{"id":17131907,"url":"https://github.com/avinashbarnwal/nlp","last_synced_at":"2025-03-24T05:18:11.260Z","repository":{"id":84695549,"uuid":"216915462","full_name":"avinashbarnwal/NLP","owner":"avinashbarnwal","description":"Learning NLP","archived":false,"fork":false,"pushed_at":"2024-12-16T08:25:41.000Z","size":3458,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-29T11:12:37.324Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/avinashbarnwal.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":"2019-10-22T21:46:08.000Z","updated_at":"2024-12-16T08:25:45.000Z","dependencies_parsed_at":"2023-11-25T13:29:25.526Z","dependency_job_id":"0c9d5e88-aab8-4aea-8db6-79f5756f6144","html_url":"https://github.com/avinashbarnwal/NLP","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/avinashbarnwal%2FNLP","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avinashbarnwal%2FNLP/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avinashbarnwal%2FNLP/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avinashbarnwal%2FNLP/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/avinashbarnwal","download_url":"https://codeload.github.com/avinashbarnwal/NLP/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245212311,"owners_count":20578443,"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":"2024-10-14T19:25:17.769Z","updated_at":"2025-03-24T05:18:11.241Z","avatar_url":"https://github.com/avinashbarnwal.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## NLP\n\n### Learning NLP Systematically\n\n#### Link\n- https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/91432#latest-636275\n\n### Word2Vec\n\n#### Link\n- https://towardsdatascience.com/an-implementation-guide-to-word2vec-using-numpy-and-google-sheets-13445eebd281\n\n#### Classification using BERT\n- https://www.kaggle.com/anushakarthik1991/nlp-with-disaster-tweets-eda-cleaning-and-bert\n\n#### Multilabel\n- https://towardsdatascience.com/journey-to-the-center-of-multi-label-classification-384c40229bff\n\n#### BERT\n\n- Question Answering (SQuAD v1.1),\n- Natural Language Inference (MNLI)\n- NER Modeling\n\n#### How to use BERT (Fine-tuning)\nUsing BERT for a specific task is relatively straightforward:\n- BERT can be used for a wide variety of language tasks, while only adding a small layer to the core model:\nClassification tasks such as sentiment analysis are done similarly to Next Sentence classification, by adding a classification layer on top of the Transformer output for the [CLS] token.\n- In Question Answering tasks (e.g. SQuAD v1.1), the software receives a question regarding a text sequence and is required to mark the answer in the sequence. Using BERT, a Q\u0026A model can be trained by learning two extra vectors that mark the beginning and the end of the answer.\n- In Named Entity Recognition (NER), the software receives a text sequence and is required to mark the various types of entities (Person, Organization, Date, etc) that appear in the text. Using BERT, a NER model can be trained by feeding the output vector of each token into a classification layer that predicts the NER label.\n\n#### ROC-Star\n\n- https://github.com/iridiumblue/roc-star/blob/master/README.md\n\n#### LLM\n- https://www.manning.com/books/build-a-large-language-model-from-scratch\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favinashbarnwal%2Fnlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Favinashbarnwal%2Fnlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favinashbarnwal%2Fnlp/lists"}