Here at Unite.AI, we have already covered the release of Quantum Stat’s “Big Bad NLP Database,” as well as its NLP Colab Repository. The tech company’s newest creation is its NLP Model Forge, which is a database and code template generator for 1,400 NLP models.
According to the company, “It’s the most diverse line-up around right now for developers!”
What is the NLP Model Forge
Quantum Stat has set out to achieve fast prototyping by “streamlining an inference pipeline on the latest fine-tuned NLP model.”
One of the issues surrounding prototyping is that it can be time-consuming. This is due to the high amount of different model architectures and NLP libraries available on the market. In order to address this, the NLP Model Forge was developed.
The NLP Model Forge’s 1,400 fine-tuned models were curated from some of the top NLP research companies like Hugging Face, Facebook (ParlAI), DeepPavlov and AI2. It consists of finely-tuned code for pre-trained models, which spans across several tasks such as classic text classification, text-to-speech and commonsense reasoning.
The developer is able to select several models at a time, and the process is simple and clear. By clicking a button on the Forge, the developer will be met with generated code templates that are ready to run and be pasted in a Colab notebook.
A developer can easily create inference API since the code blocks are formatted in batch and python programming scripts.
The current tasks that are available in the Forge include: Sequence Classification, Text Generation, Question Answering, Token Classification, Summarization, Natural Language Inference, Conversational AI, Machine Translation, Text-to-Speech and Commonsense Reasoning.
According to Quantum Stat, the best features of the Forge are its diversity of architectures, languages and libraries, as well as the meta descriptions of each model.
The metadata descriptions help guide a developer through their chosen model and the different tasks.
Quantum Stat’s post about the release of the Forge details how to generate code blocks to run inference on the models, which is a simple and straightforward process. The generated code blocks are programmatically labeled with relevant metadata, and this improves interpretation in the functionality of each model.
After this, the developer has the choice to edit the code right on the webpage, email the code or copy each code block to the clipboard so that it can be pasted in a local machine.
The other choice is to click on the “Colab” button, which allows a developer to copy all code blocks and the page and open a new Colab page.
Quantum Stat’s NLP Model Forge is just one of the company’s newest impressive releases. The database and code template generator is an important tool for developers, and it’s format makes it easy to access. The database will play a big role in reducing the time-consuming task of prototyping.