84 lines
2.8 KiB
JSON
84 lines
2.8 KiB
JSON
{
|
|
"_comment": "Example text inputs and expected outputs for the NLP Text Analyzer plugin.",
|
|
"examples": [
|
|
{
|
|
"_comment": "Sentiment Analysis Example",
|
|
"task": "sentiment_analysis",
|
|
"input_text": "This is the best day ever! I'm so happy and excited.",
|
|
"expected_output": {
|
|
"sentiment": "positive",
|
|
"confidence": 0.95
|
|
}
|
|
},
|
|
{
|
|
"_comment": "Sentiment Analysis Example - Negative",
|
|
"task": "sentiment_analysis",
|
|
"input_text": "I'm feeling really down today. Everything seems to be going wrong.",
|
|
"expected_output": {
|
|
"sentiment": "negative",
|
|
"confidence": 0.88
|
|
}
|
|
},
|
|
{
|
|
"_comment": "Sentiment Analysis Example - Neutral",
|
|
"task": "sentiment_analysis",
|
|
"input_text": "The weather is cloudy today.",
|
|
"expected_output": {
|
|
"sentiment": "neutral",
|
|
"confidence": 0.75
|
|
}
|
|
},
|
|
{
|
|
"_comment": "Named Entity Recognition Example",
|
|
"task": "named_entity_recognition",
|
|
"input_text": "Barack Obama was the 44th President of the United States.",
|
|
"expected_output": [
|
|
{
|
|
"entity": "Barack Obama",
|
|
"type": "PERSON"
|
|
},
|
|
{
|
|
"entity": "President",
|
|
"type": "TITLE"
|
|
},
|
|
{
|
|
"entity": "United States",
|
|
"type": "GPE"
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"_comment": "Text Summarization Example",
|
|
"task": "text_summarization",
|
|
"input_text": "The quick brown fox jumps over the lazy dog. This is a classic pangram, a sentence that contains all the letters of the alphabet. Pangrams are often used to test fonts or keyboard layouts. They can also be used to demonstrate handwriting skills.",
|
|
"expected_output": "This text is a classic pangram, a sentence containing all letters of the alphabet, used for testing fonts, keyboard layouts, and handwriting skills."
|
|
},
|
|
{
|
|
"_comment": "Keyword Extraction Example",
|
|
"task": "keyword_extraction",
|
|
"input_text": "Artificial intelligence is rapidly transforming various industries. Machine learning algorithms are becoming increasingly sophisticated, enabling automation and improved decision-making.",
|
|
"expected_output": [
|
|
"artificial intelligence",
|
|
"machine learning",
|
|
"algorithms",
|
|
"automation",
|
|
"decision-making"
|
|
]
|
|
},
|
|
{
|
|
"_comment": "Text Translation Example",
|
|
"task": "text_translation",
|
|
"source_language": "en",
|
|
"target_language": "es",
|
|
"input_text": "Hello, how are you?",
|
|
"expected_output": "Hola, ¿cómo estás?"
|
|
},
|
|
{
|
|
"_comment": "Question Answering Example",
|
|
"task": "question_answering",
|
|
"context_text": "The capital of France is Paris.",
|
|
"question_text": "What is the capital of France?",
|
|
"expected_output": "Paris"
|
|
}
|
|
]
|
|
} |