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change the backgrounds of collapsable sample text
Browse files
web.py
CHANGED
@@ -297,8 +297,9 @@ def web_data():
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Summary("Text Extraction Examples"),
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DV2("data/sample_wet.json", "data/sample_warc.json", 3),
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style="""
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-
background-color: #
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border-radius: 12px;
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""", #https://colors.muz.li/palette/d3d3d3/949494/d3d3d3/d3d3d3/949494
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),
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@@ -314,6 +315,11 @@ def web_data():
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Details(
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Summary("Non-English Documents"),
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DV("data/sample_non_en.json", 3, "Sample documents that are classified as non-English"),
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#DV("data/sample_non_en.json", 3, "Sample documents that are classified as non-English"),
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@@ -321,6 +327,11 @@ def web_data():
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Details(
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Summary("English Documents Scoring Lower than 0.65"),
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DV("data/sample_en_low.json", 3, "Sample documents that are classified as English but with score less than 0.65"),
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),
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H3("1.3 URL Filtering"),
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Details(
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Summary("24 URL domains with more than 4k matches"),
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DVS(urls_high_matches, "24 URL domains with more than 4k matches"),
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),
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P("""
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Details(
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Summary("6 url domains that are removed from the blocklist"),
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DVS(urls_false_positives, "6 url domains that are removed from the blocklist"),
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),
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Details(
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"data/bad_url_doc.jsonl",
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3,
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"Sample documents whose urls are blocked by the refined url blocklist",
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),
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),
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H3("1.3.2 Excluded High Quality Sources"),
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non_web_urls,
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"curated url domains that are excluded from our dataset",
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Details(
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Summary("Sample documents whose urls are in our curated url domain list"),
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DV("data/sample_url_exclusion.json", 0, "Sample documents whose urls are in our curated url domain list"),
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),
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@@ -401,6 +437,11 @@ def web_data():
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"Sample documents with lines that are removed by the rule of terminal punctuation",
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@@ -422,6 +463,11 @@ def web_data():
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"Sample documents that are removed by original C4 javascript rule but are kept after our refinement",
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),
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H3("2.2 Other Rules from RefinedWeb"),
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P("""
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"Sample documents with lines that are removed by the RefinedWeb rules",
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H3("2.3 Toxic Lines"),
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P("""
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json.load(open("data/toxic_lines.json")),
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"Sample documents with toxic lines",
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),
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),
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H2("3. Document-Level Filtering"),
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json.load(open("data/all_signals.json")),
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"Overview of all the quality signals that are used for filtering",
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P("""Similar to previous sections, we will present sample documents filtered out by the given quality signals.
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Most quality signals were initially introduced by Gopher [2] and subsequently adopted by later
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len(line) * count for line, count in line_counts.items() if count > 1
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) / max(character_count, 1)
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""", block="block", language="python"),
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),
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Details(
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Summary("Implementations from DataTrove"),
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if self.dup_line_char_frac and char_duplicates / len(text) > self.dup_line_char_frac:
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return False, "dup_line_char_frac"
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""", block="block", language="python"),
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P("""
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After evaluating the implementations of Dolma and DataTrove (note: RedPajama V2 does not implement these two quality
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sum(sum(len(w) for w in line.split()) * (count - 1) for line, count in
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line_counts.items() if count > 1) / character_count
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""", block="block", language="python"),
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Details(
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Summary("Sample documents filtered by excessive line repetitions / characters in repeated lines"),
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0,
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"Sample documents filtered by excessive line repetitions / characters in repeated lines",
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H3("3.1.2 Fraction of Characters in the Most Common N-grams (n=2,3,4)"),
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P("""
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value = count * sum(len(w) for w in most_common_ngram) / max(character_count, 1)
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attrs.fraction_of_characters_in_most_common_ngram.append((n, value))
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""", block="block", language="python"),
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),
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Details(
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Summary("Implementations from RedPajama-V2"),
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@@ -649,6 +735,11 @@ def web_data():
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score = round(score, PRECISION)
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return [(0, len(document), score)]
