Package: backprop
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.Solve a variety of tasks with pre-trained models or finetune them in one line for your own tasks.Out of the box tasks you can solve with Backprop:Conversational question answering in EnglishText Classification in 100+ languagesImage ClassificationText Vectorisation in 50+ languagesImage VectorisationSummarisation in EnglishEmotion detection in EnglishText GenerationFor more specific use cases, you can adapt a task with little data and a single line of code via finetuning.⚡Getting startedInstallation, few minute introduction💡ExamplesFinetuning and usage examples📙DocsIn-depth documentation about task inference and finetuning⚙️ModelsOverview of available modelsGetting startedInstallationInstall Backprop via PyPi:pipinstallbackpropBasic task inferenceTasks act as interfaces that let you easily use a variety of supported models.importbackpropcontext="Take a look at the examples folder to see use cases!"qa=backprop.QA()# Start building!answer=qa("Where can I see what to build?",context)print(answer)# Prints"the examples folder"You can run all tasks and models on your own machine, or in production with our inferenceAPI, simply by specifying yourapi_key.See how to useall available tasks.Basic finetuning and uploadingEach task implements finetuning that lets you adapt a model for your specific use case in a single line of code.A finetuned model is easy to upload to production, letting you focus on building great applications.frombackprop.modelsimportT5frombackpropimportTextGenerationtg=TextGeneration(T5)# Any text works as training datainp=["I really liked the service I received!","Meh, it was not impressive."]out=["positive","negative"]# Finetune with a single line of codetg.finetune({"input_text":inp,"output_text":out})# Use your trained modelprediction=tg("I enjoyed it!")print(prediction)# Prints"positive"# Upload to Backprop for production ready inference# Describe your modelname="t5-sentiment"description="Predicts positive and negative sentiment"tg.upload(name=name,description=description,api_key="abc")Seefinetuning for other tasks.Why Backprop?No experience neededEntrance to practical AI should be simpleGet state-of-the-art performance in your task without being an expertData is a bottleneckSolve real world tasks without any dataWith transfer learning, even a small amount of data can adapt a task to your niche requirementsThere are an overwhelming amount of modelsWe offer a curated selection of the best open-source models and make them simple to useA few general models can accomplish more with less optimisationDeploying models cost effectively is hard workIf our models suit your use case, no deployment is needed: just call our APIAdapt and deploy your own model with just a few lines of codeOur API scales, is always available, and you only pay for usageExamplesSolve any text based task with Finetuning (Github,Colab)Search for images using text (Github)Finding answers from text (Github)More finetuning and task examplesDocumentationCheck out ourdocsfor in-depth task inference and finetuning.Model HubCurated list ofstate-of-the-art models.DemosZero-shot image classification withCLIP.CreditsBackprop relies on many great libraries to work, most notably:PyTorchPyTorch LightningTransformersSentence TransformersEfficientNet PyTorchCLIPFeedbackFound a bug or have ideas for new tasks and models? Open anissue.