{"id":19041286,"url":"https://github.com/piesposito/transformers-low-code-experiments","last_synced_at":"2025-07-31T22:42:29.660Z","repository":{"id":135983179,"uuid":"304038548","full_name":"piEsposito/transformers-low-code-experiments","owner":"piEsposito","description":"Low-code pre-built pipelines for experiments with huggingface/transformers for Data Scientists in a rush.","archived":false,"fork":false,"pushed_at":"2020-10-14T18:04:01.000Z","size":231,"stargazers_count":16,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-23T21:33:32.026Z","etag":null,"topics":["deep-learning","machine-learning","nlp","pytorch","transformer"],"latest_commit_sha":null,"homepage":"","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/piEsposito.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":"2020-10-14T14:20:34.000Z","updated_at":"2023-03-28T18:18:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"fd65593b-d7da-47eb-8a9e-ec4b37ca3e4b","html_url":"https://github.com/piEsposito/transformers-low-code-experiments","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/piEsposito/transformers-low-code-experiments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/piEsposito%2Ftransformers-low-code-experiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/piEsposito%2Ftransformers-low-code-experiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/piEsposito%2Ftransformers-low-code-experiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/piEsposito%2Ftransformers-low-code-experiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/piEsposito","download_url":"https://codeload.github.com/piEsposito/transformers-low-code-experiments/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/piEsposito%2Ftransformers-low-code-experiments/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259388315,"owners_count":22849769,"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":["deep-learning","machine-learning","nlp","pytorch","transformer"],"created_at":"2024-11-08T22:28:27.157Z","updated_at":"2025-06-12T03:16:05.757Z","avatar_url":"https://github.com/piEsposito.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Transformers for Data Scientists in a rush\nLow-code pre-built pipelines for experiments with huggingface/transformers for Data Scientists in a rush.\n\n---\nThis repository contains low-code, easy to understand, pre-built pipelines for fast experimentation on NLP tasks using [huggingface/transformers](https://github.com/huggingface/transformers) pre-trained language models, which are explained and explored in a post series on Medium about the theme. \n\nThis was inspired by a LinkedIn post of Thomas Wolf, HuggingFace's CSO in which there was an image of a low-code pipeline for fast experimentation on their Transformers repo. As I could not see anything like it implemented on the internet, I've decided to do it myself. \n\n# Index \nAs of now, we have:\n * [classification](#Classification), with a classification example. \n \n ## Classification\n On the classification example, we use a [dataset for email spam classification](https://www.kaggle.com/team-ai/spam-text-message-classification)  from Kaggle, and use [optuna](https://optuna.org/) for hyperparameter tuning.\n \n You might run it, on the classification directory, with:\n \n```bash\npython classification-experiment.py --model-name bert-base-multilingual-cased ---metric f1_score --train-data-path train.csv --test-data-path test.csv --max-sequence-length 25 --label-nbr 2\n```\n\nIt should yield a f1_score higher than 0.9.\n\n---\n###### Made by Pi Esposito","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiesposito%2Ftransformers-low-code-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpiesposito%2Ftransformers-low-code-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpiesposito%2Ftransformers-low-code-experiments/lists"}