Duplicate from cambridge-climb/BabyLM

Co-authored-by: Richard Diehl Martinez <rdiehlmartinez@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
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+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
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+original_tagged/test/qed.txt filter=lfs diff=lfs merge=lfs -text
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+*.txt filter=lfs diff=lfs merge=lfs -text

BabyLM.py

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+import datasets
+
+from typing import List
+
+_DESCRIPTION = """\
+Dataset for the shared baby language modeling task.
+The goal is to train a language model from scratch on this data which represents
+roughly the amount of text and speech data a young child observes.  
+"""
+
+_HOMEPAGE = "https://babylm.github.io"
+
+filenames = [
+    "aochildes.txt", 
+    "bnc_spoken.txt",
+    "cbt.txt",
+    "children_stories.txt",
+    "gutenberg.txt",
+    "open_subtitles.txt",
+    "qed.txt", 
+    "simple_wikipedia.txt",
+    "switchboard.txt",
+    "wikipedia.txt"
+]
+class BabyLM(datasets.GeneratorBasedBuilder):
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="original_strict_small",
+            description="Original dataset, 10M words, no POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="strict_small",
+            description="Cleaned version of the dataset, 10M words, unsupervised POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="original_strict",
+            description="Original dataset, 100M words, no POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="strict",
+            description="Cleaned version of the dataset, 100M words, unsupervised POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="original_strict_small_gold",
+            description="Original dataset, 10M words, gold POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="strict_small_gold",
+            description="Cleaned version of the dataset, 10M words, gold POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="original_strict_gold",
+            description="Original dataset, 100M words, gold POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="strict_gold",
+            description="Cleaned version of the dataset, 100M words, gold POS tags",
+            version="1.0.0",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "strict_small"
+
+    def _info(self):
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "tagged_text": datasets.Value("string"),
+                    "filename": datasets.Value("string"),
+                }
+            )
+            return datasets.DatasetInfo(
+                # This is the description that will appear on the datasets page.
+                description=_DESCRIPTION,
+                features=features,  # Here we define them above because they are different between the two configurations
+                homepage=_HOMEPAGE,
+            )
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """ 
+        Returns data for different splits 
+        """
+
+        if "strict_small" in self.config.name:
+            train_data_dir = "10M"
+        else: 
+            train_data_dir = "100M"
+
+        folder = 'original_tagged' if 'original' in self.config.name else 'clean_tagged'
+        folder = folder + '_gold' if 'gold' in self.config.name else folder
+        
+        urls_to_download = {
+            "train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames],
+            "dev": [f"{folder}/dev/{fn}" for fn in filenames],
+            "test": [f"{folder}/test/{fn}" for fn in filenames]
+        } 
+
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                    "filepaths": downloaded_files["train"]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "split": "dev",
+                    "filepaths": downloaded_files["dev"]}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test",
+                    "filepaths": downloaded_files["test"]
+                }
+            ),
+        ]
+
+     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, split, filepaths):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        # the filepaths should be a list of filepaths 
+        if isinstance(filepaths, str):
+            filepaths = [filepaths]
+        
+        global_idx = 0 
+
+        for filepath in filepaths:
+            with open(filepath, encoding="utf-8") as f:
+                is_tags = False
+                text = ""
+                filename = ""
+                # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching)
+                for row in f:
+                    if filename == "":
+                        filename = row.strip()
+                        continue
+                    if is_tags:
+                        yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename}
+                        global_idx += 1 
+                        is_tags = False
+                    else:
+                        text = row
+                        is_tags = True

README.md

@@ -0,0 +1,68 @@
+---
+language:
+- en
+tags:
+- language modeling
+- cognitive modeling
+pretty_name: Baby Language Modeling Dataset
+size_categories:
+- 10M<n<100M
+---
+
+# BabyLM Dataset
+This download includes LM Pretraining data for the 2023 CoNLL/CMCL shared task, [The BabyLM Challenge](https://babylm.github.io/). The (unzipped) data is not large, only ~700MB.
