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@@ -24,7 +24,7 @@ The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (
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  WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
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  annotated with LOC (location), PER (person), and ORG (organisation)
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  tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of
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- Rahimi et al. (2019), and uses the following subsets from the original WikiANN corpus
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  Language WikiAnn ISO 639-3
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  Indonesian id ind
@@ -50,25 +50,25 @@ ind, jav, min, sun, ace, zlm, map-bms, mya, tgl, tha, vie, khm
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  ## Supported Tasks
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  Named Entity Recognition
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-
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  ## Dataset Usage
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  ### Using `datasets` library
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  ```
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- from datasets import load_dataset
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- dset = datasets.load_dataset("SEACrowd/wikiann", trust_remote_code=True)
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  ```
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  ### Using `seacrowd` library
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  ```import seacrowd as sc
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  # Load the dataset using the default config
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- dset = sc.load_dataset("wikiann", schema="seacrowd")
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  # Check all available subsets (config names) of the dataset
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- print(sc.available_config_names("wikiann"))
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  # Load the dataset using a specific config
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- dset = sc.load_dataset_by_config_name(config_name="<config_name>")
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  ```
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-
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- More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
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-
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  ## Dataset Homepage
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  WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
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  annotated with LOC (location), PER (person), and ORG (organisation)
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  tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of
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+ Rahimi et al. (2019), and uses the following subsets from the original WikiANN corpus
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  Language WikiAnn ISO 639-3
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  Indonesian id ind
 
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  ## Supported Tasks
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  Named Entity Recognition
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+
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  ## Dataset Usage
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  ### Using `datasets` library
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  ```
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+ from datasets import load_dataset
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+ dset = datasets.load_dataset("SEACrowd/wikiann", trust_remote_code=True)
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  ```
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  ### Using `seacrowd` library
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  ```import seacrowd as sc
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  # Load the dataset using the default config
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+ dset = sc.load_dataset("wikiann", schema="seacrowd")
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  # Check all available subsets (config names) of the dataset
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+ print(sc.available_config_names("wikiann"))
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  # Load the dataset using a specific config
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+ dset = sc.load_dataset_by_config_name(config_name="<config_name>")
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  ```
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+
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+ More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
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+
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  ## Dataset Homepage
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