<jupyter_start><jupyter_text>Trimming down the size and save the toy and medium size datasets don't execute this unless you need to create them<jupyter_code>toy_glove_vectors = gloveVectors.iloc[:, 0:11]
print(toy_glove_vectors.shape)
toy_glove_vectors.to_csv(dataset_path+'glove.42B.10d.txt',header=False, index=False,sep=' ')
medium_glove_vectors = gloveVectors.iloc[:, 0:101]
print(medium_glove_vectors.shape)
medium_glove_vectors.to_csv(dataset_path+'glove.42B.100d.txt',header=False, index=False,sep=' ')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12059369202226346, 'fraction_numerical': 0.029684601113172542, 'mean_word_length': 10.25, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '509510', 'license': 'Apache-2.0', 'n_tokens_mistral': 221, 'n_tokens_neox': 204, 'n_words': 33, 'path': 'pre-processing/glove-word-vectors-dataset.ipynb', 'repo_name': 'Kandy16/constituency-parsing', 'size': '539'}