{"id":18777424,"url":"https://github.com/cloudera/cml_amp_spacy_entity_extraction","last_synced_at":"2026-04-01T22:08:33.088Z","repository":{"id":42412094,"uuid":"333970674","full_name":"cloudera/CML_AMP_SpaCy_Entity_Extraction","owner":"cloudera","description":"A Jupyter notebook demonstrating entity extraction on headlines with SpaCy.","archived":false,"fork":false,"pushed_at":"2022-04-06T21:32:56.000Z","size":3522,"stargazers_count":4,"open_issues_count":0,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2026-01-18T04:30:59.769Z","etag":null,"topics":["entity-extraction","named-entity-recognition","nlp","spacy"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/cloudera.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-28T22:16:25.000Z","updated_at":"2024-06-05T07:09:27.000Z","dependencies_parsed_at":"2022-09-10T12:02:52.191Z","dependency_job_id":null,"html_url":"https://github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/cloudera/CML_AMP_SpaCy_Entity_Extraction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloudera%2FCML_AMP_SpaCy_Entity_Extraction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloudera%2FCML_AMP_SpaCy_Entity_Extraction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloudera%2FCML_AMP_SpaCy_Entity_Extraction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloudera%2FCML_AMP_SpaCy_Entity_Extraction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cloudera","download_url":"https://codeload.github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloudera%2FCML_AMP_SpaCy_Entity_Extraction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31018566,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-27T03:51:26.850Z","status":"ssl_error","status_checked_at":"2026-03-27T03:51:09.693Z","response_time":164,"last_error":"SSL_read: 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":false,"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":["entity-extraction","named-entity-recognition","nlp","spacy"],"created_at":"2024-11-07T20:10:38.583Z","updated_at":"2026-03-27T04:23:30.906Z","avatar_url":"https://github.com/cloudera.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Analyzing News Headlines with SpaCy\n\n[SpaCy](https://spacy.io/) wraps industrial-strength natural language processing capabilites into a Python library with an elegant and powerful API. The notebook in this repo demonstrates its use for Named Entity Recognition (NER) on a real world news dataset.\n\n![Sentences with named entities highlighted.](docs/images/NER.png)\n\nWe take a public domain dataset of [Reuters news headlines](https://www.kaggle.com/notlucasp/financial-news-headlines) and use spaCy to extract named entities. We demonstrate three example downstream use cases:\n\n- investigating the organizations that appeared most often in Reuters in 2020\n- viewing the mentions of any given organization over time\n- inspecting which organizations appear in headlines together\n\n## Deploying on Cloudera Machine Learning (CML)\n\nThere are three ways to launch this notebook on CML:\n\n1. **From Prototype Catalog** - Navigate to the Prototype Catalog in a CML workspace, select the \"Analyzing News Headlines with SpaCy\" tile, click \"Launch as Project\", click \"Configure Project\"\n2. **As ML Prototype** - In a CML workspace, click \"New Project\", add a Project Name, select \"ML Prototype\" as the Initial Setup option, copy in the [repo URL](https://github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction), click \"Create Project\", click \"Configure Project\"\n3. **Manual Setup** - In a CML workspace, click \"New Project\", add a Project Name, select \"Git\" as the Initial Setup option, copy in the [repo URL](https://github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction), click \"Create Project\".\n\nOnce the project has been initialized in a CML workspace, run the notebook by starting a Python 3 Jupyter notebook server session. All library and model dependencies are installed inline in the notebook.\n\nHappy hacking!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloudera%2Fcml_amp_spacy_entity_extraction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcloudera%2Fcml_amp_spacy_entity_extraction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloudera%2Fcml_amp_spacy_entity_extraction/lists"}