From what I've found so far, the index for which the score is calculated is from the one you split with python standard str.split(' ') function. It means there are a lot of mis-splitted tokens.
\n\nThe the first line as example, the ground truth is 1960:1969, which stands for the following tokens.\n['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']
\n\nHere you can see, many corpus do not have \" s\" in them. There are many other symbols as well. If we clean the text it can cause misalignments. How do you deal with these symbols?
", "messages": [ { "id": "688116", "postDate": "12/05/2019 09:00:37", "content": "From what I've found so far, the index for which the score is calculated is from the one you split with python standard str.split(' ') function. It means there are a lot of mis-splitted tokens.
\n\nThe the first line as example, the ground truth is 1960:1969, which stands for the following tokens.\n['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']
\n\nHere you can see, many corpus do not have \" s\" in them. There are many other symbols as well. If we clean the text it can cause misalignments. How do you deal with these symbols?
", "rawMarkdown": "From what I've found so far, the index for which the score is calculated is from the one you split with python standard str.split(' ') function. It means there are a lot of mis-splitted tokens.\n\nThe the first line as example, the ground truth is 1960:1969, which stands for the following tokens.\n['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']\n\nHere you can see, many corpus do not have \" s\" in them. There are many other symbols as well. If we clean the text it can cause misalignments. How do you deal with these symbols?", "votes": null }, { "id": "688165", "postDate": "12/05/2019 10:00:14", "content": "You need to maintain a mapping from the original token indexes to whatever tokens you actually use in your model. \nThe general technique it to maintain an array of original token indexes for each model token. So in your example where you are converting:\n\norig_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm\\'s', 'customers']\n\nto the model tokens:\n\nmodel_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']\n
Your mapping would be:\n\ntok_map = [0, 1, 2, 3, 4, 5, 6, 6, 7]\n
Then given model_tok_idx you can do tok_map[model_tok_idx] to get the corresponding original token.
How exactly to best create this depends a bit on how you tokenise. But if you initially split on whitepace and then call some function to create model tokens you might use something like:\n\nmodel_toks = []\ntok_map = []\nfor tok_idx,tok in enumerate(doc.split(' ')):\n mts = create_model_tokens(tok)\n model_toks.extend(mts)\n tok_map.extend([tok_idx]*len(mts))\n
One more point, for bert tokenizer result of tokenizing firm 's and firm's are the same (and similar with other punctuation):
>>> tokenizer.tokenize(\"I: firm's\")\n['i', ':', 'firm', \"'\", 's']\n>>> tokenizer.tokenize(\"I : firm 's\")\n['i', ':', 'firm', \"'\", 's']\nIt might make sense to skip the data cleaning part in the workflow for this competition.
", "rawMarkdown": "It might make sense to skip the data cleaning part in the workflow for this competition.", "votes": null }, { "id": "698406", "postDate": "12/19/2019 07:24:20", "content": "I created another embedding just for missing values, hope that works fine.
", "rawMarkdown": "I created another embedding just for missing values, hope that works fine.", "votes": null }, { "id": "1633131", "postDate": "12/30/2021 12:46:56", "content": "How do you clean the data?
\nManaging structural errors
\nKeep track of the patterns that lead to the majority of your errors. When you measure or transfer data and find unusual naming conventions, typos, or wrong capitalization, you have structural issues.
\nVerify the accuracy of the data.
\nValidate the accuracy of your data after you've cleaned up your existing database. Maintaining your communication channels will reap far-reaching benefits from reviewing existing data for consistency and accuracy. This ensures that your customers will be able to pay you and that you will be able to meet any legal requirements. Some solutions even employ artificial intelligence (AI) or machine learning to improve accuracy testing.
\nLook for data that is duplicated.
\nTo save time when examining data, look for duplication. Remove any undesirable observations, such as duplicates or irrelevant observations, from your dataset. Research and invest in alternative data cleaning solutions that can examine raw data in bulk and automate the process for you to avoid repeating data. One of the most important aspects to consider in this procedure is deduplication.
\nExamine your data.
\nUse third-party sources to augment your data after it has been standardized, vetted, and cleansed for duplicates. Postcodes that are absent may result in undelivered products, while surnames that are lacking may result in the critical correspondence being misdirected.
\nFinal Lines!
\nTo obtain cleaned data, data cleaning is an integral aspect of the data science process. What is the significance of data cleansing in the corporate world? It all boils down to having accurate information. Consider it your workstation. You'll typically have trouble getting the raw data if you try to bypass the data cleansing stages. It will clog up your database to the point where the data you're pulling is untrustworthy. As a result, the data cleaning procedures and data cleaning methods must be taken into account.
How do you clean the data?
\nManaging structural errors
\nKeep track of the patterns that lead to the majority of your errors. When you measure or transfer data and find unusual naming conventions, typos, or wrong capitalization, you have structural issues.
\nVerify the accuracy of the data.
\nValidate the accuracy of your data after you've cleaned up your existing database. Maintaining your communication channels will reap far-reaching benefits from reviewing existing data for consistency and accuracy. This ensures that your customers will be able to pay you and that you will be able to meet any legal requirements. Some solutions even employ artificial intelligence (AI) or machine learning to improve accuracy testing.
\nLook for data that is duplicated.
\nTo save time when examining data, look for duplication. Remove any undesirable observations, such as duplicates or irrelevant observations, from your dataset. Research and invest in alternative data cleaning solutions that can examine raw data in bulk and automate the process for you to avoid repeating data. One of the most important aspects to consider in this procedure is deduplication.
\nExamine your data.
\nUse third-party sources to augment your data after it has been standardized, vetted, and cleansed for duplicates. Postcodes that are absent may result in undelivered products, while surnames that are lacking may result in the critical correspondence being misdirected.
