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import sys |
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import math |
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import re |
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import random |
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import json |
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from pathlib import Path |
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__FILE_COUNT__ = 60 |
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doc_regex = re.compile("<doc id=\"([^\"]+)_\\d+\">") |
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file_names = [] |
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file_pointers = {} |
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record_counter = {} |
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line_counter = 0 |
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sum_token_count = 0 |
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sum_token_sq = 0 |
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sum_char_count = 0 |
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sum_char_sq = 0 |
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source_dist = {} |
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dataset_names = { |
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"2109_0.txt": "oscar_2109", |
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"2109_1.txt": "oscar_2109", |
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"2109_2.txt": "oscar_2109", |
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"2109_3.txt": "oscar_2109", |
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"2109_4.txt": "oscar_2109", |
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"2109_5.txt": "oscar_2109", |
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"2109_6.txt": "oscar_2109", |
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"2109_7.txt": "oscar_2109", |
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"2109_8.txt": "oscar_2109", |
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"2109_9.txt": "oscar_2109", |
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"2201_0.txt": "oscar_2201", |
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"2201_1.txt": "oscar_2201", |
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"2201_2.txt": "oscar_2201", |
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"2201_3.txt": "oscar_2201", |
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"2201_4.txt": "oscar_2201", |
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"2201_5.txt": "oscar_2201", |
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"2201_6.txt": "oscar_2201", |
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"2201_7.txt": "oscar_2201", |
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"2301_0.txt": "oscar_2301", |
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"2301_10.txt": "oscar_2301", |
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"2301_11.txt": "oscar_2301", |
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"2301_1.txt": "oscar_2301", |
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"2301_2.txt": "oscar_2301", |
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"2301_3.txt": "oscar_2301", |
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"2301_4.txt": "oscar_2301", |
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"2301_5.txt": "oscar_2301", |
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"2301_6.txt": "oscar_2301", |
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"2301_7.txt": "oscar_2301", |
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"2301_8.txt": "oscar_2301", |
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"2301_9.txt": "oscar_2301", |
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"commoncrawl_fa_merged_aa.txt": "cc", |
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"commoncrawl_fa_merged_ab.txt": "cc", |
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"commoncrawl_fa_merged_ac.txt": "cc", |
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"commoncrawl_fa_merged_ad.txt": "cc", |
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"commoncrawl_fa_merged_ae.txt": "cc", |
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"commoncrawl_fa_merged_af.txt": "cc", |
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"commoncrawl_fa_merged_ag.txt": "cc", |
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"commoncrawl_fa_merged_ah.txt": "cc", |
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"commoncrawl_fa_merged_ai.txt": "cc", |
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"commoncrawl_fa_merged_aj.txt": "cc", |
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"fas-ir_web-public_2019_100K-sentences.txt": "web-2019_100K", |
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"fas-ir_web-public_2019_10K-sentences.txt": "web-2019_10K", |
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"fas-ir_web-public_2019_1M-sentences.txt": "web-2019_1M", |
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"fas-ir_web-public_2019_300K-sentences.txt": "web-2019_300K", |
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"fas-ir_web-public_2019_30K-sentences.txt": "web-2019_30K", |
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"fas_news_2019_100K-sentences.txt": "news_2019_100K", |
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"fas_news_2019_10K-sentences.txt": "news_2019_10K", |
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"fas_news_2019_300K-sentences.txt": "news_2019_300K", |
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"fas_news_2019_30K-sentences.txt": "news_2019_30K", |
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"fas_news_2020_100K-sentences.txt": "news_2020_100K", |
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"fas_news_2020_10K-sentences.txt": "news_2020_10K", |
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"fas_news_2020_300K-sentences.txt": "news_2020_300K", |
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"fas_news_2020_30K-sentences.txt": "news_2020_30K", |
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"fas_newscrawl_2011_100K-sentences.txt": "newscrawl_2011_100K", |
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"fas_newscrawl_2011_10K-sentences.txt": "newscrawl_2011_10K", |
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"fas_newscrawl_2011_1M-sentences.txt": "newscrawl_2011_1M", |
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"fas_newscrawl_2011_300K-sentences.txt": "newscrawl_2011_300K", |
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"fas_newscrawl_2011_30K-sentences.txt": "newscrawl_2011_30K", |
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"fas_newscrawl_2015_100K-sentences.txt": "newscrawl_2015_100K", |
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"fas_newscrawl_2015_10K-sentences.txt": "newscrawl_2015_10K", |
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"fas_newscrawl_2015_1M-sentences.txt": "newscrawl_2015_1M", |
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"fas_newscrawl_2015_300K-sentences.txt": "newscrawl_2015_300K", |
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"fas_newscrawl_2015_30K-sentences.txt": "newscrawl_2015_30K", |
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"fas_newscrawl_2016_100K-sentences.txt": "newscrawl_2016_100K", |
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"fas_newscrawl_2016_10K-sentences.txt": "newscrawl_2016_10K", |
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"fas_newscrawl_2016_1M-sentences.txt": "newscrawl_2016_1M", |
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"fas_newscrawl_2016_300K-sentences.txt": "newscrawl_2016_300K", |
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"fas_newscrawl_2016_30K-sentences.txt": "newscrawl_2016_30K", |
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"fas_newscrawl_2017_100K-sentences.txt": "newscrawl_2017_100K", |
