सन्दर्भ:
en_annotated
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xed_en_fi/en_annotated')
- विवरण :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- लाइसेंस : लाइसेंस: क्रिएटिव कॉमन्स एट्रिब्यूशन 4.0 इंटरनेशनल लाइसेंस (CC-BY)
- संस्करण : 1.1.0
- विभाजन :
विभाजित करना | उदाहरण |
---|---|
'train' | 17528 |
- विशेषताएँ :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"feature": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
en_तटस्थ
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xed_en_fi/en_neutral')
- विवरण :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- लाइसेंस : लाइसेंस: क्रिएटिव कॉमन्स एट्रिब्यूशन 4.0 इंटरनेशनल लाइसेंस (CC-BY)
- संस्करण : 1.1.0
- विभाजन :
विभाजित करना | उदाहरण |
---|---|
'train' | 9675 |
- विशेषताएँ :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
fi_annotated
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xed_en_fi/fi_annotated')
- विवरण :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- लाइसेंस : लाइसेंस: क्रिएटिव कॉमन्स एट्रिब्यूशन 4.0 इंटरनेशनल लाइसेंस (CC-BY)
- संस्करण : 1.1.0
- विभाजन :
विभाजित करना | उदाहरण |
---|---|
'train' | 14449 |
- विशेषताएँ :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"feature": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
fi_neutral
इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:
ds = tfds.load('huggingface:xed_en_fi/fi_neutral')
- विवरण :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- लाइसेंस : लाइसेंस: क्रिएटिव कॉमन्स एट्रिब्यूशन 4.0 इंटरनेशनल लाइसेंस (CC-BY)
- संस्करण : 1.1.0
- विभाजन :
विभाजित करना | उदाहरण |
---|---|
'train' | 10794 |
- विशेषताएँ :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}