{ "id": 334951, "title": "Best local CV discussion thread", "url": "/competitions/dlsprint/discussion/334951", "author_name": "", "post_date": "2022-07-04T00:40:23.765225200Z", "votes": 17, "comment_count": 1, "views": 0, "content": "

We know that, One secret of doing well in kaggle competition is to design a robust validation strategy and trusting that more than the leaderboard.
\nIn this notebook, commonvoice_bn xls-r metric calculation we tried to demonstrate how to calculate CV using the best publicly available pretrained model for this task which is this one arijitx/wav2vec2-xls-r-300m-bengali from huggingface. you can use the results.csv file attached as kernel output for better error analysis and try to come up with good post-processing trick that maximizes CV. i personally think post processing will play vital role in this competition.
\nfor example -> ground truth : তিন বছর বয়সে তার বাবা মারা যান and predicted sentence তিন বছর বয়সে তাঁর বাবা মারা যান।
\nhere তার and তাঁর both represents kind of same information and also that extra '।' looks okay too, but these are going to increase error rate according to the metric for this task. So beside modeling and blending, if you can come up with unique preprocessing and post-processing techniques too then it might help.
\nIf you are a beginner and not sure from where to start then IMHO this should be your starting point Wave2Vec2 Starter for DL Sprint::Commonvoice

\n

As you can see from the metric calculation notebook that the best publicly available notebook is already getting :
\nvalidation cer_score -> 0.09787704766628824

\n

validation wer_score -> 0.30921300101701055
\non this competition's validation dataset.

\n

So no matter what model you try, you should check rigorously if that model of you is getting better WER, CER score compared to best publicly available model or not.

\n

In this discussion thread if you are interested then you can share your best single model's CV and LB score.

\n

I Wish You Luck

", "messages": [ { "id": "1842387", "postDate": "07/04/2022 00:40:23", "content": "

We know that, One secret of doing well in kaggle competition is to design a robust validation strategy and trusting that more than the leaderboard.
\nIn this notebook, commonvoice_bn xls-r metric calculation we tried to demonstrate how to calculate CV using the best publicly available pretrained model for this task which is this one arijitx/wav2vec2-xls-r-300m-bengali from huggingface. you can use the results.csv file attached as kernel output for better error analysis and try to come up with good post-processing trick that maximizes CV. i personally think post processing will play vital role in this competition.
\nfor example -> ground truth : তিন বছর বয়সে তার বাবা মারা যান and predicted sentence তিন বছর বয়সে তাঁর বাবা মারা যান।
\nhere তার and তাঁর both represents kind of same information and also that extra '।' looks okay too, but these are going to increase error rate according to the metric for this task. So beside modeling and blending, if you can come up with unique preprocessing and post-processing techniques too then it might help.
\nIf you are a beginner and not sure from where to start then IMHO this should be your starting point Wave2Vec2 Starter for DL Sprint::Commonvoice

\n

As you can see from the metric calculation notebook that the best publicly available notebook is already getting :
\nvalidation cer_score -> 0.09787704766628824

\n

validation wer_score -> 0.30921300101701055
\non this competition's validation dataset.

\n

So no matter what model you try, you should check rigorously if that model of you is getting better WER, CER score compared to best publicly available model or not.

\n

In this discussion thread if you are interested then you can share your best single model's CV and LB score.

