सन्दर्भ:
एआर
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/ar')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
बीजी
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/bg')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
डे
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/de')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
एल
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/el')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
एन
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/en')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
तों
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/es')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
फादर
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/fr')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
नमस्ते
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/hi')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
आरयू
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/ru')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
स्व
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/sw')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
वां
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/th')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
टी.आर.
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/tr')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
उर
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/ur')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
छठी
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/vi')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
झ
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/zh')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hypothesis": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
सभी_भाषाएँ
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xnli/all_languages')
- विवरण :
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
- लाइसेंस : कोई ज्ञात लाइसेंस नहीं
- संस्करण : 1.1.0
- विभाजन :
| विभाजित करना | उदाहरण |
|---|---|
'test' | 5010 |
'train' | 392702 |
'validation' | 2490 |
- विशेषताएँ :
{
"premise": {
"languages": [
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
],
"id": null,
"_type": "Translation"
},
"hypothesis": {
"languages": [
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
],
"num_languages": 15,
"id": null,
"_type": "TranslationVariableLanguages"
},
"label": {
"num_classes": 3,
"names": [
"entailment",
"neutral",
"contradiction"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}