{
  "id": 117007,
  "title": "Ready-to-use:Transformed jsonl to dataframe",
  "url": "/competitions/tensorflow2-question-answering/discussion/117007",
  "author_name": "",
  "post_date": "2019-11-12T20:27:35.080708300Z",
  "votes": 1,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Attached are liked to dataset which are converted into data frames\nDataframes are saved as PKL file.\nJust load and work on \n- <a href=\"https://www.kaggle.com/rajnishe/tf-2-train-test-data\">https://www.kaggle.com/rajnishe/tf-2-train-test-data</a> <br>\n       contains 100K records and test file in csv format\n- <a href=\"https://www.kaggle.com/rajnishe/tf-data-file-2\">https://www.kaggle.com/rajnishe/tf-data-file-2</a>\n       contains next 100k records\n- <a href=\"https://www.kaggle.com/rajnishe/tf-data-file-3\">https://www.kaggle.com/rajnishe/tf-data-file-3</a>\n      contains rest 107373 records</p>\n\n<p>Dataframe is as :\n- document_text             100000 non-null object\n- long_answer_candidates    100000 non-null object\n- question_text             100000 non-null object\n- annotations               100000 non-null object\n- document_url              100000 non-null object\n- example_id                100000 non-null object</p>",
  "messages": [
    {
      "id": "671518",
      "postDate": "11/12/2019 20:27:35",
      "content": "<p>Attached are liked to dataset which are converted into data frames\nDataframes are saved as PKL file.\nJust load and work on \n- <a href=\"https://www.kaggle.com/rajnishe/tf-2-train-test-data\">https://www.kaggle.com/rajnishe/tf-2-train-test-data</a> <br>\n       contains 100K records and test file in csv format\n- <a href=\"https://www.kaggle.com/rajnishe/tf-data-file-2\">https://www.kaggle.com/rajnishe/tf-data-file-2</a>\n       contains next 100k records\n- <a href=\"https://www.kaggle.com/rajnishe/tf-data-file-3\">https://www.kaggle.com/rajnishe/tf-data-file-3</a>\n      contains rest 107373 records</p>\n\n<p>Dataframe is as :\n- document_text             100000 non-null object\n- long_answer_candidates    100000 non-null object\n- question_text             100000 non-null object\n- annotations               100000 non-null object\n- document_url              100000 non-null object\n- example_id                100000 non-null object</p>",
      "rawMarkdown": "Attached are liked to dataset which are converted into data frames\nDataframes are saved as PKL file.\nJust load and work on \n- https://www.kaggle.com/rajnishe/tf-2-train-test-data  \n       contains 100K records and test file in csv format\n- https://www.kaggle.com/rajnishe/tf-data-file-2\n       contains next 100k records\n- https://www.kaggle.com/rajnishe/tf-data-file-3\n      contains rest 107373 records\n\nDataframe is as :\n- document_text             100000 non-null object\n- long_answer_candidates    100000 non-null object\n- question_text             100000 non-null object\n- annotations               100000 non-null object\n- document_url              100000 non-null object\n- example_id                100000 non-null object",
      "votes": null
    },
    {
      "id": "691941",
      "postDate": "12/10/2019 17:13:54",
      "content": "<p>Hi !  It is nice.\nBut , how to use them ?</p>",
      "rawMarkdown": "Hi !  It is nice.\nBut , how to use them ?",
      "votes": null
    },
    {
      "id": "698219",
      "postDate": "12/19/2019 00:37:46",
      "content": "<p>Thanks a lot <a href=\"/rajnishe\">@rajnishe</a>  This has the potential of helping with quick iterations.\nPerhaps, it would be great if you could also include a short description as to how these dataframes might actually be used. A kernel or a code snippet would be good.</p>",
      "rawMarkdown": "Thanks a lot @rajnishe  This has the potential of helping with quick iterations.\nPerhaps, it would be great if you could also include a short description as to how these dataframes might actually be used. A kernel or a code snippet would be good.",
      "votes": null
    },
    {
      "id": "709188",
      "postDate": "01/03/2020 06:23:40",
      "content": "<p><a href=\"/rohitagarwal\">@rohitagarwal</a> I start working on this , it is just pre-processing of data into easy to iterate dataframe objects. But due to other priority I could not continue on this competition. Will start again as learning for NLP as till now I worked on Image classification and numeric data of manufacturing only.\nWill share code but it will take one more month and competition will be over by that.</p>",
      "rawMarkdown": "rohitagarwal I start working on this , it is just pre-processing of data into easy to iterate dataframe objects. But due to other priority I could not continue on this competition. Will start again as learning for NLP as till now I worked on Image classification and numeric data of manufacturing only.\nWill share code but it will take one more month and competition will be over by that.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 691941,
      "author_name": "hikarukondo",
      "author_url": "",
      "post_date": "12/10/2019 17:13:54",
      "content": "<p>Hi !  It is nice.\nBut , how to use them ?</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 698219,
      "author_name": "rohitagarwal",
      "author_url": "",
      "post_date": "12/19/2019 00:37:46",
      "content": "<p>Thanks a lot <a href=\"/rajnishe\">@rajnishe</a>  This has the potential of helping with quick iterations.\nPerhaps, it would be great if you could also include a short description as to how these dataframes might actually be used. A kernel or a code snippet would be good.</p>",
      "votes": null,
      "replies": [
        {
          "id": 709188,
          "author_name": "rajnishe",
          "author_url": "",
          "post_date": "01/03/2020 06:23:40",
          "content": "<p><a href=\"/rohitagarwal\">@rohitagarwal</a> I start working on this , it is just pre-processing of data into easy to iterate dataframe objects. But due to other priority I could not continue on this competition. Will start again as learning for NLP as till now I worked on Image classification and numeric data of manufacturing only.\nWill share code but it will take one more month and competition will be over by that.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "671518": "Attached are liked to dataset which are converted into data frames\nDataframes are saved as PKL file.\nJust load and work on \n- https://www.kaggle.com/rajnishe/tf-2-train-test-data  \n       contains 100K records and test file in csv format\n- https://www.kaggle.com/rajnishe/tf-data-file-2\n       contains next 100k records\n- https://www.kaggle.com/rajnishe/tf-data-file-3\n      contains rest 107373 records\n\nDataframe is as :\n- document_text             100000 non-null object\n- long_answer_candidates    100000 non-null object\n- question_text             100000 non-null object\n- annotations               100000 non-null object\n- document_url              100000 non-null object\n- example_id                100000 non-null object",
    "691941": "Hi !  It is nice.\nBut , how to use them ?",
    "698219": "Thanks a lot @rajnishe  This has the potential of helping with quick iterations.\nPerhaps, it would be great if you could also include a short description as to how these dataframes might actually be used. A kernel or a code snippet would be good.",
    "709188": "rohitagarwal I start working on this , it is just pre-processing of data into easy to iterate dataframe objects. But due to other priority I could not continue on this competition. Will start again as learning for NLP as till now I worked on Image classification and numeric data of manufacturing only.\nWill share code but it will take one more month and competition will be over by that."
  },
  "source": "meta"
}