{"id":19287810,"url":"https://github.com/deeppavlov/ner-meta","last_synced_at":"2026-02-16T03:33:10.833Z","repository":{"id":83451017,"uuid":"127435340","full_name":"deeppavlov/ner-meta","owner":"deeppavlov","description":null,"archived":false,"fork":false,"pushed_at":"2022-12-11T05:53:57.000Z","size":4246,"stargazers_count":2,"open_issues_count":1,"forks_count":2,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-10-09T19:16:18.765Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deeppavlov.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-03-30T14:10:32.000Z","updated_at":"2022-09-13T18:28:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"42019884-3303-4429-a61a-a472df85e98c","html_url":"https://github.com/deeppavlov/ner-meta","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/deeppavlov/ner-meta","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeppavlov%2Fner-meta","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeppavlov%2Fner-meta/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeppavlov%2Fner-meta/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeppavlov%2Fner-meta/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deeppavlov","download_url":"https://codeload.github.com/deeppavlov/ner-meta/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deeppavlov%2Fner-meta/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29499615,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-16T02:07:14.481Z","status":"online","status_checked_at":"2026-02-16T02:03:22.852Z","response_time":115,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T22:07:18.019Z","updated_at":"2026-02-16T03:33:10.827Z","avatar_url":"https://github.com/deeppavlov.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"doc/static/allennlp-logo-dark.png\" /\u003e\u003c/p\u003e\n\n[![Build Status](http://build.allennlp.org/app/rest/builds/buildType:(id:AllenNLP_AllenNLPCommits)/statusIcon)](http://build.allennlp.org/viewType.html?buildTypeId=AllenNLP_AllenNLPCommits\u0026guest=1)\n[![codecov](https://codecov.io/gh/allenai/allennlp/branch/master/graph/badge.svg)](https://codecov.io/gh/allenai/allennlp)\n[![docker](https://images.microbadger.com/badges/version/allennlp/allennlp.svg)](https://microbadger.com/images/allennlp/allennlp)\n\nAn [Apache 2.0](https://github.com/allenai/allennlp/blob/master/LICENSE) NLP research library, built on PyTorch,\nfor developing state-of-the-art deep learning models on a wide variety of linguistic tasks.\n\n## Quickstart\n\nThe fastest way to get an environment to run AllenNLP is with Docker.  Once you have [installed Docker](https://docs.docker.com/engine/installation/)\njust run `docker run -it --rm allennlp/allennlp:v0.3.0` to get an environment that will run on either the cpu or gpu.\n\nNow you can do any of the following:\n\n* Run a model on example sentences with `python -m allennlp.run predict`.\n* Start a web service to host our models with `python -m allennlp.run serve`.\n* Interactively code against AllenNLP from the Python interpreter with `python`.\n\nYou can also install via the `pip` package manager or by cloning this repository into a Python 3.6 environment.\nSee below for more detailed instructions.\n\n## What is AllenNLP?\n\nBuilt on PyTorch, AllenNLP makes it easy to design and evaluate new deep\nlearning models for nearly any NLP problem, along with the infrastructure to\neasily run them in the cloud or on your laptop.  AllenNLP was designed with the\nfollowing principles:\n\n* *Hyper-modular and lightweight.* Use the parts which you like seamlessly with PyTorch.\n* *Extensively tested and easy to extend.* Test coverage is above 90% and the example\n  models provide a template for contributions.\n* *Take padding and masking seriously*, making it easy to implement correct\n  models without the pain.\n* *Experiment friendly.*  Run reproducible experiments from a json\n  specification with comprehensive logging.\n\nAllenNLP includes reference implementations of high quality models for Semantic\nRole Labelling, Question and Answering (BiDAF), Entailment (decomposable\nattention), and more.\n\nAllenNLP is built and maintained by the Allen Institute for Artificial\nIntelligence, in close collaboration with researchers at the University of\nWashington and elsewhere. With a dedicated team of best-in-field researchers\nand software engineers, the AllenNLP project is uniquely positioned to provide\nstate of the art models with high quality engineering.