# 6. Specific WALS Feature Analysis (if standard format) # WALS usually includes 'Language', 'Genus', 'Area', and feature columns like '1A', '2A' wals_feature_cols = [col for col in df.columns if col[0].isdigit()] if wals_feature_cols: print(f"\n--- WALS FEATURES DETECTED ({len(wals_feature_cols)}) ---") print(f"Example features: {wals_feature_cols[:5]}") except zipfile.BadZipFile: print("Error: The file is not a valid zip file or is corrupted.") except Exception as e: print(f"An error occurred: {e}") Uncle Drew Vietsub
# Now this is ready for HuggingFace tokenizer # from transformers import RobertaTokenizer # tokenizer = RobertaTokenizer.from_pretrained('roberta-base') # train_encodings = tokenizer(train_texts, truncation=True, padding=True) If you can provide a sample of the file contents (the first few lines of the unzipped text), I can give you the exact code to parse the specific format contained within. 2016 Download Free — Bhouri
print(f"Loading dataset: {data_file}") # 4. Read the file into a Pandas DataFrame # Handling different delimiters if data_file.endswith('.tsv') or data_file.endswith('.txt'): df = pd.read_csv(z.open(data_file), sep='\t') elif data_file.endswith('.json'): df = pd.read_json(z.open(data_file)) else: df = pd.read_csv(z.open(data_file))