<jupyter_start><jupyter_text>Malignant data<jupyter_code>for dirname in dir_list[8:12]:
    GCS_PATH = KaggleDatasets().get_gcs_path(dirname)
    TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train*.tfrec')
    TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test*.tfrec')
    train_size = count_data_items(TRAINING_FILENAMES)

    print(f'{dirname} \t {GCS_PATH}')
    print(f'Train samples: {train_size}')
    print(f'5-Fold size: {int(train_size * .75)}\n')<jupyter_output>malignant-v2-256x256 	 gs://kds-7a08ac82d57200266dc619d51106e4af5e89cedf0c6556f632deebf9
Train samples: 3960
5-Fold size: 2970

malignant-v2-384x384 	 gs://kds-4dacd59e0327aff4eef56d9398ecced45e56a0afa6a43bf6bed7251b
Train samples: 3960
5-Fold size: 2970

malignant-v2-512x512 	 gs://kds-e47f613ded612087ba7fcca706cccfd4e9fcd036dcb4127def0d79c4
Train samples: 3960
5-Fold size: 2970

malignant-v2-768x768 	 gs://kds-4826fbc5832fe6eb691053599c8fa1b06eb57685db32bc294a545f72
Train samples: 3960
5-Fold size: 2970<jupyter_text>Competition data<jupyter_code>for dirname in dir_list[12:]:
    GCS_PATH = KaggleDatasets().get_gcs_path(dirname)
    TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train*.tfrec')
    TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test*.tfrec')
    train_size = count_data_items(TRAINING_FILENAMES)
    test_size = count_data_items(TEST_FILENAMES)

    print(f'{dirname} \t {GCS_PATH}')
    print(f'Train samples: {train_size}')
    print(f'Test samples: {test_size}')
    print(f'5-Fold size: {int(train_size * .75)}\n')<jupyter_output>melanoma-256x256 	 gs://kds-fbc00c0b868eb34b554705994009a9d2ea1c168e4e3806326e516ba5
Train samples: 32692
Test samples: 10982
5-Fold size: 24519

melanoma-384x384 	 gs://kds-4794b97a7db9238be7b3a7e36e6d03f2de112b355765280d28e2b579
Train samples: 32692
Test samples: 10982
5-Fold size: 24519

melanoma-512x512 	 gs://kds-3f2b9fbffe7fc1f1219236db9534c99ce7c277d4d69662c3bbf6a6b0
Train samples: 32692
Test samples: 10982
5-Fold size: 24519

melanoma-768x768 	 gs://kds-863a8421e19bd1e8af6e49fd881c4adceae0015e529b674f0b61d4c9
Train samples: 32692
Test samples: 10982
5-Fold size: 24519<jupyter_text>External data (2019)<jupyter_code>for dirname in dir_list[4:8]:
    GCS_PATH = KaggleDatasets().get_gcs_path(dirname)
    TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train*.tfrec')
    TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test*.tfrec')
    train_size = count_data_items(TRAINING_FILENAMES)

    print(f'{dirname} \t {GCS_PATH}')
    print(f'Train samples: {train_size}')
    print(f'5-Fold size: {int(train_size * .75)}\n')<jupyter_output>isic2019-256x256 	 gs://kds-dfae178ddbe4da1a77993af1ac7ede33a6b835ee7c24404c2e618e00
Train samples: 25272
5-Fold size: 18954

isic2019-384x384 	 gs://kds-ba3a4a10d62d1e054c5eb06cc1cc58dca6e03bb18f5f06af448dec55
Train samples: 25272
5-Fold size: 18954

isic2019-512x512 	 gs://kds-f0e31d98a8a127bdee8ff028fef64d527f41e044629193a390f7610c
Train samples: 25272
5-Fold size: 18954

isic2019-768x768 	 gs://kds-d76b4251d97923c2736426394305c4928d933ed129182dc7adc4d77c
Train samples: 25272
5-Fold size: 18954<jupyter_text>Big Transfer (BiT) weights<jupyter_code>GCS_PATH = KaggleDatasets().get_gcs_path('big-transfer-bit-weights')
print(f'{GCS_PATH}')<jupyter_output>gs://kds-3291c2fc2e4dace373d145791f39ed24d7270d90595f7db108242541
{'fraction_non_alphanumeric': 0.10923863978332832, 'fraction_numerical': 0.21005115859163406, 'mean_word_length': 6.2894736842105265, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '382535', 'license': 'MIT', 'n_tokens_mistral': 1967, 'n_tokens_neox': 1547, 'n_words': 189, 'path': 'Model backlog/Other/datasets-gcs-path.ipynb', 'repo_name': 'dimitreOliveira/melanoma-classification', 'size': '3323'}