From ef4d2d0e6f57c9b2435a22a434f6d023c6f9371c Mon Sep 17 00:00:00 2001 From: matteo-grella Date: Mon, 30 Oct 2023 23:28:27 +0100 Subject: [PATCH] Update README.md --- README.md | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 5a7c759..33b27f0 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,27 @@ # Cybertron -Cybertron is a Go package designed for developers to easily implement NLP using Transformer models, such as BERT and BART. -It's tailored for inference tasks and allows for simple server deployment with a single executable. -This tool leverages pre-trained models from the [HuggingFace models repository](https://huggingface.co/models), converting models for use with the Go-based [Spago](https://github.com/nlpodyssey/spago) framework. While spaGO is a complete ML framework that may eventually allow for model fine-tuning, Cybertron's current focus is on **inference**. +Cybertron is a package in pure Go built upon [spaGO](https://github.com/nlpodyssey/spago) that provides Go developers with an easy interface to use NLP technologies, without needing other programming languages or complex frameworks. It's designed for using pre-trained Transformer models available on the [HuggingFace models repository](https://huggingface.co/models). -The project is open for collaboration to expand its development further. +The package is primarily aimed at running **inference** with the possibility of adding fine-tuning in the future. + +The team is open to contributors to expedite its growth. + +## Supported models + +- BERT +- ELECTRA +- BART +- PEGASUS +- MarianMT ## Supported tasks -- Language Modeling (Masked) -- Text Classification (Supervised, Zero-Shot) -- Token Classification (NER, POS-Tagging) -- Question-Answering (Extractive, Abstractive) -- Text Encoding (Text Similarity) -- Text Generation (Translation, Paraphrasing) +- Masked Language Modeling +- Supervised and Zero-Shot Text Classification (Sentiment Analysis, Topic Classification, Intent Detection, ...) +- Token Classification (Named Entity Recognition, Part-of-Speech Tagging, ...) +- Extractive and Abstractive Question-Answering +- Text Encoding (Text Embedding, Semantic Search, ...) +- Text Generation (Translation, Paraphrasing, Summarization, ...) - Relation Extraction # Usage