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
davanstrien HF Staff
Weights: GLiNER2.5-base fine-tuned on Hub dataset task tags (v3)
cbe8453 verified Download classification_schema.json from davanstrien/hub-task-tagger-gliner2.5-base: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/davanstrien/hub-task-tagger-gliner2.5-base/resolve/main/classification_schema.json
- Command line
-
hf download hf://davanstrien/hub-task-tagger-gliner2.5-base/classification_schema.json
-
curl -L -o classification_schema.json https://huggingface.co/davanstrien/hub-task-tagger-gliner2.5-base/resolve/main/classification_schema.json
1.67 kB
| { | |
| "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 | |
| } | |
| ] | |
| } |