+
+
+Note that there is also a multi-lingual version of this dataset, that is availabled under the `multi-lingual` branch of the dataset repository.
+
+## Contents of this download
+- `10M`: 10M-word training set for the *strict-small* track.
+- `dev`: Development set for both tracks (10M words)
+- `test`: Test set for both tracks (10M words)
+
+Each directory above contains a single `.txt` file from each of the 10 domains listed below.
+
+## Composition of the data
+All datasets are sampled from a mixture of 10 data domains, shown below, along with their respective weights in the distributed dataset.
+
+| Source | Weight | Domain | Citation | Website | License |
+| --- | --- | --- | --- | --- | --- |
+| OpenSubtitles | 30% | Dialogue, Scripted | Lison & Tiedermann (2016) | [link](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | Open source |
+| Simple English Wikipedia | 15% | Nonfiction | -- | [link](https://dumps.wikimedia.org/simplewiki/20221201/) | [link](https://dumps.wikimedia.org/legal.html) |
+| BNC | 10% | Dialogue | BNC Consortium (2007) | [link](http://www.natcorp.ox.ac.uk/) | [link](http://www.natcorp.ox.ac.uk/docs/licence.html) <sup>1</sup> |
+| Project Gutenberg | 10% | Fiction, Nonfiction | Gerlach & Font-Clos (2020) | [link](https://github.com/pgcorpus/gutenberg) | [link](https://www.gutenberg.org/policy/license.html) |
+| QED | 10% | Dialogue, Education | Abdelali et al. (2014) | [link](https://opus.nlpl.eu/QED.php) | [link](https://opus.nlpl.eu/QED.php) |
+| Wikipedia | 10% | Nonfiction | -- | [link](https://dumps.wikimedia.org/enwiki/20221220/) | [link](https://dumps.wikimedia.org/legal.html) |
+| Children's Book Test | 6% | Fiction, Child-Directed | Hill et al. (2016) | [link](https://research.facebook.com/downloads/babi/) | Public domain |
+| CHILDES | 4% | Dialogue, Child-Directed | MacWhinney (2000) | | [link](https://talkbank.org/share/rules.html) |
+| Children's Stories | 4% | Fiction, Child-Directed | -- | [link](https://www.kaggle.com/datasets/edenbd/children-stories-text-corpus) | Public domain |
+| Switchboard | 1% | Dialogue | Godfrey et al. (1992), Stolcke et al., (2000) | [link](http://compprag.christopherpotts.net/swda.html) | [link](http://compprag.christopherpotts.net/swda.html) |
+
+<sup>1</sup> Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
+
+
+## Data preprocessing
+
+Data was minimally preprocessed to conform to a plain text format. We did not tokenize the data. Documents are not necessarily complete are newline separated.
+
+For documentation of the preprocessing pipeline, consult the following repo: https://github.com/babylm/babylm_data_preprocessing
+
+
+## References
+Abdelali, A., Guzman, F., Sajjad, H., & Vogel, S. (2014). The AMARA Corpus: Building parallel language resources for the educational domain. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). 1856-1862.
+
+BNC Consortium. (2007). The British National Corpus, XML Edition. Oxford Text Archive, http://hdl.handle.net/20.500.12024/2554.
+
+Gerlach, M., & Font-Clos, F. (2020). A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics. Entropy, 22(1), 126.
+
+Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992). SWITCHBOARD: Telephone speech corpus for research and development. In Acoustics, Speech, and Signal Processing, IEEE International Conference on (Vol. 1, pp. 517-520). IEEE Computer Society.
+
+Hill, F., Bordes, A., Chopra, S., Weston, J. (2016). The Goldilocks principle: Reading children’s books with explicit memory representations. In Proceedings of the 4th International Conference on Learning Representations (ICLR 2016).
