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Customizing Dataset

This part of tutorial shows how to prepare a custom dataset.

1. Overview

If you want to load a custom dataset, you should figure out 2 problems:

  1. Where is the dataset?
  2. Is the data format already supported?

In the following sections, we will introduce various dataset loading alternatives and different data formats we support (more and more on the way).

2. Loading Dataset

Currently, 5 types of dataset loading methods are supported. You can set it in the configuration file.

2.1 Loading dataset from ModelScope

dataset:
  name: ${modelscope_dataset_name}
  access_token: ${access_token}

name should be one of the uploaded datasets in ModelScope, such as damo/resume_ner. access_token is NOT necessary unless the dataset is private.

2.2 Loading dataset from Huggingface

dataset:
  path: ${huggingface_dataset_name}

path should be one of the uploaded datasets in Huggingface, such as conll2003.

2.3 Loading dataset via dataset loading script

dataset:
  path: ${path_to_py_script_or_folder}

path should be the absolute path to a custom python script for the datasets.load_dataset or a directory containing the script.

2.4 Loading dataset files

dataset:
  data_file:
    train: ${path_to_train_file}
    valid: ${path_to_validation_file}
    test: ${path_to_test_file}
  data_type: ${data_format}

train valid test could be the urls or local paths (absolute paths) to the dataset files. data_type should be one of the supported data formats such as conll.

2.5 Loading dataset directory or archive

dataset:
  data_file: ${path_or_url_to_dir_or_archive}
  data_type: ${data_format}

data_file could be an url like "https://data.deepai.org/conll2003.zip", or a local directory (absolute path) like "/home/data/conll2003", or a local archive file (absolute path) like "/home/data/conll2003.zip". Also data_type should be one of the supported data formats such as conll.

3. Supported Dataset Formats

3.1 Sequence Labeling Tasks

For example, NER, CWS, POS Tagging, etc.

3.1.1 CoNLL format

The widely-used CoNLL format is a specific vertical format (like TSV) that represents a tagged dataset. Normally it is a text file with one word per line with sentences separated by an empty line. The first column in a line should be a word and the last column should be the word's tag (usually from BIO or BIOES).

Data Example:

鲁  B-ORG
迅  I-ORG
文  I-ORG
学  I-ORG
院  I-ORG
组  O
织  O
有  O
关  O
专  O
家  O

我  O
是  O
另  O
一  O
句  O
话  O

To use CoNLL format, set data_type: conll. Optionally, you can use delimiter: ${custom_delimiter} to set a custom delimiter for the conll file. By default, the delimiter is whitespace or tab.

3.1.2 json-tags format

The json-tags format is similar to CoNLL format, where each sentence contains a 'text' field and a 'labels' field. The length of 'text' and 'labels' should be exactly equal to each other, so we can assign all labels to its corresponding character.

{
    "text": "鲁迅文学院组织有关专家",
    "labels": ["B-ORG", "I-ORG", "I-ORG", "I-ORG", "I-ORG", "O", "O", "O", "O", "O", "O"]
}

To use CoNLL format, set data_type: json_tags.

3.1.3 json-spans format

The json-spans format is another widely used format for both flat NER and nested NER. Each meaningful span is represented as a dict with start end type field, indicating the [start, end) offsets, and the type of the span.

{
    "text": "鲁迅文学院组织有关专家",
    "spans": [{"start": 0, "end": 5, "type": "ORG"}, ...]
}

What's more, we allow type to be a list of labels, which means multi-label tagging is possible.

{
    "text": "人民日报出版社新近出版了王梦奎的短文集《翠微居杂笔》。",
    "spans": [{"start": 0, "end": 7, "type": ["组织", "出版商", "出版社"]}, ...]
}

To use CoNLL format, set data_type: json_spans.

3.1.4 CLUENER format

The CLUENER format is the official format used in the CLUENER benchmark, which gathers entities of the same type in a group.

{
    "text": "鲁迅文学院组织有关专家",
    "label": {'ORG': [[0, 5], ...]}
}

To use CLUENER format, set data_type: cluener.