In this guide, we explore what Transformers are, why Transformers are so important in computer vision, and how they work.
let’s assume the target output is the French translation of the English sentence “the quick brown fox jumped”, which translates to “le renard brun rapide a sauté” in French. In the decoder, separate embedding vectors are computed for each French word in the sentence, and the...
(called a completion). In effect, the "magic" of the model is that it has the ability to string a coherent sentence together. This ability doesn't imply any "knowledge" or "intelligence" on the part of the model; just a large vocabulary and the ability to generate meaningful sequences ...
A year later, another Google team tried processing text sequences both forward and backward with a transformer. That helped capture more relationships among words, improving the model’s ability to understand the meaning of a sentence. Their Bidirectional Encoder Representations from Transformers (BERT)...
Transformers have layers of attention blocks, feedforward neural networks (FNNs), and embeddings. The model takes in a text-based input and returns output text. To do this, it follows these steps: 1 Tokenization: Turns the text into tokens (similar to breaking down a sentence into individual...
There are two key innovations that make transformers particularly adept for large language models: positional encodings and self-attention. Positional encoding embeds the order of which the input occurs within a given sequence. Essentially, instead of feeding words within a sentence sequentially into the...
Paraphrasing toolsare often referred to as article paraphrasing tools, content transformation tools, sentence transformers, etc. Paraphrasing is a reformulation tool that does not necessarily reflect the unique ideas of an author. It can also create plagiarized text, which is a false representation of...
SBERT: Also known as sentence BERT and sentence transformers, SBERT is a variant of BERT with an adapted Siamese neural network structure, fine-tuned on pairs of sentences to improve its ability to encode sentence embeddings. DistilBERT: A lightweight BERT variant, created through knowledge distill...
Let’s delve into the compelling reasons behind the need for HuggingFace Transformers: Contextual Understanding: Conventional NLP systems are incapable of modeling complex contextual relationships that form between words in a sentence. The HuggingFace Transformers Pulse is quite the opposite; by virtue of...
which stands for Generative Pre-trained Transformer. Transformers are specialized algorithms for finding long-range patterns in sequences of data. A transformer learns to predict not just the next word in a sentence but also the next sentence in a paragraph and the next paragraph in an essay. ...