{
  "id": 160494,
  "title": "International BERT? ",
  "url": "/competitions/jigsaw-multilingual-toxic-comment-classification/discussion/160494",
  "author_name": "",
  "post_date": "2020-06-21T12:55:15.576583100Z",
  "votes": 1,
  "comment_count": 6,
  "views": 0,
  "content": "<p>One idea I am currently trying: </p>\n\n<ul>\n<li>translating the train dataset from English into different test languages: 6 languages 'tr', 'ru', 'it', 'fr', 'pt', 'es'. </li>\n<li>training using each translated dataset and a fine-tuned BERT. For example BERTurk for Turkish and CamemBERT for French and so on. </li>\n<li>translating the test dataset to all the languages. For example, instead of having only 14000 Turkish rows, we will have as much as 63812 and so on.</li>\n<li>predicting the translated test datasets then ensembling:  the average of the 6 models could be a starting point.</li>\n</ul>\n\n<p>Has anyone tried this approach? Is there a notebook that implements this (even partially)? Of course, I understand if some of you want to share more about this after the end. Best of luck for the remaining time!</p>",
  "messages": [
    {
      "id": "895549",
      "postDate": "06/21/2020 12:55:15",
      "content": "<p>One idea I am currently trying: </p>\n\n<ul>\n<li>translating the train dataset from English into different test languages: 6 languages 'tr', 'ru', 'it', 'fr', 'pt', 'es'. </li>\n<li>training using each translated dataset and a fine-tuned BERT. For example BERTurk for Turkish and CamemBERT for French and so on. </li>\n<li>translating the test dataset to all the languages. For example, instead of having only 14000 Turkish rows, we will have as much as 63812 and so on.</li>\n<li>predicting the translated test datasets then ensembling:  the average of the 6 models could be a starting point.</li>\n</ul>\n\n<p>Has anyone tried this approach? Is there a notebook that implements this (even partially)? Of course, I understand if some of you want to share more about this after the end. Best of luck for the remaining time!</p>",
      "rawMarkdown": "One idea I am currently trying: \n\n- translating the train dataset from English into different test languages: 6 languages 'tr', 'ru', 'it', 'fr', 'pt', 'es'. \n- training using each translated dataset and a fine-tuned BERT. For example BERTurk for Turkish and CamemBERT for French and so on. \n- translating the test dataset to all the languages. For example, instead of having only 14000 Turkish rows, we will have as much as 63812 and so on.\n- predicting the translated test datasets then ensembling:  the average of the 6 models could be a starting point.\n\nHas anyone tried this approach? Is there a notebook that implements this (even partially)? Of course, I understand if some of you want to share more about this after the end. Best of luck for the remaining time!",
      "votes": null
    },
    {
      "id": "896072",
      "postDate": "06/21/2020 19:55:25",
      "content": "<p>I tried it earlier in this competition ....But led me nowhere :)   May it's because we have only base models for some  languages and Translation has bad quality for some of them .</p>\n\n<p>For French I used <a href=\"https://arxiv.org/abs/1912.05372\">FlauBERT</a> instead of Camembert</p>",
      "rawMarkdown": "I tried it earlier in this competition ....But led me nowhere :)   May it's because we have only base models for some  languages and Translation has bad quality for some of them .\n\nFor French I used [FlauBERT](https://arxiv.org/abs/1912.05372) instead of Camembert",
      "votes": null
    },
    {
      "id": "896083",
      "postDate": "06/21/2020 20:07:12",
      "content": "<p>As I know, data scientists from Jigsaw said that translations detoxify comments. So maybe you will succeed in such approach but I chose pretrained Multilingual model (XLM-R) and seems many other participants finetune that model.</p>",
      "rawMarkdown": "As I know, data scientists from Jigsaw said that translations detoxify comments. So maybe you will succeed in such approach but I chose pretrained Multilingual model (XLM-R) and seems many other participants finetune that model.",
      "votes": null
    },
    {
      "id": "896087",
      "postDate": "06/21/2020 20:11:12",
      "content": "<p>Yes indeed. I am trying this approach mainly to learn about the language variations of BERT. I have no hope to get something good in one day. 😁 </p>",
      "rawMarkdown": "Yes indeed. I am trying this approach mainly to learn about the language variations of BERT. I have no hope to get something good in one day. 😁",
      "votes": null
    },
    {
      "id": "896089",
      "postDate": "06/21/2020 20:12:47",
      "content": "<p>Thanks for the insights! Will also check FlauBERT. Very creative naming for BERT models as always. 😁 </p>",
      "rawMarkdown": "Thanks for the insights! Will also check FlauBERT. Very creative naming for BERT models as always. 😁",
