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https://github.com/nlpodyssey/cybertron
Cybertron: the home planet of the Transformers in Go
https://github.com/nlpodyssey/cybertron
bart bert bert-as-service deep-learning huggingface machine-learning machine-translation named-entity-recognition natural-language-processing nlp question-answering summarization text-categorization text-classification text-similarity transformers translation zero-shot-classification
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Cybertron: the home planet of the Transformers in Go
- Host: GitHub
- URL: https://github.com/nlpodyssey/cybertron
- Owner: nlpodyssey
- License: bsd-2-clause
- Created: 2022-06-21T13:45:54.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-08T19:22:45.000Z (5 months ago)
- Last Synced: 2024-06-18T17:01:51.344Z (5 months ago)
- Topics: bart, bert, bert-as-service, deep-learning, huggingface, machine-learning, machine-translation, named-entity-recognition, natural-language-processing, nlp, question-answering, summarization, text-categorization, text-classification, text-similarity, transformers, translation, zero-shot-classification
- Language: Go
- Homepage:
- Size: 1.17 MB
- Stars: 267
- Watchers: 12
- Forks: 25
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cybertron
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 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
- 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
Requirements:
* [Go 1.21](https://golang.org/dl/)
Clone this repo or get the library:
```console
go get -u github.com/nlpodyssey/cybertron
```
Cybertron supports two main use cases, which are explained more in detail in the following.
## Server mode
Settings are configured in a `.env` file, which is automatically loaded by Cybertron. Alternatively, it also accepts configurations via flags.
For a complete list run:
```console
GOARCH=amd64 go run ./cmd/server -h
```
Output:
```console
Usage of server:
-address value
server listening address
-allowed-origins value
allowed origins (comma separated)
-loglevel value
zerolog global level
-model value
model name (and sub-path of models-dir)
-model-conversion value
model conversion policy ("always"|"missing"|"never")
-model-conversion-precision value
floating-point bits of precision to use if the model is converted ("32"|"64")
-model-download value
model downloading policy ("always"|"missing"|"never")
-models-dir value
models's base directory
-network value
network type for server listening
-task value
type of inference/computation that the model can fulfill ("textgeneration"|"zero-shot-classification"|"question-answering"|"text-classification"|"token-classification"|"text-encoding")
-tls value
whether to enable TLS ("true"|"false")
-tls-cert value
TLS cert filename
-tls-key value
TLS key filename
```
For example, to run Cybertron in server mode for Machine Translation (e.g. `en` to `it`) with default settings, simply create a `.env` file in the current directory:
```console
echo "CYBERTRON_MODEL=Helsinki-NLP/opus-mt-en-it" > .env
echo "CYBERTRON_MODELS_DIR=models" >> .env
echo "CYBERTRON_MODEL_TASK=text-generation" >> .env
```
and execute the following command:
```console
GOARCH=amd64 go run ./cmd/server -address 0.0.0.0:8080
```
To test the server, run:
```console
curl -X 'POST' \
'0.0.0.0:8080/v1/generate' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"input": "You must be the change you wish to see in the world.",
"parameters": {}
}'
```
## Library mode
Several examples can be leveraged to tour the current NLP capabilities in Cybertron. A list of the demos now follows.
### Machine Translation (En -> It)
```
GOARCH=amd64 go run ./examples/textgeneration
```
### Zero-Shot Text Classification
⚠️ If the model specified in `.env` file is not compatible, an error will be returned. In this case, remove the specified model from the configuration file, so the default one will be used.
```
GOARCH=amd64 go run ./examples/zeroshotclassification politics,business,science,technology,health,culture,sports
```
# Dependencies
Cybertron's pricipal dependencies are:
- [Spago](https://github.com/nlpodyssey/spago) - a lightweight self-contained machine learning framework in pure Go
- [GoPickle](https://github.com/nlpodyssey/gopickle) - a Go module for loading Python's data serialized with pickle and PyTorch module files
- [GoTokenizers](https://github.com/nlpodyssey/gotokenizers) - Go implementation of today's most used tokenizers
The rest are mainly for gRPC and HTTP API developments.
# Dev Tools
> This section is intended for developers who want to change or enrich the Cybertron gRPC and HTTP APIs.
To get started, you need [buf](https://github.com/bufbuild/buf) installed in your machine.
Then install the following tools:
```
go install github.com/grpc-ecosystem/grpc-gateway/v2/protoc-gen-grpc-gateway \
github.com/grpc-ecosystem/grpc-gateway/v2/protoc-gen-openapiv2 \
google.golang.org/protobuf/cmd/protoc-gen-go \
google.golang.org/grpc/cmd/protoc-gen-go-grpc
```
Then run the following command to generate the gRPC and HTTP APIs:
```
go generate ./...
```