+
+Lison, P. & Tiedemann, J. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016).
+
+MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk. Third Edition. Mahwah, NJ: Lawrence Erlbaum Associates.
+
+Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., Taylor, P., Martin, R., Van Ess-Dykema, C., & Meteer, M. (2000). Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics, 26(3), 339-373.
+
+Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012).

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clean_data.py

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+""" Script used to clean the data. """
+
+import os
+import re
+from nltk import tokenize
+
+def clean_aochildes(lines):
+    """ For aochildes, we remove the space between the punctuation mark and the final word and join together every 5 lines """
+    new_lines = []
+    joined = []
+    for i, line in enumerate(lines):
+        new_line = line[:-3] + line[-2:]
+        joined.append(new_line.strip())
+        if i % 5 == 0:
+            new_lines.append(" ".join(joined) + "\n")
+            joined = []
+    return new_lines
+
+def clean_bnc_spoken(lines):
+    """ For bnc_spoken, we lowercase """
+    new_lines = []
+    for line in lines:
+        new_line = line.lower()
+        if new_line != '\n':
+            new_lines.append(new_line)
+    return new_lines
+
+def clean_cbt(lines):
+    """ For cbt, we lowercase and normalise punctuation """
+    punctuation = ['.', ',', '?', '!', ':', ';', '(', ')', '[', ']', '{', '}', '"', "'", '“', '”', '—', '–']
+    new_lines = []
+    for line in lines:
+        new_line = line.lower()
+        new_line = new_line.replace(": ' ", ":  \"")
+        new_line = new_line.replace("''", "\"")
+        new_line = new_line.replace(" '\n", "\"\n")
+        new_line = new_line.replace(" ' ", "\" ")
+        new_line = new_line.replace(" `` ", "  \"")
+        new_line = new_line.replace("` ", " \"")
+        new_line = new_line.replace("`", "\"")
+        new_line = new_line.replace("’", "\"")
+        for punct in punctuation:
+            new_line = new_line.replace(f" {punct}", punct)
+        new_lines.append(new_line)
+    return new_lines
+
+def clean_children_stories(lines):
+    """ For children_stories, we lowercase """
+    new_lines = []
+    for line in lines:
+        new_line = line.lower().strip()
+        if new_line != '':
+            new_lines.append(new_line + "\n")
+    return new_lines
+
+def clean_gutenberg(lines):
+    """ For gutenberg, we lowercase, remove italics and group lines into paragraphs. We also remove any lines containing '*' or 'p.' """
+    # Get paragraphs
+    paragraphs = []
+    paragraph = ""
+    for line in lines:
+        # Remove italics
+        tmp_line = line.lower().strip().replace('_','')
+        if tmp_line == "" and paragraph != "":
+            if len(paragraph.split()) > 2 and not paragraph.split()[-1][-1].isnumeric(): # Remove paragraphs with less than 3 words and those that end in a number (probably part of a bibliography)
+                paragraphs.append(paragraph[:-1] + '\n')
+            paragraph = ""
+        else:
+            paragraph += tmp_line + " "
+    
+    # Bad characters - gutenberg has a lot of figures, footnotes, chapter names etc that we want to remove
+    bad_chars = ['*', 'p.', '=', '|', '[', ']', '       ', '    ', 'v.']
