Referencias:
es_anotado
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_annotated')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 17528 |
- Características :
{
"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_neutral
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_neutral')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 9675 |
- Características :
{
"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_anotado
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_annotated')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 14449 |
- Características :
{
"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
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_neutral')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 10794 |
- Características :
{
"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"
}
}
Referencias:
es_anotado
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_annotated')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 17528 |
- Características :
{
"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_neutral
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/en_neutral')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 9675 |
- Características :
{
"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_anotado
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_annotated')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 14449 |
- Características :
{
"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
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:xed_en_fi/fi_neutral')
- Descripción :
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.
- Licencia : Licencia: Creative Commons Attribution 4.0 International License (CC-BY)
- Versión : 1.1.0
- Divisiones :
| Separar | Ejemplos |
|---|---|
'train' | 10794 |
- Características :
{
"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"
}
}