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=================================================================
XGLUE train/dev/test file format (* denotes a specific language):
=================================================================
QAM:
(1) xglue.qam.en.train: question \t answer passage \t answer label
(2) xglue.qam.*.dev: question \t answer passage \t answer label
(3) xglue.qam.*.test: question \t answer passage
QADSM:
(1) xglue.qadsm.en.train: query \t ad title \t ad description \t relevance label
(2) xglue.qadsm.*.dev: query \t ad title \t ad description \t relevance label
(3) xglue.qadsm.*.test: query \t ad title \t ad description
WPR:
(1) xglue.wpr.en.train: query \t web page title \t web page snippet \t relevance label
(2) xglue.wpr.*.dev: query \t web page title \t web page snippet \t relevance label
(3) xglue.wpr.*.test: query \t web page title \t web page snippet
NC:
(1) xglue.nc.en.train: query \t news title \t news body \t news category
(2) xglue.nc.*.dev: query \t news title \t news body \t news category
(3) xglue.nc.*.test: query \t news title \t news body
QG:
(1) xglue.qg.en.src.train: answer passage
(2) xglue.qg.en.tgt.train: labeled question
(3) xglue.qg.*.src.dev: answer passage
(4) xglue.qg.*.tgt.dev: labeled question
(5) xglue.qg.*.src.test: answer passage
NTG:
(1) xglue.ntg.en.src.train: news body
(2) xglue.ntg.en.tgt.train: labeled news title
(3) xglue.ntg.*.src.dev: news body
(4) xglue.ntg.*.tgt.dev: labeled news title
(5) xglue.ntg.*.src.test: news body
XNLI:
(1) multinli.train.en.tsv premise \t hypothesis \t label
(2) xnli.dev.tsv language \t gold_label \t sentence1_binary_parse \t sentence2_binary_parse \t sentence1_parse \t sentence2_parse \t sentence1 \t sentence2 \t promptID \t ...
(3) xnli.test.tsv language \t gold_label \t sentence1_binary_parse \t sentence2_binary_parse \t sentence1_parse \t sentence2_parse \t sentence1 \t sentence2 \t promptID \t ...
NER:
(sentences are separated by a blank line)
(1) en.train word \space label
(2) *.dev word \space label
(3) *.test word \space label
POS:
(sentences are separated by a blank line)
(1) en.train word \space label
(2) *.dev word \space label
(3) *.test word \space label
PAWSX:
(The first line is header: "id" \t "sentence1" \t "sentence2" \t "label")
(1) en/train.tsv id \t sentence1 \t sentence2 \t label
(2) */dev_2k.tsv id \t sentence1 \t sentence2 \t label
(3) */test_2k.tsv id \t sentence1 \t sentence2 \t label
MLQA:
(1) squad1.1/train-v1.1.json
{
"title": "...",
"paragraphs": [
{
"context": "...",
"qas": [
{
"answers": [
{
"answer_start": ...,
"text": "..."
}
],
"question": "...",
"id": "..."
}]
}
}
(2) MLQA/MLQA_v1/dev/dev-context-*-question-*.json same as train set
(3) MLQA/MLQA_v1/test/test-context-*-question-*.json same as train set
=================================================================
XGLUE prediction file format (* denotes a specific language):
=================================================================
(NOTES: All the tasks except MLQA and PAWS-X: The line number of each prediction file should be same as the input file. The i-th line of input corresponding to i-th line of output.
PAWS-X's input file has one additional header. MLQA uses JSON format.)
QAM:
QAM/*.prediction: predicted label
(Label should be in [0, 1])
QADSM:
QADSM/*.prediction: predicted label
(Label should be in ["Good", "Bad"])
WPR:
WPR/*.prediction: predicted label
(Label should be in [0, 1, 2, 3, 4], the meaning of every rating is presented by the dictionary: {"perfect":"4", "excellent":"3", "good":"2", "fair":"1", "bad":"0"})
NC:
NC/*.prediction: predicted label
(Label should be in ["foodanddrink", "sports", "news", "entertainment", "health", "video", "finance", "travel", "lifestyle", "autos"])
QG:
QG/*.prediction: predicted question
NTG:
NTG/*.prediction: predicted news title
XNLI:
XNLI/*.prediction: predicted label
(Label should be in ["neutral", "contradiction", "entailment"])
NER:
NER/*.prediction: predicted label of each word
(Label should be in ["B-LOC", "B-MISC", "B-ORG", "B-PER", "I-LOC", "I-MISC", "I-ORG", "I-PER", "O"])
POS:
POS/*.prediction: predicted label of each word
(Label should be in ["ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X"])
PAWS-X:
PAWSX/*.prediction: predicted label
(Label should be in [0, 1])
MLQA:
MLQA/*.prediction: {"qas_id1": "answer text 1", "qas_id2": "answer text 2"}