+    new_lines = [p.strip()+'\n' for p in paragraphs if not any([c in p for c in bad_chars]) and p != '' and p != '\n' and p[0] != '(']
+    return new_lines
+
+def clean_open_subtitles(lines):
+    """ For open_subtitles, we lowercase, remove subtitle dashes and fix the lowercase 'l' problem. We also join every 5 lines. """
+    punctuation = ['.', ',', '?', '!', ':', ';', '(', ')', '[', ']', '{', '}', '"', "'", '“', '”', '—', '–', ' ', '\n']
+    new_lines = []
+    joined = []
+    count = 0
+    for line in lines:
+        new_line = line.lower()
+        # Skip music lines
+        if '♪' in new_line or '[' in new_line or ']' in new_line or '‎' in new_line:
+            continue
+        if new_line[0:2] in ["- ", "– ", "— "]:
+            new_line = new_line[2:]
+        if new_line[0] in ["-", "–", "—"]:
+            new_line = new_line[1:]
+        new_line = ' ' + new_line
+        for punct in punctuation:
+            new_line = new_line.replace(f" l{punct}", f" i{punct}")
+            new_line = new_line.replace(f" lm{punct}", f" im{punct}")
+            new_line = new_line.replace(f" lf{punct}", f" if{punct}")
+        new_line = new_line.replace(' lc', ' ic')
+        new_line = new_line.replace(' ld', ' id')
+        new_line = new_line.replace(' lj', ' i j')
+        new_line = new_line.replace(' ln', ' in')
+        new_line = new_line.replace(' lp', ' ip')
+        new_line = new_line.replace(' lr', ' ir')
+        new_line = new_line.replace(' ls', ' is')
+        new_line = new_line.replace(' isd', ' lsd')
+        new_line = new_line.replace(' lt', ' it')
+        new_line = new_line.replace(' lt', ' it')
+        new_line = new_line.replace(' lv', ' iv')
+        if new_line.strip() != '':
+            joined.append(new_line.strip())
+            count += 1
+            if count % 5 == 0:
+                new_lines.append(" ".join(joined) + '\n')
+                joined = []
+    return new_lines
+
+def clean_qed(lines):
+    """ For qed, we lowercase and normalise punctuation, remove words contained in parentheses,
+    remove lines that are just character's names and fix the lowercase 'l' problem. We also join every 5 lines. """
+
+    new_lines = []
+    count = 0
+    joined = []
+    for line in lines:
+        # Before lowercasing, check if the words in the line are uppercase containing lowercase 'l' instead of 'I' and fix accordingly
+        words = line.split()
+        for i, word in enumerate(words):
+            if word.replace('l','I').isupper() and 'l' in word and word != 'I\'ll':
+                words[i] = word.replace('l', 'I')
+        new_line = ' '.join(words).lower()
+        new_line = new_line.replace(' lc', ' ic')
+        new_line = new_line.replace(' ld', ' id')
+        new_line = new_line.replace(' lj', ' i j')
+        new_line = new_line.replace(' ln', ' in')
+        new_line = new_line.replace(' lp', ' ip')
+        new_line = new_line.replace(' lr', ' ir')
+        new_line = new_line.replace(' ls', ' is')
+        new_line = new_line.replace(' isd', ' lsd')
+        new_line = new_line.replace(' lt', ' it')
+        new_line = new_line.replace(' lt', ' it')
+        new_line = new_line.replace(' lv', ' iv')
+        new_line = new_line.replace('>', '')
+        new_line = new_line.replace('<i', '')
+        new_line = new_line.replace('</i', '')
+        new_line = new_line.replace('>i', '')
+        new_line = new_line.replace('>/i', '')
+        new_line = new_line.replace('&gt', '')
+        new_line = new_line.replace('&lt', '')
+        new_line = new_line.replace('&', '')
+
+        # Skip lines that are just character names, e.g. "AMY GOODMAN:"
+        if len(new_line.strip()) < 1 or (len(words) <= 3 and new_line.strip()[-1] == ':'):
+            continue
+
+        # Remove subtitle dashes
+        if new_line[0:2] == "- ":
+            new_line = new_line[2:]
+        if new_line[0] == "-":
+            new_line = new_line[1:]
+
+        # Remove substrings contained within circular or square parantheses (screen descriptions)
+        pattern = r'\([^)]*\)'
+        new_line = re.sub(pattern, '', new_line)
+        pattern = r'\[[^)]*\]'
+        new_line = re.sub(pattern, '', new_line)
+        new_line = new_line.replace('"', '\'')
+
+        # Remove strange characters
+        new_line = new_line.replace('#','')
+        new_line = new_line.replace('*','')
+
+        new_line = new_line.strip()
+        if new_line != "":
+            joined.append(new_line)
+            count += 1
+            if count % 5 == 0:
+                new_lines.append(" ".join(joined) + '\n')
+                joined = []
+    return new_lines
+
+def clean_simple_wikipedia(lines):
+    """ For simple_wikipedia, we lowercase, remove empty lines and article names."""
