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alt=\"Backprop\"/\u003e\n   \u003c/a\u003e\n\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n   \u003ca href=\"https://pypi.org/project/backprop/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/backprop\"/\u003e\u003c/a\u003e \u003cimg src=\"https://img.shields.io/pypi/pyversions/backprop\"/\u003e \u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\nBackprop makes it simple to use, finetune, and deploy state-of-the-art ML models.\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n   \u003cimg src=\".github/example.png\" width=\"600\"/\u003e\n\u003c/p\u003e\n\nSolve a variety of tasks with pre-trained models or finetune them in one line for your own tasks.\n\nOut of the box tasks you can solve with Backprop:\n\n- Conversational question answering in English\n- Text Classification in 100+ languages\n- Image Classification\n- Text Vectorisation in 50+ languages\n- Image Vectorisation\n- Summarisation in English\n- Emotion detection in English\n- Text Generation\n\nFor more specific use cases, you can adapt a task with little data and a single line of code via finetuning.\n\n| ⚡ [Getting started](#getting-started)                | Installation, few minute introduction                      |\n| :---------------------------------------------------- | :-------------------------------------------------------- |\n| 💡 [Examples](#examples)                              | Finetuning and usage examples                              |\n| 📙 [Docs](https://backprop.readthedocs.io/en/latest/) | In-depth documentation about task inference and finetuning |\n| ⚙️ [Models](https://backprop.co/hub)                   | Overview of available models                              |\n\n## Getting started\n\n### Installation\n\nInstall Backprop via PyPi:\n\n```bash\npip install backprop\n```\n\n### Basic task inference\n\nTasks act as interfaces that let you easily use a variety of supported models.\n\n```python\nimport backprop\n\ncontext = \"Take a look at the examples folder to see use cases!\"\n\nqa = backprop.QA()\n\n# Start building!\nanswer = qa(\"Where can I see what to build?\", context)\n\nprint(answer)\n# Prints\n\"the examples folder\"\n```\n\nYou can run all tasks and models on your own machine, or in production with our inference [API](https://backprop.co), simply by specifying your `api_key`.\n\nSee how to use [all available tasks](https://backprop.readthedocs.io/en/latest/Tasks.html).\n\n### Basic finetuning and uploading\n\nEach task implements finetuning that lets you adapt a model for your specific use case in a single line of code.\n\nA finetuned model is easy to upload to production, letting you focus on building great applications.\n\n```python\nimport backprop\n\ntg = backprop.TextGeneration(\"t5-small\")\n\n# Any text works as training data\ninp = [\"I really liked the service I received!\", \"Meh, it was not impressive.\"]\nout = [\"positive\", \"negative\"]\n\n# Finetune with a single line of code\ntg.finetune({\"input_text\": inp, \"output_text\": out})\n\n# Use your trained model\nprediction = tg(\"I enjoyed it!\")\n\nprint(prediction)\n# Prints\n\"positive\"\n\n# Upload to Backprop for production ready inference\n# Describe your model\nname = \"t5-sentiment\"\ndescription = \"Predicts positive and negative sentiment\"\n\ntg.upload(name=name, description=description, api_key=\"abc\")\n```\n\nSee [finetuning for other tasks](https://backprop.readthedocs.io/en/latest/Finetuning.html).\n\n## Why Backprop?\n\n1. No experience needed\n\n   - Entrance to practical AI should be simple\n   - Get state-of-the-art performance in your task without being an expert\n\n2. Data is a bottleneck\n\n   - Solve real world tasks without any data\n   - With transfer learning, even a small amount of data can adapt a task to your niche requirements\n\n3. There are an overwhelming amount of models\n\n   - We offer a curated selection of the best open-source models and make them simple to use\n   - A few general models can accomplish more with less optimisation\n\n4. Deploying models cost effectively is hard work\n   - If our models suit your use case, no deployment is needed: just call our API\n   - Adapt and deploy your own model with just a few lines of code\n   - Our API scales, is always available, and you only pay for usage\n\n## Examples\n\n- Solve any text based task with Finetuning ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb), [Colab](https://colab.research.google.com/github/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb))\n- Search for images using text ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/ImageVectorisation.ipynb))\n- Finding answers from text ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/Q%26A.ipynb))\n- [More finetuning and task examples](https://github.com/backprop-ai/backprop/tree/main/examples)\n\n## Documentation\n\nCheck out our [docs](https://backprop.readthedocs.io/en/latest/) for in-depth task inference and finetuning.\n\n## Model Hub\n\nCurated list of [state-of-the-art models](https://backprop.co/hub).\n\n## Demos\n\nZero-shot image classification with [CLIP](https://clip.backprop.co).\n\n## Credits\n\nBackprop relies on many great libraries to work, most notably:\n\n* [PyTorch](https://github.com/pytorch/pytorch)\n* [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)\n* [Transformers](https://github.com/huggingface/transformers)\n* [Sentence Transformers](https://github.com/UKPLab/sentence-transformers)\n* [EfficientNet PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)\n* [CLIP](https://github.com/openai/CLIP)\n\n## Feedback\n\nFound a bug or have ideas for new tasks and models? Open an [issue](https://github.com/backprop-ai/backprop/issues).\n","funding_links":[],"categories":["Python","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Deployment and Serving","Pytorch \u0026 related libraries"],"sub_categories":["NLP \u0026 Speech Processing｜自然语言处理 \u0026 语音处理:","NLP \u0026 Speech Processing:","General-Purpose Machine Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbackprop-ai%2Fbackprop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbackprop-ai%2Fbackprop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbackprop-ai%2Fbackprop/lists"}