\nFinal Lines!
\nTo obtain cleaned data, data cleaning is an integral aspect of the data science process. What is the significance of data cleansing in the corporate world? It all boils down to having accurate information. Consider it your workstation. You'll typically have trouble getting the raw data if you try to bypass the data cleansing stages. It will clog up your database to the point where the data you're pulling is untrustworthy. As a result, the data cleaning procedures and data cleaning methods must be taken into account.
You need to maintain a mapping from the original token indexes to whatever tokens you actually use in your model. \nThe general technique it to maintain an array of original token indexes for each model token. So in your example where you are converting:\n\norig_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm\\'s', 'customers']\n\nto the model tokens:\n\nmodel_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']\n
Your mapping would be:\n\ntok_map = [0, 1, 2, 3, 4, 5, 6, 6, 7]\n
Then given model_tok_idx you can do tok_map[model_tok_idx] to get the corresponding original token.
How exactly to best create this depends a bit on how you tokenise. But if you initially split on whitepace and then call some function to create model tokens you might use something like:\n\nmodel_toks = []\ntok_map = []\nfor tok_idx,tok in enumerate(doc.split(' ')):\n mts = create_model_tokens(tok)\n model_toks.extend(mts)\n tok_map.extend([tok_idx]*len(mts))\n
One more point, for bert tokenizer result of tokenizing firm 's and firm's are the same (and similar with other punctuation):
>>> tokenizer.tokenize(\"I: firm's\")\n['i', ':', 'firm', \"'\", 's']\n>>> tokenizer.tokenize(\"I : firm 's\")\n['i', ':', 'firm', \"'\", 's']\nIt might make sense to skip the data cleaning part in the workflow for this competition.
", "votes": null, "replies": [ { "id": 698406, "author_name": "icemekaveli", "author_url": "", "post_date": "12/19/2019 07:24:20", "content": "I created another embedding just for missing values, hope that works fine.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "688116": "From what I've found so far, the index for which the score is calculated is from the one you split with python standard str.split(' ') function. It means there are a lot of mis-splitted tokens.\n\nThe the first line as example, the ground truth is 1960:1969, which stands for the following tokens.\n['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']\n\nHere you can see, many corpus do not have \" s\" in them. There are many other symbols as well. If we clean the text it can cause misalignments. How do you deal with these symbols?", "688165": "You need to maintain a mapping from the original token indexes to whatever tokens you actually use in your model. \nThe general technique it to maintain an array of original token indexes for each model token. So in your example where you are converting:\n```\norig_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm\\'s', 'customers']\n```\nto the model tokens:\n```\nmodel_toks = ['a', 'newsletter', 'sent', 'to', 'an', 'advertising', 'firm', \"'s\", 'customers']\n```\n\nYour mapping would be:\n```\ntok_map = [0, 1, 2, 3, 4, 5, 6, 6, 7]\n```\n\nThen given `model_tok_idx` you can do `tok_map[model_tok_idx]` to get the corresponding original token.\n\nHow exactly to best create this depends a bit on how you tokenise. But if you initially split on whitepace and then call some function to create model tokens you might use something like:\n```\nmodel_toks = []\ntok_map = []\nfor tok_idx,tok in enumerate(doc.split(' ')):\n mts = create_model_tokens(tok)\n model_toks.extend(mts)\n tok_map.extend([tok_idx]*len(mts))\n```", "688198": "One more point, for bert tokenizer result of tokenizing ``firm 's`` and ``firm's`` are the same (and similar with other punctuation):\n\n >>> tokenizer.tokenize(\"I: firm's\")\n ['i', ':', 'firm', \"'\", 's']\n >>> tokenizer.tokenize(\"I : firm 's\")\n ['i', ':', 'firm', \"'\", 's']", "689330": "", "697751": "It might make sense to skip the data cleaning part in the workflow for this competition.", "698406": "I created another embedding just for missing values, hope that works fine.", "1633131": "**How do you clean the data?**\n\n**Managing structural errors**\n\nKeep track of the patterns that lead to the majority of your errors. When you measure or transfer data and find unusual naming conventions, typos, or wrong capitalization, you have structural issues. \n\n**Verify the accuracy of the data.**\n\nValidate the accuracy of your data after you've cleaned up your existing database. Maintaining your communication channels will reap far-reaching benefits from reviewing existing data for consistency and accuracy. This ensures that your customers will be able to pay you and that you will be able to meet any legal requirements. Some solutions even employ artificial intelligence (AI) or machine learning to improve accuracy testing.\n\n**Look for data that is duplicated.**\n\nTo save time when examining data, look for duplication. Remove any undesirable observations, such as duplicates or irrelevant observations, from your dataset. Research and invest in alternative data cleaning solutions that can examine raw data in bulk and automate the process for you to avoid repeating data. One of the most important aspects to consider in this procedure is deduplication.\n\n**Examine your data.**\n\nUse third-party sources to augment your data after it has been standardized, vetted, and cleansed for duplicates. Postcodes that are absent may result in undelivered products, while surnames that are lacking may result in the critical correspondence being misdirected. \n\n**Final Lines!**\nTo obtain cleaned data, data cleaning is an integral aspect of the [data science](https://www.learnbay.co/data-science-course/) process. What is the significance of data cleansing in the corporate world? It all boils down to having accurate information. Consider it your workstation. You'll typically have trouble getting the raw data if you try to bypass the data cleansing stages. It will clog up your database to the point where the data you're pulling is untrustworthy. As a result, the data cleaning procedures and data cleaning methods must be taken into account." }, "source": "meta" }