{"id":19710799,"url":"https://github.com/rootstrap/biobert-test","last_synced_at":"2025-05-07T19:10:08.054Z","repository":{"id":74711061,"uuid":"344228978","full_name":"rootstrap/biobert-test","owner":"rootstrap","description":null,"archived":false,"fork":false,"pushed_at":"2021-03-22T21:04:29.000Z","size":22,"stargazers_count":3,"open_issues_count":3,"forks_count":2,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-05-07T19:09:57.168Z","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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rootstrap.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2021-03-03T18:41:10.000Z","updated_at":"2024-08-31T11:33:52.000Z","dependencies_parsed_at":"2023-07-04T01:16:55.308Z","dependency_job_id":null,"html_url":"https://github.com/rootstrap/biobert-test","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/rootstrap%2Fbiobert-test","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootstrap%2Fbiobert-test/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootstrap%2Fbiobert-test/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootstrap%2Fbiobert-test/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rootstrap","download_url":"https://codeload.github.com/rootstrap/biobert-test/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252940934,"owners_count":21828769,"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":[],"created_at":"2024-11-11T22:08:26.916Z","updated_at":"2025-05-07T19:10:07.904Z","avatar_url":"https://github.com/rootstrap.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# biobert-test\n\nThis code is for medical text summarization by extractive strategy, using bert with a pre-trained model. \n\nThe original code is from [BERT-based-Summ](https://github.com/BioTextSumm/BERT-based-Summ). The associated paper for \nthe code is [Deep contextualized embeddings for quantifying the informative content in biomedical text summarization\n](https://www.researchgate.net/publication/336272974_Deep_contextualized_embeddings_for_quantifying_the_informative_content_in_biomedical_text_summarization)\n\nThe objective of the paper is to show how contextualized  embeddings produced  by  a  deep bidirectional language  model \n can  be  utilized  to  quantify  the  informative content of sentences in biomedical text summarization \n\n(5) (PDF) Deep contextualized embeddings for quantifying the informative content in biomedical text summarization. Available from: https://www.researchgate.net/publication/336272974_Deep_contextualized_embeddings_for_quantifying_the_informative_content_in_biomedical_text_summarization [accessed Mar 05 2021].\nontext-sensitive embeddings for biomedical text summarization\n\nSome modifications to the [Summarizer.py](Summarizer.py) script have been done. \n\nDue to compatibility issues it works with python3.6.   \n\n## Setup Script \nThe [setup.sh](setup.sh) script is created to automatically download all the dependencies for the project. It follows these steps:  \n1. Clone [bert](https://github.com/google-research/bert.git) repository \n2. Download pre-trained biobert_v1.1_pubmed model \n3. Install python dependencies in a virtualenv \n4. Creates directories INPUT OUTPUT TEMP\n\n```bash\n    sh setup.sh\n```\n\n## Run the Summarizer script \n1. Locate the input file at INPUT directory\n2. Run the script: \n\nFour parameters must be specified when running the script:    \n- INPUT_FILE_NAME(-i) is the name of input file already copied to the INPUT directory.\n- OUTPUT_FILE_NAME(-o) is the name of output file containing the summary that will be created in the OUTPUT directory.\n- COMPRESSION_RATE(-c) specifies the size of summary and takes a value in the range (0, 1).\n- NUMBER_OF_CLUSTERS(-k) specifies the number of final clusters in the clustering step.\n\n```bash\n     python3.6 Summarizer.py -i 14.txt -o 14.txt -c 0.5 -k 4\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frootstrap%2Fbiobert-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frootstrap%2Fbiobert-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frootstrap%2Fbiobert-test/lists"}