kiri 0.5.0

Kiri Natural Language Engine

Kiri

Kiri is a Python library that makes it simple to solve AI tasks without requiring any data.

Kiri is built around solving tasks with transfer learning. It implements state-of-the-art AI models that are general enough to solve real world tasks with no data required from the user.

Out of the box tasks you can solve with Kiri:

• Conversational question answering in English (for FAQ chatbots, text analysis, etc.)
• Text Classification in 100+ languages (for email sorting, intent detection, etc.)
• Image Classification (for object recognition, OCR, etc.)
• Text Vectorisation in 50+ languages (semantic search for ecommerce, documentation, etc.)
• Summarisation in English (TLDRs for long documents)
• Emotion detection in English (for customer satisfaction, text analysis, etc.)
• Text Generation (for idea, story generation and broad task solving)

For more specific use cases, you can adapt a task with little data and a couple of lines of code using finetuning. We are adding finetuning support for all tasks soon.

You can run all tasks locally or in production with our optimised inference API, where you only pay for usage. It includes all the tasks, models in our library and lets you upload your own finetuned models.

⚡ Getting started	Installation, few minute introduction
💡 Examples	Sample problems solved using Kiri
📙 Docs	In-depth documentation for advanced usage

Getting started

Installation

Install Kiri via PyPi:

pip install kiri

Basic task solving

from kiri import Kiri

context = "Take a look at the examples folder to see use cases!"

# Use our inference API
k = Kiri(api_key="abc")
# Or run locally
k = Kiri(local=True)

# Start building!
answer = k.qa("Where can I see what to build?", context)

print(answer)
# Prints
"the examples folder"

Basic finetuning and uploading

from kiri.models import T5
from kiri.tasks import TextGeneration

tg = TextGeneration(T5, local=True)

# Any text works as training data
inp = ["I really liked the service I received!", "Meh, it was not impressive."]
out = ["positive", "negative"]

# Finetune with a single line of code
tg.finetune(inp, out)

# Use your trained model
prediction = tg("I enjoyed it!")

print(prediction)
# Prints
"positive"

# Upload to Kiri for production ready inference
import kiri

model = tg.model
# Describe your model
model.name = "t5-sentiment"
model.description = "Predicts positive and negative sentiment"

kiri.upload(model, api_key="abc")

Why Kiri?

1. No experience needed

   • Entrance to practical AI should be simple
   • Get state-of-the-art performance in your task without being an expert

2. Data is a bottleneck

   • Use AI without needing access to "big data"
   • With transfer learning, no data is required, but even a small amount can adapt a task to your niche.

3. There is an overwhelming amount of models

   • We implement the best ones for various tasks
   • A few general models can accomplish more with less optimisation

4. Deploying models cost effectively is hard work

   • If our models suit your use case, no deployment is needed
   • Adapt and deploy your own model with a couple of lines of code
   • Our API scales, is always available, and you only pay for usage

Examples

Take a look at the examples folder.

Documentation

Check out our docs.