Zero-Shot Classification
GLiNER2
Safetensors
English
Russian
extractor
safety
pii
ai-security
zero-shot
text-classification
span-categorization
token-classification
guardrails
Instructions to use hivetrace/gliner-guard-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-biencoder with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-biencoder") # 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: 648 Bytes
d4b0603 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"backend": "tokenizers",
"bos_token": "<bos>",
"clean_up_tokenization_spaces": false,
"cls_token": "<bos>",
"eos_token": "<eos>",
"extra_special_tokens": [
"[SEP_STRUCT]",
"[SEP_TEXT]",
"[P]",
"[C]",
"[E]",
"[R]",
"[L]",
"[EXAMPLE]",
"[OUTPUT]",
"[DESCRIPTION]"
],
"is_local": false,
"mask_token": "<mask>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "<pad>",
"padding_side": "right",
"sep_token": "<eos>",
"spaces_between_special_tokens": false,
"tokenizer_class": "TokenizersBackend",
"unk_token": "<unk>"
}
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