{"id":24429212,"url":"https://github.com/peterk/swebert-summarize","last_synced_at":"2025-12-30T00:03:24.753Z","repository":{"id":66470421,"uuid":"265573755","full_name":"peterk/swebert-summarize","owner":"peterk","description":"A tiny NLP demo on how to summarize swedish texts using the BERT model from the National Library of Sweden.","archived":false,"fork":false,"pushed_at":"2024-07-27T15:51:10.000Z","size":152,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-20T13:35:32.110Z","etag":null,"topics":["bert-model","docker","nlp-machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/peterk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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}},"created_at":"2020-05-20T13:27:06.000Z","updated_at":"2024-07-27T15:51:06.000Z","dependencies_parsed_at":"2025-01-20T13:34:04.014Z","dependency_job_id":"5b5eda48-9689-4d0b-a960-d12161abcb26","html_url":"https://github.com/peterk/swebert-summarize","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/peterk%2Fswebert-summarize","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/peterk%2Fswebert-summarize/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/peterk%2Fswebert-summarize/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/peterk%2Fswebert-summarize/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/peterk","download_url":"https://codeload.github.com/peterk/swebert-summarize/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243456566,"owners_count":20293905,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["bert-model","docker","nlp-machine-learning","python"],"created_at":"2025-01-20T13:33:51.389Z","updated_at":"2025-12-30T00:03:24.712Z","avatar_url":"https://github.com/peterk.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# swebert-summarize\nA tiny demo on how to summarize swedish texts using the [BERT model from the National Library of Sweden](https://github.com/Kungbib/swedish-bert-models). It uses the [Bert Extractive Summarizer library](https://pypi.org/project/bert-extractive-summarizer/).\n\nInstall requirements:\n\n```pip install -r requirements.txt```\n\nRun it with \n\n```python summarize.py my_long_text.txt```\n\n# Simple web gui\nA simple web gui in Flask is provided for testing. To run it, install Docker and docker-compose. \n\nTo run it:\n\n```docker-compose up -d```\n\nOn the first run the BERT-model is downloaded (this may take several minutes). The ser ver will be available on 0.0.0.0:80.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpeterk%2Fswebert-summarize","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpeterk%2Fswebert-summarize","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpeterk%2Fswebert-summarize/lists"}