simpletransformers
fastapi
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simpletransformers | fastapi | |
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6 | 466 | |
3,984 | 70,779 | |
- | - | |
7.3 | 9.8 | |
about 1 month ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simpletransformers
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Huggingface is a great idea poorly executed.
You might try this: https://github.com/ThilinaRajapakse/simpletransformers
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Gpt 2 124m using transformers
https://github.com/ThilinaRajapakse/simpletransformers/blob/master/simpletransformers/language_generation/language_generation_model.py#L146
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Neural Search Tutorial
Getting embeddings from BERT Encoder
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Neural Search Step-by-Step
Tutorial includes: - What is the Neural Search? - Getting embeddings from BERT Encoder - Using vector search engine Qdrant - Creating an API server with FastAPI.
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Document Classification
If you want to do text classification hugging face transformers is great. There's also a simple version for it: https://github.com/ThilinaRajapakse/simpletransformers
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A Shortly like user interface for GPT 2?
Here is an example script to finetune a GPT-2 model: https://github.com/ThilinaRajapakse/simpletransformers/blob/master/examples/language_generation/fine_tune.py
fastapi
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.
This ends up made worse if you’re using sync routes.
If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.
This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
- Ask HN: What is your go-to stack for the web?
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Fun with Avatars: Crafting the core engine | Part. 1
We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:
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Building Fast APIs with FastAPI: A Comprehensive Guide
FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.
What are some alternatives?
BERTweet - BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Flask - The Python micro framework for building web applications.
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.