참고자료:
presupposition_all_n_presupposition
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_all_n_presupposition')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'all_n_presupposition' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
presupposition_both_presupposition
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_both_presupposition')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'both_presupposition' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
presupposition_change_of_state
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_change_of_state')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'change_of_state' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
전제_갈라진_존재
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_cleft_existence')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'cleft_existence' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
전제_갈라진_고유성
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_cleft_uniqueness')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'cleft_uniqueness' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
presupposition_only_presupposition
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_only_presupposition')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'only_presupposition' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
전제_소유_확정_존재
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_existence')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'possessed_definites_existence' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
전제_소유_확정_고유성
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_uniqueness')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'possessed_definites_uniqueness' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
presupposition_question_presupposition
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/presupposition_question_presupposition')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'question_presupposition' | 1900 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger1": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger2": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"presupposition": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"pairID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"paradigmID": {
"dtype": "int16",
"id": null,
"_type": "Value"
}
}
암시_연결
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_connectives')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'connectives' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
암시적_등급_형용사
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_gradable_adjective')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'gradable_adjective' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
암시적_등급_동사
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_gradable_verb')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'gradable_verb' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
암시적_모달
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_modals')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'modals' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
implicature_numerals_10_100
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_numerals_10_100')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'numerals_10_100' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
암시_숫자_2_3
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_numerals_2_3')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'numerals_2_3' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
암시적 수량자
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:imppres/implicature_quantifiers')
- 설명 :
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
- 라이센스 : Creative Commons Attribution-NonCommercial 4.0 국제 공중 라이센스
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'quantifiers' | 1200 |
- 특징 :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gold_label_log": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"gold_label_prag": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"spec_relation": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"item_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"trigger": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lexemes": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}