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"fas_newscrawl_2017_10K-sentences.txt": "newscrawl_2017_10K", |
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"fas_newscrawl_2017_1M-sentences.txt": "newscrawl_2017_1M", |
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"fas_newscrawl_2017_300K-sentences.txt": "newscrawl_2017_300K", |
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"fas_newscrawl_2017_30K-sentences.txt": "newscrawl_2017_30K", |
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"fas_newscrawl_2019_100K-sentences.txt": "newscrawl_2019_100K", |
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"fas_newscrawl_2019_10K-sentences.txt": "newscrawl_2019_10K", |
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"fas_newscrawl_2019_1M-sentences.txt": "newscrawl_2019_1M", |
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"fas_newscrawl_2019_300K-sentences.txt": "newscrawl_2019_300K", |
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"fas_newscrawl_2019_30K-sentences.txt": "newscrawl_2019_30K", |
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"fas_wikipedia_2010_100K-sentences.txt": "wikipedia_2010_100K", |
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"fas_wikipedia_2010_10K-sentences.txt": "wikipedia_2010_10K", |
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"fas_wikipedia_2010_300K-sentences.txt": "wikipedia_2010_300K", |
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"fas_wikipedia_2010_30K-sentences.txt": "wikipedia_2010_30K", |
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"fas_wikipedia_2012_100K-sentences.txt": "wikipedia_2012_100K", |
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"fas_wikipedia_2012_10K-sentences.txt": "wikipedia_2012_10K", |
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"fas_wikipedia_2012_300K-sentences.txt": "wikipedia_2012_300K", |
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"fas_wikipedia_2012_30K-sentences.txt": "wikipedia_2012_30K", |
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"fas_wikipedia_2014_100K-sentences.txt": "wikipedia_2014_100K", |
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"fas_wikipedia_2014_10K-sentences.txt": "wikipedia_2014_10K", |
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"fas_wikipedia_2014_1M-sentences.txt": "wikipedia_2014_1M", |
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"fas_wikipedia_2014_300K-sentences.txt": "wikipedia_2014_300K", |
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"fas_wikipedia_2014_30K-sentences.txt": "wikipedia_2014_30K", |
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"poems_merged.txt": "poems", |
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"TEP_fa.txt": "tep", |
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"voa_persian_2003_2008_cleaned.txt": "voa", |
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"w2c_merged.txt": "w2c", |
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} |
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def stats(tokens): |
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global line_counter, sum_token_count, sum_token_sq, sum_char_count, sum_char_sq |
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line_counter = line_counter + 1 |
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sum_token_count = sum_token_count + len(tokens) |
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sum_token_sq = sum_token_sq + len(tokens) * len(tokens) |
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sum_char = sum([len(t) for t in tokens]) |
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sum_char_count = sum_char_count + sum_char |
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sum_char_sq = sum_char_sq + sum_char * sum_char |
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output_folder = sys.argv[1] |
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Path(output_folder).mkdir(parents=True, exist_ok=True) |
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for i in range(__FILE_COUNT__): |
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fn = f"jomleh_{i+1}.jsonl" |
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file_names.append(fn) |
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record_counter[fn] = 0 |
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seen = set() |
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tokens = [] |
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for token in sys.stdin: |
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token = token.strip() |
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if token.startswith("<doc"): |
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tokens = [] |
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doc_id = doc_regex.match(token).groups()[0] |
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ds_name = dataset_names[doc_id] if doc_id in dataset_names else doc_id |
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source_dist[ds_name] = source_dist.get(ds_name, 0) + 1 |
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continue |
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if token == "</doc>": |
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sentence = " ".join(tokens) |
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if len(tokens) >= 10: |
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stats(tokens) |
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jsonl = json.dumps({"source": ds_name, "text": sentence}, ensure_ascii=False) |
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fn = random.sample(file_names, 1)[0] |
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record_counter[fn] += 1 |
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elif sentence not in seen: |
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seen.add(sentence) |
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stats(tokens) |
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jsonl = json.dumps({"source": ds_name, "text": sentence}, ensure_ascii=False) |
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fn = random.sample(file_names, 1)[0] |
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record_counter[fn] += 1 |
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continue |
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tokens.append(token) |
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avg_tokens = sum_token_count / line_counter |
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stddev_tokens = math.sqrt((sum_token_sq / line_counter) - avg_tokens * avg_tokens) |
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avg_char = sum_char_count / sum_token_count |
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stddev_chars = math.sqrt((sum_char_sq / sum_token_count) - avg_char * avg_char) |
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results = { |
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"Number of records per each file": record_counter, |
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"Number of samples from each source": source_dist, |
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"Number of lines": line_counter, |
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"Total number of words": sum_token_count, |
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"Average number of tokens per line": avg_tokens, |
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"Standard deviation for the number of tokens per line": stddev_tokens, |
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"Average number of characters per token": avg_char, |
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"Standard deviation for the number of characters per token": stddev_chars, |
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} |
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print(json.dumps(results)) |
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