[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9762":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":16,"starSnapshotCount":16,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},9762,"DeepPavlov","deeppavlov\u002FDeepPavlov","deeppavlov","An open source library for deep learning end-to-end dialog systems and chatbots.","https:\u002F\u002Fdeeppavlov.ai",null,"Python",6987,1173,206,30,0,8,40.21,"Apache License 2.0",false,"master",true,[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"ai","artificial-intelligence","bot","chatbot","chitchat","deep-learning","deep-neural-networks","dialogue-agents","dialogue-manager","dialogue-systems","entity-extraction","intent-classification","intent-detection","machine-learning","named-entity-recognition","nlp","nlp-machine-learning","question-answering","slot-filling","tensorflow","2026-06-12 02:02:12","# DeepPavlov 1.0\n\n[![License Apache 2.0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache%202.0-blue.svg)](LICENSE)\n![Python 3.6, 3.7, 3.8, 3.9, 3.10, 3.11](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-green.svg)\n[![Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fdeeppavlov)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fdeeppavlov)\n[![Static Badge](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDeepPavlov%20Community-blue)](https:\u002F\u002Fforum.deeppavlov.ai\u002F)\n[![Static Badge](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDeepPavlov%20Demo-blue)](https:\u002F\u002Fdemo.deeppavlov.ai\u002F)\n\n\nDeepPavlov 1.0 is an open-source NLP framework built on [PyTorch](https:\u002F\u002Fpytorch.org\u002F) and [transformers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers). DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP\u002FML.\n\n## Quick Links\n\n|name|Description|\n|--|--|\n| ⭐️ [*Demo*](https:\u002F\u002Fdemo.deeppavlov.ai\u002F)|Check out our NLP models in the online demo|\n| 📚 [*Documentation*](http:\u002F\u002Fdocs.deeppavlov.ai\u002F)|How to use DeepPavlov 1.0 and its features|\n| 🚀 [*Model List*](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Ffeatures\u002Foverview.html)|Find the NLP model you need in the list of available models|\n| 🪐 [*Contribution Guide*](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fdevguides\u002Fcontribution_guide.html)|Please read the contribution guidelines before making a contribution|\n| 🎛 [*Issues*](https:\u002F\u002Fgithub.com\u002Fdeeppavlov\u002FDeepPavlov\u002Fissues)|If you have an issue with DeepPavlov, please let us know|\n| ⏩ [*Forum*](https:\u002F\u002Fforum.deeppavlov.ai\u002F)|Please let us know if you have a problem with DeepPavlov|\n| 📦 [*Blogs*](https:\u002F\u002Fmedium.com\u002Fdeeppavlov)|Read about our current development|\n| 🦙 [Extended colab tutorials](https:\u002F\u002Fgithub.com\u002Fdeeppavlov\u002Fdp_tutorials)|Check out the code tutorials for our models|\n| 🌌 [*Docker Hub*](https:\u002F\u002Fhub.docker.com\u002Fu\u002Fdeeppavlov\u002F)|Check out the Docker images for rapid deployment|\n| 👩‍🏫 [*Feedback*](https:\u002F\u002Fforms.gle\u002Fi64fowQmiVhMMC7f9)|Please leave us your feedback to make DeepPavlov better|\n\n\n## Installation\n\n0. DeepPavlov supports `Linux`, `Windows 10+` (through WSL\u002FWSL2), `MacOS` (Big Sur+) platforms, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10`.\n    Depending on the model used, you may need from 4 to 16 GB RAM.\n\n1. Create and activate a virtual environment:\n    * `Linux`\n\n    ```\n    python -m venv env\n    source .\u002Fenv\u002Fbin\u002Factivate\n    ```\n\n2. Install the package inside the environment:\n\n    ```\n    pip install deeppavlov\n    ```\n\n## QuickStart\n\nThere is a bunch of great pre-trained NLP models in DeepPavlov. Each model is\ndetermined by its config file.\n\nList of models is available on\n[the doc page](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Ffeatures\u002Foverview.html) in\nthe `deeppavlov.configs` (Python):\n\n```python\nfrom deeppavlov import configs\n```\n\nWhen you're decided on the model (+ config file), there are two ways to train,\nevaluate and infer it:\n\n* via [Command line interface (CLI)](#command-line-interface-cli) and\n* via [Python](#python).\n\n#### GPU requirements\n\nBy default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI could not support your device CUDA\ncapability. To run supported DeepPavlov models on GPU you should have [CUDA](https:\u002F\u002Fdeveloper.nvidia.com\u002Fcuda-toolkit)\ncompatible with used GPU and [PyTorch version](deeppavlov\u002Frequirements\u002Fpytorch.txt) required by DeepPavlov models.