In this tutorial, we understand Data2Vec model from Meta AI and show how to train your own model with a ready-to-use codebase on the Gradient Notebook.
In this tutorial, we look at the LLaMA model from Meta AI, and show how to implement it in a Gradient Notebook with lightning fast access to the models using the Public Dataset.
In this tutorial, we cover using sentence embeddings for semantic search using Cohere in a Gradient Notebook
In this article, we talk about what Dense Passage Retrieval is, how it works, and its uses. We also show how to implement it using the Simple Transformers python library in a Gradient Notebook.
In this article, we'll go over how to set up NLTK in a paperspace gradient and utilize it to carry out a variety of NLP operations during the text processing stage. Then, we will create a Keras model with the help of some NLTK tools for sentiment analysis text classification.
In this tutorial, we show how Whisper can be used with MoviePy to automatically generate and overlay translated subtitles from any video sample. We then walked through setting up this process to run both within a Notebook context and from an application served with Gradient Deployments.
In this blog, we show how to build an emoji suggestion system for short sentences rather than just a single word, and integrate it with a Flask interface.
In this deep dive of BERT, we explore the powerful NLP model's history, break down the approach and architecture behind the model, and take a look at some relevant experiments. We then close with a code demo showing how to use BERT, DistilBERT, RoBERTa, and ALBERT in a Gradient Notebook.
In this blog, we discuss various types of learning paradigms present in NLP, notations often used in the prompt-based learning paradigm, demo applications of prompt-based learning, and discuss some design considerations to make while designing a prompting environment.