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""", block="block", language="python"),
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),
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Details(
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@@ -672,6 +763,11 @@ def web_data():
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if top_char_length / len(text) > n_frac:
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return False, f"top_n_gram"
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""", block="block", language="python"),
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),
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P("""
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There are almost no contradictions between each implementations of fractions of characters in the most common
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value = count * sum(len(w) for w in most_common_ngram) / character_count
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attrs.fraction_of_characters_in_most_common_ngram.append((n, value))
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""", block="block", language="python"),
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Details(
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Summary("Sample documents filtered by the fraction of characters in the most common n-grams (n=2,3,4)"),
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0,
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"Sample documents filtered by the fraction of characters in the most common n-grams (n=2,3,4)",
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),
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H3("3.1.3 Fraction of Characters in Duplicated N-grams (n=5,...,10)"),
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P("""
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) / max(ng_char_count, 1)
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attrs.fraction_of_characters_in_duplicate_ngrams.append((n, value))
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""", block="block", language="python"),
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),
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Details(
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Summary("Implementations from RedPajama-V2"),
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score = round(score, PRECISION)
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return [(0, len(document), score)]
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""", block="block", language="python"),
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),
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Details(
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if n_duplicates_char / len(text) > n_frac:
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return False, f"duplicated_n_grams"
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""", block="block", language="python"),
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P("""
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For the computation of fraction of characters in duplicate n-gram, Dolma uses the number of characters in all
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score = get_dup_ngram_frac(n, ngram_counts, text)
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attrs.fraction_of_characters_in_duplicate_ngrams.append((n, score))
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""", block="block", language="python"),
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Details(
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Summary("An example to show the difference between above implementations"),
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In our implementation, there are 17*6 characters in total with 10*6 characters that are duplicated after excluding the first occurence. This results in a fraction of 10/17.
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"""),
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),
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H5(
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"Sample Documents Filtered by the Fraction of Characters in Duplicated N-grams (n=5,...,10)"
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"Sample documents filtered by the fraction of characters in duplicated n-grams (n=5,...,10)",
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H3("3.2 Line-wise Heuristics"),
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P("""
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D_code("""
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ELLIPSIS_SYMBOLS = ("...", "…", "[...]", "[…]")
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""", block="block", language="python"),
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Details(
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Summary("Bullet Point Identification Implemetations"),
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"*", # * star
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)
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""", block="block", language="python"),
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"Sample documents that are filtered out by line-wise heuristics",
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H3("3.3 Statistics-based Heuristics"),
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text = unicodedata.normalize("NFD", text)
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return text
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""", block="block", language="python"),
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Details(
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non_symbol_words = [w for w in words if any(ch not in PUNCTUATION_SET for ch in w)]
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n_non_symbol_words_words = len(non_symbol_words)
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""", block="block", language="python"),
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P("""
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Both Dolma and RedPajama V2 split texts into words using white spaces and newline symbols. However,
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score = float(len(self.SENT_PATTERN.findall(document.raw_content)))
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return [(0, len(document), score)]
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""", block="block", language="python"),
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P("""
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However, we found that this approach can mistakenly interpret periods in URLs as sentence endings. To address this,
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...
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attrs.num_of_sentences = count_sentences(text)
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""", block="block", language="python"),
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H3("Symbol to Word Ratio"),
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word_count, 1
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)
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""", block="block", language="python"),
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),
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Details(
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Summary("Implementations from RedPajama-V2"),
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score = round(score, PRECISION)
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return [(0, len(document), score)]
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""", block="block", language="python"),
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Details(
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if self.max_symbol_word_ratio and (text.count("...") + text.count("…")) / n_words > self.max_symbol_word_ratio:
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return False, "gopher_too_many_ellipsis"
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""", block="block", language="python"),
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Details(
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Summary("TxT360 Implementation"),
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...