\nSee [docs](https:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fintro\u002Fquick_start.html#using-gpu) for details.\nGPU with Pascal or newer architecture and 4+ GB VRAM is recommended.\n\n### Command line interface (CLI)\n\nTo get predictions from a model interactively through CLI, run\n\n```bash\npython -m deeppavlov interact \u003Cconfig_path> [-d] [-i]\n```\n\n* `-d` downloads required data - pretrained model files and embeddings (optional).\n* `-i` installs model requirements (optional).\n\nYou can train it in the same simple way:\n\n```bash\npython -m deeppavlov train \u003Cconfig_path> [-d] [-i]\n```\n\nDataset will be downloaded regardless of whether there was `-d` flag or not.\n\nTo train on your own data you need to modify dataset reader path in the\n[train config doc](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fintro\u002Fconfig_description.html#train-config).\nThe data format is specified in the corresponding model doc page.\n\nThere are even more actions you can perform with configs:\n\n```bash\npython -m deeppavlov \u003Caction> \u003Cconfig_path> [-d] [-i]\n```\n\n* `\u003Caction>` can be\n  * `install` to install model requirements (same as `-i`),\n  * `download` to download model's data (same as `-d`),\n  * `train` to train the model on the data specified in the config file,\n  * `evaluate` to calculate metrics on the same dataset,\n  * `interact` to interact via CLI,\n  * `riseapi` to run a REST API server (see\n    [doc](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fintegrations\u002Frest_api.html)),\n  * `predict` to get prediction for samples from *stdin* or from\n      *\u003Cfile_path>* if `-f \u003Cfile_path>` is specified.\n* `\u003Cconfig_path>` specifies path (or name) of model's config file\n* `-d` downloads required data\n* `-i` installs model requirements\n\n### Python\n\nTo get predictions from a model interactively through Python, run\n\n```python\nfrom deeppavlov import build_model\n\nmodel = build_model(\u003Cconfig_path>, install=True, download=True)\n\n# get predictions for 'input_text1', 'input_text2'\nmodel(['input_text1', 'input_text2'])\n```\n\nwhere\n\n* `install=True` installs model requirements (optional),\n* `download=True` downloads required data from web - pretrained model files and embeddings (optional),\n* `\u003Cconfig_path>` is model name (e.g. `'ner_ontonotes_bert_mult'`), path to the chosen model's config file (e.g.\n  `\"deeppavlov\u002Fconfigs\u002Fner\u002Fner_ontonotes_bert_mult.json\"`),  or `deeppavlov.configs` attribute (e.g.\n  `deeppavlov.configs.ner.ner_ontonotes_bert_mult` without quotation marks).\n\nYou can train it in the same simple way:\n\n```python\nfrom deeppavlov import train_model \n\nmodel = train_model(\u003Cconfig_path>, install=True, download=True)\n```\n\nTo train on your own data you need to modify dataset reader path in the\n[train config doc](http:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fintro\u002Fconfig_description.html#train-config).\nThe data format is specified in the corresponding model doc page.\n\nYou can also calculate metrics on the dataset specified in your config file:\n\n```python\nfrom deeppavlov import evaluate_model \n\nmodel = evaluate_model(\u003Cconfig_path>, install=True, download=True)\n```\n\nDeepPavlov also [allows](https:\u002F\u002Fdocs.deeppavlov.ai\u002Fen\u002Fmaster\u002Fintro\u002Fpython.html) to build a model from components for\ninference using Python.\n\n## License\n\nDeepPavlov is Apache 2.0 - licensed.\n\n## Citation\n```\n@inproceedings{savkin-etal-2024-deeppavlov,\n    title = \"DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning\",\n    author = \"Savkin Maksim and Voznyuk Anastasia and Ignatov Fedor and Korzanova Anna and Karpov Dmitry and Popov Alexander and Konovalov Vasily\"\n    editor = \"Hernandez Farias and Delia Irazu and Hope Tom and Li Manling\",\n    booktitle = \"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https:\u002F\u002Faclanthology.org\u002F2024.emnlp-demo.47\",\n    pages = \"465--474\",\n    abstract = \"We present DeepPavlov 1.0, an open-source framework for using Natural Language Processing (NLP) models by leveraging transfer learning techniques. DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP\u002FML. DeepPavlov is based on PyTorch and supports HuggingFace transformers. DeepPavlov is publicly released under the Apache 2.0 license and provides access to an online demo.\",\n}\n```\n","DeepPavlov是一个用于构建端到端对话系统和聊天机器人的开源库。它基于PyTorch和transformers，支持多种自然语言处理（NLP）模型的应用开发，包括意图识别、实体提取、问答系统等，并且采用模块化与配置驱动的方式简化了模型的开发流程。该框架适合于希望快速搭建NLP应用但对相关技术了解有限的开发者使用。此外，DeepPavlov提供了丰富的文档、示例代码以及预训练模型，方便用户上手实践。",2,"2026-06-11 03:24:37","top_topic"]