+    new_lines = []
+    next_line_is_article_name = False
+    for line in lines:
+        if next_line_is_article_name:
+            next_line_is_article_name = False
+            continue
+        if line.strip() == "":
+            next_line_is_article_name = True
+            continue
+        if len(line.split()) > 2:
+            new_lines.append(line.lower())
+    return new_lines
+
+def clean_switchboard(lines):
+    """ For switchboard, we lowercase and join every 5 lines. """
+    new_lines = []
+    count = 0
+    joined = []
+    for line in lines:
+        new_line = line.lower().strip()
+        joined.append(new_line)
+        count += 1
+        if count % 5 == 0:
+            new_lines.append(" ".join(joined) + '\n')
+            joined = []
+    return new_lines
+
+def clean_wikipedia(lines):
+    """ For wikipedia, we lowercase and remove empty lines and article names.
+     We also remove lines that seem to be figure names or table entries. """
+    new_lines = []
+    for line in lines:
+        new_line = line.strip()
+        words = new_line.split()
+        
+        # Remove empty lines and article names
+        if new_line == "":
+            continue
+        if new_line[0] == "=" and new_line[-1] == "=":
+            continue
+
+        # Filter out lines that seem to be figure names or table entries
+        all_numeric = True
+        all_uppercase = True
+        for word in words:
+            if not word.isnumeric():
+                all_numeric = False
+            if not word[0].isupper():
+                all_uppercase = False
+        if all_numeric or all_uppercase:
+            continue
+    
+        new_lines.append(new_line.lower().strip() + '\n')
+    return new_lines
+
+CLEAN_FUNCTIONS = {'aochildes' : clean_aochildes, 'bnc_spoken' : clean_bnc_spoken, 'cbt' : clean_cbt, 'children_stories' : clean_children_stories, 'gutenberg' : clean_gutenberg, 'open_subtitles' : clean_open_subtitles, 'qed' : clean_qed, 'simple_wikipedia' : clean_simple_wikipedia, 'switchboard' : clean_switchboard, 'wikipedia' : clean_wikipedia}
+FOLDERS = ['10M', '100M', 'dev', 'test']
+
+if __name__ == "__main__":
+
+    # Read all text files from directory "BabyLM"
+    all_files = []
+    for folder in FOLDERS:
+        for root, dirs, files in os.walk(f"original/{folder}"):
+            for file in files:
+                if file.endswith(".txt"):
+                    all_files.append(os.path.join(root, file))
+
+    for file in all_files:
+        print(file)
+        with open(file, 'r') as f:
+            lines = f.readlines()
+
+        # Get the corpus name
+        corpus_name = os.path.basename(file).split('.')[0]
+
+        # Clean the data
+        if CLEAN_FUNCTIONS[corpus_name] is not None:
+            lines = CLEAN_FUNCTIONS[corpus_name](lines)
+            # Replace multiple spaces with single space
+            lines = [re.sub(' +', ' ', line) for line in lines if line.strip() != '']
+
+        # Write the new file
+        new_file = file.replace('original', 'clean')
+        os.makedirs(os.path.dirname(new_file), exist_ok=True)
+        with open(new_file, 'w') as f:
+           # Save file name to file, so we can later recover the original file names
+           f.write(new_file.split('/')[-1] + '\n')
+           f.writelines(lines)

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