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attrs.symbol_to_word_ratio = sum(1 for word in words if symbol_pattern.search(word)) / word_count
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""", block="block", language="python"),
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H3("Fraction of Alphabetic Words"),
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1 for word in words if any(c.isalpha() for c in word)
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) / max(word_count, 1)
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""", block="block", language="python"),
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Details(
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Summary("Implementations from RedPajama-V2"),
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score = round(score, PRECISION)
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return [(0, len(document), score)]
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""", block="block", language="python"),
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),
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Details(
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Summary("Implementations from DataTrove"),
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):
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return False, "gopher_below_alpha_threshold"
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""", block="block", language="python"),
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P("""
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Both Dolma and DataTrove use `char.isalpha()` to detect whether a word contains alphabetic characters while
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"Sample documents that are filtered out by statistics-based heuristics",
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H3("3.4 Others"),
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P("""
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Details(
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Summary("Sample documents containing 'lorem ipsum'"),
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DV("data/lorem_ipsum.json", 0, "Sample documents containing 'lorem ipsum'"),
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H2("4. Deduplication"),
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P("""
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Summary("Text Extraction Examples"),
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DV2("data/sample_wet.json", "data/sample_warc.json", 3),
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style="""
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background-color: #F0F8FF; /* Light blue background */
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padding: 15px;
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# border: 1px solid #949494; /* Grey border */
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border-radius: 12px;
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""", #https://colors.muz.li/palette/d3d3d3/949494/d3d3d3/d3d3d3/949494
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),
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Details(
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Summary("Non-English Documents"),
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DV("data/sample_non_en.json", 3, "Sample documents that are classified as non-English"),
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style="""
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background-color: #FFC0CB; /* Light pink background */
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padding: 15px;
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border-radius: 12px;
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""",
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#DV("data/sample_non_en.json", 3, "Sample documents that are classified as non-English"),
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Details(
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Summary("English Documents Scoring Lower than 0.65"),
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DV("data/sample_en_low.json", 3, "Sample documents that are classified as English but with score less than 0.65"),
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style="""
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background-color: #EAFFF1; /* Light green background */
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padding: 15px;
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border-radius: 12px;
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""",
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),
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H3("1.3 URL Filtering"),
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Details(
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Summary("24 URL domains with more than 4k matches"),
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DVS(urls_high_matches, "24 URL domains with more than 4k matches"),
