Instructions to use spybyscript/gliner25-decide-litert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use spybyscript/gliner25-decide-litert with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- GLiNER2
How to use spybyscript/gliner25-decide-litert with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("spybyscript/gliner25-decide-litert") # 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: 594 Bytes
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"_attn_implementation_autoset": true,
"_name_or_path": "/home/urchadezaratiana/checkpoints/checkpoint-45500",
"architecture": "span",
"architecture_version": 1,
"architectures": [
"SpanExtractor"
],
"attn_implementation": "sdpa",
"config_version": 3,
"counting_layer": "count_lstm",
"max_len": null,
"max_width": 8,
"model_name": "microsoft/deberta-v3-large",
"model_type": "extractor",
"span_head": {
"dropout": 0.1,
"max_width": 8,
"span_mode": "markerV0"
},
"token_pooling": "first",
"transformers_version": "4.48.1",
"use_moe": false
}
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