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shreyas-singh
/
autotrain-data-MedicalTokenClassification

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builder_name (unknown)citation (string)config_name (unknown)dataset_size (unknown)description (string)download_checksums (unknown)download_size (unknown)features (dict)homepage (string)license (string)post_processed (unknown)post_processing_size (unknown)size_in_bytes (unknown)splits (dict)supervised_keys (unknown)task_templates (unknown)version (unknown)_data_files (list)_fingerprint (string)_format_columns (sequence)_format_kwargs (dict)_format_type (unknown)_indexes (dict)_output_all_columns (bool)_split (unknown)
""
"AutoTrain generated dataset"
{ "feat_id": { "dtype": "string", "id": null, "_type": "Value" }, "tokens": { "feature": { "dtype": "string", "id": null, "_type": "Value" }, "length": -1, "id": null, "_type": "Sequence" }, "feat_pos_tags": { "feature": { "num_classes": 47, "names": [ "\"", "#", "$", "''", "(", ")", ",", ".", ":", "CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NN", "NNP", "NNPS", "NNS", "NN|SYM", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB", "``" ], "id": null, "_type": "ClassLabel" }, "length": -1, "id": null, "_type": "Sequence" }, "feat_chunk_tags": { "feature": { "num_classes": 23, "names": [ "B-ADJP", "B-ADVP", "B-CONJP", "B-INTJ", "B-LST", "B-NP", "B-PP", "B-PRT", "B-SBAR", "B-UCP", "B-VP", "I-ADJP", "I-ADVP", "I-CONJP", "I-INTJ", "I-LST", "I-NP", "I-PP", "I-PRT", "I-SBAR", "I-UCP", "I-VP", "O" ], "id": null, "_type": "ClassLabel" }, "length": -1, "id": null, "_type": "Sequence" }, "tags": { "feature": { "num_classes": 9, "names": [ "B-LOC", "B-MISC", "B-ORG", "B-PER", "I-LOC", "I-MISC", "I-ORG", "I-PER", "O" ], "id": null, "_type": "ClassLabel" }, "length": -1, "id": null, "_type": "Sequence" } }
""
""
{ "train": { "name": "train", "num_bytes": 4930007, "num_examples": 10014, "dataset_name": null } }
[ { "filename": "dataset.arrow" } ]
"0274bbd00f190d03"
[ "feat_chunk_tags", "feat_id", "feat_pos_tags", "tags", "tokens" ]
{}
{}
false

AutoTrain Dataset for project: MedicalTokenClassification

Dataset Descritpion

This dataset has been automatically processed by AutoTrain for project MedicalTokenClassification.

Languages

The BCP-47 code for the dataset's language is unk.

Dataset Structure

Data Instances

A sample from this dataset looks as follows:

[
  {
    "feat_id": "13104",
    "tokens": [
      "Jackie",
      "Frank"
    ],
    "feat_pos_tags": [
      21,
      21
    ],
    "feat_chunk_tags": [
      5,
      16
    ],
    "tags": [
      3,
      7
    ]
  },
  {
    "feat_id": "9297",
    "tokens": [
      "U.S.",
      "lauds",
      "Russian-Chechen",
      "deal",
      "."
    ],
    "feat_pos_tags": [
      21,
      20,
      15,
      20,
      7
    ],
    "feat_chunk_tags": [
      5,
      16,
      16,
      16,
      22
    ],
    "tags": [
      0,
      8,
      1,
      8,
      8
    ]
  }
]

Dataset Fields

The dataset has the following fields (also called "features"):

{
  "feat_id": "Value(dtype='string', id=None)",
  "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "feat_pos_tags": "Sequence(feature=ClassLabel(num_classes=47, names=['\"', '#', '$', \"''\", '(', ')', ',', '.', ':', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB', '``'], id=None), length=-1, id=None)",
  "feat_chunk_tags": "Sequence(feature=ClassLabel(num_classes=23, names=['B-ADJP', 'B-ADVP', 'B-CONJP', 'B-INTJ', 'B-LST', 'B-NP', 'B-PP', 'B-PRT', 'B-SBAR', 'B-UCP', 'B-VP', 'I-ADJP', 'I-ADVP', 'I-CONJP', 'I-INTJ', 'I-LST', 'I-NP', 'I-PP', 'I-PRT', 'I-SBAR', 'I-UCP', 'I-VP', 'O'], id=None), length=-1, id=None)",
  "tags": "Sequence(feature=ClassLabel(num_classes=9, names=['B-LOC', 'B-MISC', 'B-ORG', 'B-PER', 'I-LOC', 'I-MISC', 'I-ORG', 'I-PER', 'O'], id=None), length=-1, id=None)"
}

Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

Split name Num samples
train 10014
valid 4028
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Models trained or fine-tuned on shreyas-singh/autotrain-data-MedicalTokenClassification