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style="""
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background-color: #FFC0CB; /* Light pink background */
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padding: 15px;
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border-radius: 12px;
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""",
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),
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P("""
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Details(
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Summary("6 url domains that are removed from the blocklist"),
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DVS(urls_false_positives, "6 url domains that are removed from the blocklist"),
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style="""
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background-color: #FFC0CB; /* Light pink background */
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padding: 15px;
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border-radius: 12px;
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""",
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),
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Details(
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"data/bad_url_doc.jsonl",
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3,
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"Sample documents whose urls are blocked by the refined url blocklist",
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),
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style="""
|
380 |
+
background-color: #FFC0CB; /* Light pink background */
|
381 |
+
padding: 15px;
|
382 |
+
border-radius: 12px;
|
383 |
+
""",
|
384 |
),
|
385 |
|
386 |
H3("1.3.2 Excluded High Quality Sources"),
|
|
|
394 |
non_web_urls,
|
395 |
"curated url domains that are excluded from our dataset",
|
396 |
),
|
397 |
+
style="""
|
398 |
+
background-color: #FFC0CB; /* Light pink background */
|
399 |
+
padding: 15px;
|
400 |
+
border-radius: 12px;
|
401 |
+
""",
|
402 |
),
|
403 |
|
404 |
Details(
|
405 |
Summary("Sample documents whose urls are in our curated url domain list"),
|
406 |
DV("data/sample_url_exclusion.json", 0, "Sample documents whose urls are in our curated url domain list"),
|
407 |
+
style="""
|
408 |
+
background-color: #EAFFF1; /* Light green background */
|
409 |
+
padding: 15px;
|
410 |
+
border-radius: 12px;
|
411 |
+
""",
|
412 |
),
|
413 |
|
414 |
|
|
|
437 |
0,
|
438 |
"Sample documents with lines that are removed by the rule of terminal punctuation",
|
439 |
),
|
440 |
+
style="""
|
441 |
+
background-color: #FFC0CB; /* Light pink background */
|
442 |
+
padding: 15px;
|
443 |
+
border-radius: 12px;
|
444 |
+
""",
|
445 |
),
|
446 |
|
447 |
|
|
|
463 |
0,
|
464 |
"Sample documents that are removed by original C4 javascript rule but are kept after our refinement",
|
465 |
),
|
466 |
+
style="""
|
467 |
+
background-color: #FFC0CB; /* Light pink background */
|
468 |
+
padding: 15px;
|
469 |
+
border-radius: 12px;
|
470 |
+
""",
|
471 |
),
|
472 |
H3("2.2 Other Rules from RefinedWeb"),
|
473 |
P("""
|
|
|
486 |
0,
|
487 |
"Sample documents with lines that are removed by the RefinedWeb rules",
|
488 |
),
|
489 |
+
style="""
|
490 |
+
background-color: #FFC0CB; /* Light pink background */
|
491 |
+
padding: 15px;
|
492 |
+
border-radius: 12px;
|
493 |
+
""",
|
494 |
),
|
495 |
H3("2.3 Toxic Lines"),
|
496 |
P("""
|
|
|
506 |
json.load(open("data/toxic_lines.json")),
|
507 |
"Sample documents with toxic lines",
|
508 |
),
|
509 |
+
style="""
|
510 |
+
background-color: #FFC0CB; /* Light pink background */
|
511 |
+
padding: 15px;
|
512 |
+
border-radius: 12px;
|
513 |
+
""",
|
514 |
),
|
515 |
|
516 |
H2("3. Document-Level Filtering"),
|
|
|
523 |
json.load(open("data/all_signals.json")),
|
524 |
"Overview of all the quality signals that are used for filtering",
|
525 |
),
|
526 |
+
style="""
|
527 |
+
background-color: #EAFFF1; /* Light green background */
|
528 |
+
padding: 15px;
|
529 |
+
border-radius: 12px;
|
530 |
+
""",
|
531 |
),
|
532 |
P("""Similar to previous sections, we will present sample documents filtered out by the given quality signals.
|
533 |
Most quality signals were initially introduced by Gopher [2] and subsequently adopted by later
|
|
|
566 |
len(line) * count for line, count in line_counts.items() if count > 1
|
567 |
) / max(character_count, 1)
|
568 |
""", block="block", language="python"),
|
569 |
+
style="""
|
570 |
+
background-color: #FFFAEA; /* Light yellow background */
|
571 |
+
padding: 15px;
|
572 |
+
border-radius: 12px;
|
573 |
+
""",
|
574 |
),
|
575 |
Details(
|
576 |
Summary("Implementations from DataTrove"),
|
|
|
605 |
if self.dup_line_char_frac and char_duplicates / len(text) > self.dup_line_char_frac:
|
606 |
return False, "dup_line_char_frac"
|
607 |
""", block="block", language="python"),
|
608 |
+
style="""
|
609 |
+
