaysis.ipynb
Connect to the DeepAI API and do sentimental analysis on the given text
requests.post(
"https://api.deepai.org/api/sentiment-analysis",
data={
'text': 'Africa is a land of origin for human kind',
},
headers={'api-key': api_key}
)
Connect to the DeepAI API and summarize the given text
# Example posting a local text file:
requests.post(
"https://api.deepai.org/api/summarization",
files={
'text': open('../data/experimental/interim.txt', 'rb'),
},
headers={'api-key': api_key}
)
streamlit dashboard streamlitapp/main.py
Fast API backend/backend_main.py
Watch the APP
@degagawolde