Riferimenti:
en_annotato
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_annotated')
- Descrizione :
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.
- Licenza : Licenza: Licenza Creative Commons Attribuzione 4.0 Internazionale (CC-BY)
- Versione : 1.1.0
- Divide :
Diviso | Esempi |
---|---|
'train' | 17528 |
- Caratteristiche :
{
"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_neutro
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_neutral')
- Descrizione :
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.
- Licenza : Licenza: Licenza Creative Commons Attribuzione 4.0 Internazionale (CC-BY)
- Versione : 1.1.0
- Divide :
Diviso | Esempi |
---|---|
'train' | 9675 |
- Caratteristiche :
{
"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_annotato
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_annotated')
- Descrizione :
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.
- Licenza : Licenza: Licenza Creative Commons Attribuzione 4.0 Internazionale (CC-BY)
- Versione : 1.1.0
- Divide :
Diviso | Esempi |
---|---|
'train' | 14449 |
- Caratteristiche :
{
"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
Utilizzare il comando seguente per caricare questo set di dati in TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_neutral')
- Descrizione :
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.
- Licenza : Licenza: Licenza Creative Commons Attribuzione 4.0 Internazionale (CC-BY)
- Versione : 1.1.0
- Divide :
Diviso | Esempi |
---|---|
'train' | 10794 |
- Caratteristiche :
{
"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"
}
}