      "votes": null
    },
    {
      "id": "896094",
      "postDate": "06/21/2020 20:19:58",
      "content": "<p>I didn't like \"Madame Bovary\" during high school....but appreciated later ;)</p>",
      "rawMarkdown": "I didn't like \"Madame Bovary\" during high school....but appreciated later ;)",
      "votes": null
    },
    {
      "id": "896229",
      "postDate": "06/22/2020 02:08:07",
      "content": "<p>CamemBERT </p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2F77b2512aaebc0e494ca8122cb4a20655%2Funnamed.jpg?generation=1592791685290637&amp;alt=media\" alt=\"\"></p>",
      "rawMarkdown": "CamemBERT \n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2F77b2512aaebc0e494ca8122cb4a20655%2Funnamed.jpg?generation=1592791685290637&amp;alt=media)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 896072,
      "author_name": "serigne",
      "author_url": "",
      "post_date": "06/21/2020 19:55:25",
      "content": "<p>I tried it earlier in this competition ....But led me nowhere :)   May it's because we have only base models for some  languages and Translation has bad quality for some of them .</p>\n\n<p>For French I used <a href=\"https://arxiv.org/abs/1912.05372\">FlauBERT</a> instead of Camembert</p>",
      "votes": null,
      "replies": [
        {
          "id": 896089,
          "author_name": "yassinealouini",
          "author_url": "",
          "post_date": "06/21/2020 20:12:47",
          "content": "<p>Thanks for the insights! Will also check FlauBERT. Very creative naming for BERT models as always. 😁 </p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 896094,
          "author_name": "serigne",
          "author_url": "",
          "post_date": "06/21/2020 20:19:58",
          "content": "<p>I didn't like \"Madame Bovary\" during high school....but appreciated later ;)</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 896083,
      "author_name": "aybatov",
      "author_url": "",
      "post_date": "06/21/2020 20:07:12",
      "content": "<p>As I know, data scientists from Jigsaw said that translations detoxify comments. So maybe you will succeed in such approach but I chose pretrained Multilingual model (XLM-R) and seems many other participants finetune that model.</p>",
      "votes": null,
      "replies": [
        {
          "id": 896087,
          "author_name": "yassinealouini",
          "author_url": "",
          "post_date": "06/21/2020 20:11:12",
          "content": "<p>Yes indeed. I am trying this approach mainly to learn about the language variations of BERT. I have no hope to get something good in one day. 😁 </p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 896229,
      "author_name": "muhakabartay",
      "author_url": "",
      "post_date": "06/22/2020 02:08:07",
      "content": "<p>CamemBERT </p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2F77b2512aaebc0e494ca8122cb4a20655%2Funnamed.jpg?generation=1592791685290637&amp;alt=media\" alt=\"\"></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "895549": "One idea I am currently trying: \n\n- translating the train dataset from English into different test languages: 6 languages 'tr', 'ru', 'it', 'fr', 'pt', 'es'. \n- training using each translated dataset and a fine-tuned BERT. For example BERTurk for Turkish and CamemBERT for French and so on. \n- translating the test dataset to all the languages. For example, instead of having only 14000 Turkish rows, we will have as much as 63812 and so on.\n- predicting the translated test datasets then ensembling:  the average of the 6 models could be a starting point.\n\nHas anyone tried this approach? Is there a notebook that implements this (even partially)? Of course, I understand if some of you want to share more about this after the end. Best of luck for the remaining time!",
    "896072": "I tried it earlier in this competition ....But led me nowhere :)   May it's because we have only base models for some  languages and Translation has bad quality for some of them .\n\nFor French I used [FlauBERT](https://arxiv.org/abs/1912.05372) instead of Camembert",
    "896083": "As I know, data scientists from Jigsaw said that translations detoxify comments. So maybe you will succeed in such approach but I chose pretrained Multilingual model (XLM-R) and seems many other participants finetune that model.",
    "896087": "Yes indeed. I am trying this approach mainly to learn about the language variations of BERT. I have no hope to get something good in one day. 😁",
    "896089": "Thanks for the insights! Will also check FlauBERT. Very creative naming for BERT models as always. 😁",
    "896094": "I didn't like \"Madame Bovary\" during high school....but appreciated later ;)",
    "896229": "CamemBERT \n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2F77b2512aaebc0e494ca8122cb4a20655%2Funnamed.jpg?generation=1592791685290637&amp;alt=media)"
  },
  "source": "meta"
}