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--- |
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dataset_info: |
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features: |
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- name: sentences |
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list: |
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- name: detokenized_text |
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dtype: string |
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- name: index |
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dtype: int64 |
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- name: token_positions |
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sequence: |
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sequence: int64 |
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- name: tokens |
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list: |
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- name: index |
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dtype: int64 |
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- name: text |
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dtype: string |
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- name: xpos |
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dtype: string |
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- name: coref_chains |
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sequence: |
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sequence: |
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sequence: int64 |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 41993013 |
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num_examples: 1345 |
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- name: validation |
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num_bytes: 4236748 |
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num_examples: 135 |
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- name: test |
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num_bytes: 4312728 |
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num_examples: 207 |
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download_size: 8195556 |
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dataset_size: 50542489 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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|
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Detokenizes the ECMT dataset using the kiwipiepy library. |
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The script used to convert the dataset is here: https://gist.github.com/ianporada/a246ebf59696c6e16e1bc1873bc182a4 |
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The library version used is kiwipiepy==0.20.3 / kiwipiepy_model==0.20.0 |
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The dataset schema is as follows: |
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```python |
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{ |
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# the original document filename |
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"doc_id": str, |
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# a list of sentences in the document |
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"sentences": [ |
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"index": int, # the index of the sentence within the document |
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"detokenized_text": str, # a single string representing the text of the sentence (detokenized using kiwipiepy) |
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|
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# a list of token positions which are tuples of the form (start, end) |
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# the token at index i corresponds to characters detokenized_text[start:end] |
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"token_positions": [(int, int), ...], |
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|
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# the original values of each token from the dataset |
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"tokens": [{"index": int, "text": str, "xpos": str}, ...], |
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], |
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# a list of coreference chains, each chain is a list of mentions |
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# each mention is a list of form [sentence_index, start_token_index, end_token_index] where token indices are inclusive indices within the given sentence |
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"coref_chains": [[[int, int, int], ...], ...] |
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} |
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``` |
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