\n\n\u003ctable\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e an open-source NLP research library, built on PyTorch \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.commands \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e functionality for a CLI and web service \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.data \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e a data processing module for loading datasets and encoding strings as integers for representation in matrices \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.models \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e a collection of state-of-the-art models \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.modules \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e a collection of PyTorch modules for use with text \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.nn \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e tensor utility functions, such as initializers and activation functions \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.service \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e a web server to serve our demo and API \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\u003cb\u003e allennlp.training \u003c/b\u003e\u003c/td\u003e\n    \u003ctd\u003e functionality for training models \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n## Running AllenNLP\n\n### Setting up a virtual environment\n\n[Conda](https://conda.io/) can be used set up a virtual environment\nwith the version of Python required for AllenNLP and in which you can\nsandbox its dependencies:\n\n1.  [Download and install Conda](https://conda.io/docs/download.html).\n\n2.  Create a Conda environment with Python 3.6\n\n    ```\n    conda create -n allennlp python=3.6\n    ```\n\n3.  Activate the Conda environment.  (You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.\n\n    ```\n    source activate allennlp\n    ```\n\n4. Install AllenNLP in your environment.\n\n### Installing via pip\n\nThe preferred way to install AllenNLP into your environment is via `pip`:\n\n```\npip install allennlp\n```\n\nYou will also need to manually install some dependencies:\n\n1. Visit http://pytorch.org/ and install the relevant pytorch package.\n\n2. Download necessary spacy models. `python -m spacy download en_core_web_sm`.\n\nThat's it! You're now ready to build and train AllenNLP models.\n\n### Setting up a development environment\n\nIf you want to make changes to AllenNLP library itself\n(or use bleeding-edge code that hasn't been released to PyPI)\nyou'll need to install the library from GitHub and manually install the requirements:\n\n1. First, clone the repo:\n\n```\ngit clone https://github.com/allenai/allennlp.git\n```\n\n2. Change your directory to where you cloned the files:\n\n```\ncd allennlp\n```\n\n3.  Install the required dependencies.\n\n    ```\n    INSTALL_TEST_REQUIREMENTS=\"true\" ./scripts/install_requirements.sh\n    ```\n\n4. Visit http://pytorch.org/ and install the relevant pytorch package.\n\nYou should now be able to test your installation with `./scripts/verify.py`.  Congratulations!\n\n### Setting up a Docker development environment\n\nA third option is to run AllenNLP via Docker.\nDocker provides a virtual machine with everything set up to run AllenNLP--\nwhether you will leverage a GPU or just run on a CPU.\nDocker provides more isolation and consistency,\nand also makes it easy to distribute your environment\nto a compute cluster.\n\n#### Downloading a pre-built Docker image\n\nIt is easy to run a pre-built Docker development environment.\nAllenNLP is configured with Docker Cloud to build a\nnew image on every update to the master branch.  To download\nthe latest released from [Docker Hub](https://hub.docker.com/r/allennlp/) just run:\n\n```bash\ndocker pull allennlp/allennlp:v0.3.0\n```\n\n#### Building a Docker image\n\nFor various reasons you may need to create your own AllenNLP Docker image.\nThe same image can be used either with a CPU or a GPU.\n\nFirst, follow the instructions above for setting up a development environment.\nThen run the following command\n(it will take some time, as it completely builds the\nenvironment needed to run AllenNLP.)\n\n```bash\ndocker build --tag allennlp/allennlp .\n```\n\nYou should now be able to see this image listed by running `docker images allennlp`.\n\n```\nREPOSITORY          TAG                 IMAGE ID            CREATED             SIZE\nallennlp/allennlp            latest              b66aee6cb593        5 minutes ago       2.38GB\n```\n\n#### Running the Docker image\n\nYou can run the image with `docker run --rm -it allennlp/allennlp`.  The `--rm` flag cleans up the image on exit and the\n`-it` flags make the session interactive so you can use the bash shell the Docker image starts.\n\nYou can test your installation by running  `./scripts/verify.py`.\n\n\n## Team\n\nAllenNLP is an open-source project backed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org).\nAI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.\nTo learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/allennlp/graphs/contributors) page.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeeppavlov%2Fner-meta","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeeppavlov%2Fner-meta","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeeppavlov%2Fner-meta/lists"}