background-color: #FFFAEA; /* Light yellow background */
|
610 |
+
padding: 15px;
|
611 |
+
border-radius: 12px;
|
612 |
+
""",
|
613 |
),
|
614 |
P("""
|
615 |
After evaluating the implementations of Dolma and DataTrove (note: RedPajama V2 does not implement these two quality
|
|
|
651 |
sum(sum(len(w) for w in line.split()) * (count - 1) for line, count in
|
652 |
line_counts.items() if count > 1) / character_count
|
653 |
""", block="block", language="python"),
|
654 |
+
style="""
|
655 |
+
background-color: #EAFFF1; /* Light green background */
|
656 |
+
padding: 15px;
|
657 |
+
border-radius: 12px;
|
658 |
+
""",
|
659 |
),
|
660 |
Details(
|
661 |
Summary("Sample documents filtered by excessive line repetitions / characters in repeated lines"),
|
|
|
664 |
0,
|
665 |
"Sample documents filtered by excessive line repetitions / characters in repeated lines",
|
666 |
),
|
667 |
+
style="""
|
668 |
+
background-color: #EAFFF1; /* Light green background */
|
669 |
+
padding: 15px;
|
670 |
+
border-radius: 12px;
|
671 |
+
""",
|
672 |
),
|
673 |
H3("3.1.2 Fraction of Characters in the Most Common N-grams (n=2,3,4)"),
|
674 |
P("""
|
|
|
692 |
value = count * sum(len(w) for w in most_common_ngram) / max(character_count, 1)
|
693 |
attrs.fraction_of_characters_in_most_common_ngram.append((n, value))
|
694 |
""", block="block", language="python"),
|
695 |
+
style="""
|
696 |
+
background-color: #FFFAEA; /* Light yellow background */
|
697 |
+
padding: 15px;
|
698 |
+
border-radius: 12px;
|
699 |
+
""",
|
700 |
),
|
701 |
Details(
|
702 |
Summary("Implementations from RedPajama-V2"),
|
|
|
735 |
score = round(score, PRECISION)
|
736 |
return [(0, len(document), score)]
|
737 |
""", block="block", language="python"),
|
738 |
+
style="""
|
739 |
+
background-color: #FFFAEA; /* Light yellow background */
|
740 |
+
padding: 15px;
|
741 |
+
border-radius: 12px;
|
742 |
+
""",
|
743 |
),
|
744 |
|
745 |
Details(
|
|
|
763 |
if top_char_length / len(text) > n_frac:
|
764 |
return False, f"top_n_gram"
|
765 |
""", block="block", language="python"),
|
766 |
+
style="""
|
767 |
+
background-color: #FFFAEA; /* Light yellow background */
|
768 |
+
padding: 15px;
|
769 |
+
border-radius: 12px;
|
770 |
+
""",
|
771 |
),
|
772 |
P("""
|
773 |
There are almost no contradictions between each implementations of fractions of characters in the most common
|
|
|
795 |
value = count * sum(len(w) for w in most_common_ngram) / character_count
|
796 |
attrs.fraction_of_characters_in_most_common_ngram.append((n, value))
|
797 |
""", block="block", language="python"),
|
798 |
+
style="""
|
799 |
+
background-color: #EAFFF1; /* Light green background */
|
800 |
+
padding: 15px;
|
801 |
+
border-radius: 12px;
|
802 |
+
""",
|
803 |
),
|
804 |
Details(
|
805 |
Summary("Sample documents filtered by the fraction of characters in the most common n-grams (n=2,3,4)"),
|
|
|
808 |
0,
|
809 |
"Sample documents filtered by the fraction of characters in the most common n-grams (n=2,3,4)",
|
810 |
),
|
811 |
+
style="""
|
812 |
+
background-color: #EAFFF1; /* Light green background */
|
813 |
+
padding: 15px;
|
814 |
+
border-radius: 12px;
|
815 |
+
""",
|
816 |
),
|
817 |
H3("3.1.3 Fraction of Characters in Duplicated N-grams (n=5,...,10)"),
|
818 |
P("""
|
|
|
839 |
) / max(ng_char_count, 1)
|
840 |
attrs.fraction_of_characters_in_duplicate_ngrams.append((n, value))
|
841 |
""", block="block", language="python"),
|
842 |
+
style="""
|
843 |
+
background-color: #FFFAEA; /* Light yellow background */
|
844 |
+
padding: 15px;
|
845 |
+
border-radius: 12px;
|
846 |
+
""",
|
847 |
),
|
848 |
Details(
|
849 |
Summary("Implementations from RedPajama-V2"),
|
|
|
897 |
score = round(score, PRECISION)
|
898 |
return [(0, len(document), score)]
|
899 |
""", block="block", language="python"),
|
900 |
+
style="""
|
901 |
+
background-color: #FFFAEA; /* Light yellow background */
|
902 |
+
padding: 15px;
|
903 |
+
border-radius: 12px;
|
904 |
+
""",
|
905 |
),
|
906 |
|
907 |
Details(
|
|
|
927 |
if n_duplicates_char / len(text) > n_frac:
|
928 |
return False, f"duplicated_n_grams"
|
929 |
""", block="block", language="python"),
|
930 |
+
style="""
|
931 |
+
background-color: #FFFAEA; /* Light yellow background */
|
932 |
+
padding: 15px;
|
933 |
+
border-radius: 12px;
|
934 |
+
""",
|
935 |
),
|
936 |
P("""
|
937 |
For the computation of fraction of characters in duplicate n-gram, Dolma uses the number of characters in all
|
|
|
985 |
score = get_dup_ngram_frac(n, ngram_counts, text)
|
986 |
attrs.fraction_of_characters_in_duplicate_ngrams.append((n, score))
|
987 |
""", block="block", language="python"),
|
988 |
+
style="""
|
989 |
+
background-color: #EAFFF1; /* Light green background */
|
990 |
+
padding: 15px;
|
991 |
+
border-radius: 12px;
|
992 |
+
""",
|
993 |
),
|
994 |
Details(
|
995 |
Summary("An example to show the difference between above implementations"),
|
|
|
1004 |
|
1005 |
In our implementation, there are 17*6 characters in total with 10*6 characters that are duplicated after excluding the first occurence. This results in a fraction of 10/17.
