Create dataset_filtering.py

dataset_filtering.py

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+import polars as pl
+import numpy as np
+import re
+from datasets import load_dataset, Dataset, concatenate_datasets
+
+#region Preprocessing functions
+def remove_newlines(text):
+        return text.replace('\n', '')
+
+def remove_urls(text):
+    url_regex = r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
+    return re.sub(url_regex, '', text)
+
+def remove_html_tags(text):
+    html_regex = r'<[^>]+>'
+    return re.sub(html_regex, '', text)
+
+def remove_special_characters(text):
+    special_chars_regex = r'[^a-zA-Z0-9\s]'  
+    return re.sub(special_chars_regex, '', text)
+
+def remove_numbers(text):
+    return re.sub(r'\d+', '', text)
+
+def remove_extra_spaces(text):
+    return re.sub(r'\s+', ' ', text)
+
+def remove_twitter_mentions(text):
+    return re.sub(r'@([A-Za-z0-9_]+)', '', text)
+
+def remove_emoticons(text):
+    emoticon_regex = r'[\U0001F600-\U0001F64F\U0001F300-\U0001F5FF\U0001F680-\U0001F6FF\U0001F700-\U0001F77F\U0001F780-\U0001F7FF\U0001F800-\U0001F8FF\U0001F900-\U0001F9FF\U0001FA00-\U0001FA6F\U0001FA70-\U0001FAFF\U00002702-\U000027B0\U000024C2-\U0001F251\U0001F004\U0001F0CF\U0001F170-\U0001F251\U0001F600-\U0001F64F\U00002702-\U000027B0\U000024C2-\U0001F251\U0001F300-\U0001F5FF\U0001F680-\U0001F6FF\U0001F700-\U0001F773\U0001F780-\U0001F7D8\U0001F7E0-\U0001F7EB\U0001F7F0-\U0001F7FF\U0001F800-\U0001F80B\U0001F90D-\U0001F9FF\U0001FA70-\U0001FA74\U0001F600-\U0001F64F\U0001F90D-\U0001F971\U0001F973-\U0001F978\U0001F97A-\U0001F9CB\U0001F9CD-\U0001F9FF]+'
+    return re.sub(emoticon_regex, '', text)
+
+def normalize_case(text):
+    return text.lower()
+
+def remove_unnecessary_spaces(text):
+    text = text.strip()  
+    text = re.sub(r'\s+', ' ', text)
+    return text
+
+def remove_punctuation_and_brackets(text):
+    text = re.sub(r'[^\w\s\[\]]', '', text)
+    return text
+
+def remove_numbered_brackets(text):
+    text = re.sub(r'\[\d+\]', '', text)
+    return text
+
+def remove_initial_article(text):
+    words_to_remove = [
+    'a', 'an', 'the', 'some', 'many', 'much', 'few', 'little', 
+    'several', 'a few', 'a little', 'a lot of', 'lots of', 'plenty of', 
+    'this', 'that', 'these', 'those', 'its' ]
+    words = text.split()
+    if words and words[0].lower() in words_to_remove:
+        words.pop(0)
+    return ' '.join(words)
+    
+def preprocess_text(text):
+    text = remove_newlines(text)
+    text = remove_punctuation_and_brackets(text)
+    text = remove_special_characters(text)
+    text = remove_urls(text)
+    text = remove_html_tags(text)
+    text = remove_numbers(text)
+    text = remove_extra_spaces(text)
+    text = remove_twitter_mentions(text)
+    text = remove_emoticons(text)
+    text = normalize_case(text)
+    text = remove_unnecessary_spaces(text)
+    text = remove_numbered_brackets(text)
+    text=remove_initial_article(text)
+    return text
+#endregion
+
+def softmax(x):
+    r=np.exp(x - np.max(x))
+    return r/r.sum(axis=0)
+
+# Load the dataset
+dataset1 = load_dataset("Fizzarolli/wattpad", split="train")
+
+df2 = pl.read_parquet("wattpad_stories.parquet")
+df2 = df2.to_pandas()
+dataset2 = Dataset.from_pandas(df2)
+
+df3 = pl.read_parquet("wattpad_stories_2.parquet")
+df3 = df3.to_pandas()
+dataset3 = Dataset.from_pandas(df2)
+
+dataset = concatenate_datasets([dataset1, dataset2, dataset3]).shuffle(seed=42)
+
+print(dataset)
+print(dataset[0])
+
+# Language detection
+import fasttext
+
+model = fasttext.load_model("glotlid.bin") # https://huggingface.co/cis-lmu/glotlid
+def add_language_column(example):
+    preds = {}
+    pred = model.predict(example["title"], 3)
+    for i in range(len(pred[0])):
+        label = pred[0][i].replace("__label__", "")
+        label_liklihood = pred[1][i]
+        if label not in preds:
+            preds[label] = 0
+        preds[label] += label_liklihood
+    if not preds:
+        example["language"] = "unk_Unkn"
+        example["language_confidence"] = 0.0
+        return example
+    example["language"] = max(preds, key=preds.get)
+    example["language_confidence"] = np.max(softmax(list(preds.values())))
+    return example
+
+dataset = dataset.map(add_language_column)
+
+print(dataset)
+print(dataset[0])
+
+# NSFW detection
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("eliasalbouzidi/distilbert-nsfw-text-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("eliasalbouzidi/distilbert-nsfw-text-classifier", device_map="cuda")
+
+def add_nsfw_column(example):
+    nsfw_scores = []
+    for chapter_text in example["chapter_contents"]:
+        preprocessed_text = preprocess_text(chapter_text)
+        inputs = tokenizer(preprocessed_text, return_tensors="pt", padding=True, truncation=True, max_length=512).to("cuda")
+        outputs = model(**inputs).logits
+        probs = torch.softmax(outputs, dim=1).tolist()[0]
+        nsfw_scores.append(probs[1])
+    example["overall_nsfw_score"] = sum(nsfw_scores) / len(nsfw_scores)
+    example["chapter_nsfw_scores"] = nsfw_scores
+    return example
+
+dataset = dataset.map(add_nsfw_column)
+
+print(dataset)
+print(dataset[0])
+
+dataset.push_to_hub("Fizzarolli/wattpad2")