\n

I Wish You Luck

", "rawMarkdown": "We know that, One secret of doing well in kaggle competition is to **design a robust validation strategy and trusting that more than the leaderboard**.\nIn this notebook, [commonvoice_bn xls-r metric calculation](https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation/notebook) we tried to demonstrate how to calculate CV using the best publicly available pretrained model for this task which is this one [arijitx/wav2vec2-xls-r-300m-bengali](https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali) from huggingface. you can use the results.csv file attached as kernel output for better error analysis and try to come up with good post-processing trick that maximizes CV. i personally think post processing will play vital role in this competition.\nfor example -> **ground truth :** তিন বছর বয়সে তার বাবা মারা যান and **predicted sentence** তিন বছর বয়সে তাঁর বাবা মারা যান।\nhere তার and তাঁর both represents kind of same information and also that extra '।' looks okay too, but these are going to increase error rate according to the metric for this task. So beside modeling and blending, if you can come up with unique preprocessing and post-processing techniques too then it might help.\nIf you are a beginner and not sure from where to start then IMHO this should be your starting point [Wave2Vec2 Starter for DL Sprint::Commonvoice ](https://www.kaggle.com/code/nazmuddhohaansary/wave2vec2-starter-for-dl-sprint-commonvoice)\n\nAs you can see from the metric calculation notebook that the best publicly available notebook is already getting :\n**validation cer_score -> 0.09787704766628824**\n\n**validation wer_score -> 0.30921300101701055**\non this competition's validation dataset.\n\nSo no matter what model you try, you should check rigorously if that model of you is getting better WER, CER score compared to best publicly available model or not.\n\nIn this discussion thread if you are interested then you can share your best single model's CV and LB score.\n\nI Wish You Luck", "votes": null }, { "id": "1856670", "postDate": "07/15/2022 13:55:29", "content": "

CV with post-processing

\n

validation cer_score -> 0.09301847750965592
\nvalidation wer_score -> 0.28501372267654884
\nmodel -> wav2vec2-xls-r-300m-bengali
\npublic lb score -> 6.53524
\ninference notebook -> https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation

", "rawMarkdown": "**CV with post-processing**\n\nvalidation cer_score -> 0.09301847750965592\nvalidation wer_score -> 0.28501372267654884\nmodel -> [wav2vec2-xls-r-300m-bengali](https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali)\npublic lb score -> 6.53524\ninference notebook -> https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation", "votes": null } ], "comments": [ { "id": 1856670, "author_name": "mobassir", "author_url": "", "post_date": "07/15/2022 13:55:29", "content": "

CV with post-processing

\n

validation cer_score -> 0.09301847750965592
\nvalidation wer_score -> 0.28501372267654884
\nmodel -> wav2vec2-xls-r-300m-bengali
\npublic lb score -> 6.53524
\ninference notebook -> https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1842387": "We know that, One secret of doing well in kaggle competition is to **design a robust validation strategy and trusting that more than the leaderboard**.\nIn this notebook, [commonvoice_bn xls-r metric calculation](https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation/notebook) we tried to demonstrate how to calculate CV using the best publicly available pretrained model for this task which is this one [arijitx/wav2vec2-xls-r-300m-bengali](https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali) from huggingface. you can use the results.csv file attached as kernel output for better error analysis and try to come up with good post-processing trick that maximizes CV. i personally think post processing will play vital role in this competition.\nfor example -> **ground truth :** তিন বছর বয়সে তার বাবা মারা যান and **predicted sentence** তিন বছর বয়সে তাঁর বাবা মারা যান।\nhere তার and তাঁর both represents kind of same information and also that extra '।' looks okay too, but these are going to increase error rate according to the metric for this task. So beside modeling and blending, if you can come up with unique preprocessing and post-processing techniques too then it might help.\nIf you are a beginner and not sure from where to start then IMHO this should be your starting point [Wave2Vec2 Starter for DL Sprint::Commonvoice ](https://www.kaggle.com/code/nazmuddhohaansary/wave2vec2-starter-for-dl-sprint-commonvoice)\n\nAs you can see from the metric calculation notebook that the best publicly available notebook is already getting :\n**validation cer_score -> 0.09787704766628824**\n\n**validation wer_score -> 0.30921300101701055**\non this competition's validation dataset.\n\nSo no matter what model you try, you should check rigorously if that model of you is getting better WER, CER score compared to best publicly available model or not.\n\nIn this discussion thread if you are interested then you can share your best single model's CV and LB score.\n\nI Wish You Luck", "1856670": "**CV with post-processing**\n\nvalidation cer_score -> 0.09301847750965592\nvalidation wer_score -> 0.28501372267654884\nmodel -> [wav2vec2-xls-r-300m-bengali](https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali)\npublic lb score -> 6.53524\ninference notebook -> https://www.kaggle.com/code/mobassir/commonvoice-bn-xls-r-metric-calculation" }, "source": "meta" }