|
1006 |
"""),
|
1007 |
+
style="""
|
1008 |
+
background-color: #EAFFF1; /* Light green background */
|
1009 |
+
padding: 15px;
|
1010 |
+
border-radius: 12px;
|
1011 |
+
""",
|
1012 |
),
|
1013 |
H5(
|
1014 |
"Sample Documents Filtered by the Fraction of Characters in Duplicated N-grams (n=5,...,10)"
|
|
|
1020 |
0,
|
1021 |
"Sample documents filtered by the fraction of characters in duplicated n-grams (n=5,...,10)",
|
1022 |
),
|
1023 |
+
style="""
|
1024 |
+
background-color: #EAFFF1; /* Light green background */
|
1025 |
+
padding: 15px;
|
1026 |
+
border-radius: 12px;
|
1027 |
+
""",
|
1028 |
),
|
1029 |
H3("3.2 Line-wise Heuristics"),
|
1030 |
P("""
|
|
|
1051 |
D_code("""
|
1052 |
ELLIPSIS_SYMBOLS = ("...", "…", "[...]", "[…]")
|
1053 |
""", block="block", language="python"),
|
1054 |
+
style="""
|
1055 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1056 |
+
padding: 15px;
|
1057 |
+
border-radius: 12px;
|
1058 |
+
""",
|
1059 |
),
|
1060 |
Details(
|
1061 |
Summary("Bullet Point Identification Implemetations"),
|
|
|
1100 |
"*", # * star
|
1101 |
)
|
1102 |
""", block="block", language="python"),
|
1103 |
+
style="""
|
1104 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1105 |
+
padding: 15px;
|
1106 |
+
border-radius: 12px;
|
1107 |
+
""",
|
1108 |
),
|
1109 |
|
1110 |
|
|
|
1115 |
0,
|
1116 |
"Sample documents that are filtered out by line-wise heuristics",
|
1117 |
),
|
1118 |
+
style="""
|
1119 |
+
background-color: #EAFFF1; /* Light green background */
|
1120 |
+
padding: 15px;
|
1121 |
+
border-radius: 12px;
|
1122 |
+
""",
|
1123 |
),
|
1124 |
|
1125 |
H3("3.3 Statistics-based Heuristics"),
|
|
|
1180 |
text = unicodedata.normalize("NFD", text)
|
1181 |
return text
|
1182 |
""", block="block", language="python"),
|
1183 |
+
style="""
|
1184 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1185 |
+
padding: 15px;
|
1186 |
+
border-radius: 12px;
|
1187 |
+
""",
|
1188 |
),
|
1189 |
|
1190 |
Details(
|
|
|
1196 |
non_symbol_words = [w for w in words if any(ch not in PUNCTUATION_SET for ch in w)]
|
1197 |
n_non_symbol_words_words = len(non_symbol_words)
|
1198 |
""", block="block", language="python"),
|
1199 |
+
style="""
|
1200 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1201 |
+
padding: 15px;
|
1202 |
+
border-radius: 12px;
|
1203 |
+
""",
|
1204 |
),
|
1205 |
P("""
|
1206 |
Both Dolma and RedPajama V2 split texts into words using white spaces and newline symbols. However,
|
|
|
1245 |
score = float(len(self.SENT_PATTERN.findall(document.raw_content)))
|
1246 |
return [(0, len(document), score)]
|
1247 |
""", block="block", language="python"),
|
1248 |
+
style="""
|
1249 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1250 |
+
padding: 15px;
|
1251 |
+
border-radius: 12px;
|
1252 |
+
""",
|
1253 |
),
|
1254 |
P("""
|
1255 |
However, we found that this approach can mistakenly interpret periods in URLs as sentence endings. To address this,
|
|
|
1266 |
...
