Instructions to use davanstrien/hub-task-tagger-gliner2.5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use davanstrien/hub-task-tagger-gliner2.5-base with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("davanstrien/hub-task-tagger-gliner2.5-base") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 1,667 Bytes
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"text_column": "text",
"tasks": [
{
"name": "labels",
"labels": [
"any-to-any",
"audio-classification",
"audio-to-audio",
"automatic-speech-recognition",
"depth-estimation",
"feature-extraction",
"fill-mask",
"graph-ml",
"image-classification",
"image-feature-extraction",
"image-segmentation",
"image-text-to-image",
"image-text-to-text",
"image-text-to-video",
"image-to-3d",
"image-to-image",
"image-to-text",
"image-to-video",
"mask-generation",
"multiple-choice",
"object-detection",
"question-answering",
"reinforcement-learning",
"robotics",
"sentence-similarity",
"summarization",
"table-question-answering",
"table-to-text",
"tabular-classification",
"tabular-regression",
"tabular-to-text",
"text-classification",
"text-generation",
"text-ranking",
"text-retrieval",
"text-to-3d",
"text-to-audio",
"text-to-image",
"text-to-speech",
"text-to-video",
"time-series-forecasting",
"token-classification",
"translation",
"unconditional-image-generation",
"video-classification",
"video-text-to-text",
"visual-document-retrieval",
"visual-question-answering",
"voice-activity-detection",
"zero-shot-classification",
"zero-shot-image-classification",
"zero-shot-object-detection"
],
"multi_label": true
}
]
} |