|
1267 |
attrs.num_of_sentences = count_sentences(text)
|
1268 |
""", block="block", language="python"),
|
1269 |
+
style="""
|
1270 |
+
background-color: #EAFFF1; /* Light green background */
|
1271 |
+
padding: 15px;
|
1272 |
+
border-radius: 12px;
|
1273 |
+
""",
|
1274 |
),
|
1275 |
|
1276 |
H3("Symbol to Word Ratio"),
|
|
|
1287 |
word_count, 1
|
1288 |
)
|
1289 |
""", block="block", language="python"),
|
1290 |
+
style="""
|
1291 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1292 |
+
padding: 15px;
|
1293 |
+
border-radius: 12px;
|
1294 |
+
""",
|
1295 |
),
|
1296 |
Details(
|
1297 |
Summary("Implementations from RedPajama-V2"),
|
|
|
1318 |
score = round(score, PRECISION)
|
1319 |
return [(0, len(document), score)]
|
1320 |
""", block="block", language="python"),
|
1321 |
+
style="""
|
1322 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1323 |
+
padding: 15px;
|
1324 |
+
border-radius: 12px;
|
1325 |
+
""",
|
1326 |
),
|
1327 |
|
1328 |
Details(
|
|
|
1333 |
if self.max_symbol_word_ratio and (text.count("...") + text.count("…")) / n_words > self.max_symbol_word_ratio:
|
1334 |
return False, "gopher_too_many_ellipsis"
|
1335 |
""", block="block", language="python"),
|
1336 |
+
style="""
|
1337 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1338 |
+
padding: 15px;
|
1339 |
+
border-radius: 12px;
|
1340 |
+
""",
|
1341 |
),
|
1342 |
Details(
|
1343 |
Summary("TxT360 Implementation"),
|
|
|
1348 |
...
|
1349 |
attrs.symbol_to_word_ratio = sum(1 for word in words if symbol_pattern.search(word)) / word_count
|
1350 |
""", block="block", language="python"),
|
1351 |
+
style="""
|
1352 |
+
background-color: #EAFFF1; /* Light green background */
|
1353 |
+
padding: 15px;
|
1354 |
+
border-radius: 12px;
|
1355 |
+
""",
|
1356 |
),
|
1357 |
|
1358 |
H3("Fraction of Alphabetic Words"),
|
|
|
1363 |
1 for word in words if any(c.isalpha() for c in word)
|
1364 |
) / max(word_count, 1)
|
1365 |
""", block="block", language="python"),
|
1366 |
+
style="""
|
1367 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1368 |
+
padding: 15px;
|
1369 |
+
border-radius: 12px;
|
1370 |
+
""",
|
1371 |
),
|
1372 |
Details(
|
1373 |
Summary("Implementations from RedPajama-V2"),
|
|
|
1392 |
score = round(score, PRECISION)
|
1393 |
return [(0, len(document), score)]
|
1394 |
""", block="block", language="python"),
|
1395 |
+
style="""
|
1396 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1397 |
+
padding: 15px;
|
1398 |
+
border-radius: 12px;
|
1399 |
+
""",
|
1400 |
),
|
1401 |
Details(
|
1402 |
Summary("Implementations from DataTrove"),
|
|
|
1408 |
):
|
1409 |
return False, "gopher_below_alpha_threshold"
|
1410 |
""", block="block", language="python"),
|
1411 |
+
style="""
|
1412 |
+
background-color: #FFFAEA; /* Light yellow background */
|
1413 |
+
padding: 15px;
|
1414 |
+
border-radius: 12px;
|
1415 |
+
""",
|
1416 |
),
|
1417 |
P("""
|
1418 |
Both Dolma and DataTrove use `char.isalpha()` to detect whether a word contains alphabetic characters while
|
|
|
1439 |
0,
|
1440 |
"Sample documents that are filtered out by statistics-based heuristics",
|
1441 |
),
|
1442 |
+
style="""
|
1443 |
+
background-color: #EAFFF1; /* Light green background */
|
1444 |
+
padding: 15px;
|
1445 |
+
border-radius: 12px;
|
1446 |
+
""",
|
1447 |
),
|
1448 |
H3("3.4 Others"),
|
1449 |
P("""
|
|
|
1454 |
Details(
|
1455 |
Summary("Sample documents containing 'lorem ipsum'"),
|
1456 |
DV("data/lorem_ipsum.json", 0, "Sample documents containing 'lorem ipsum'"),
|
1457 |
+
style="""
|
1458 |
+
background-color: #FFC0CB; /* Light pink background */
|
1459 |
+
padding: 15px;
|
1460 |
+
border-radius: 12px;
|
1461 |
+
""",
|
1462 |
),
|
1463 |
H2("4. Deduplication"),
|
1464 |
P("""
|