Token Classification
GLiNER
PyTorch
Catalan
Spanish
NER
Catalan
NLP
television transcriptions
manual annotation
GLiNER
Instructions to use Ugiat/NERCat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use Ugiat/NERCat with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("Ugiat/NERCat") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
File size: 1,284 Bytes
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"added_tokens_decoder": {
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
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},
"2": {
"content": "[SEP]",
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"rstrip": false,
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"special": true
},
"3": {
"content": "[UNK]",
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"normalized": true,
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"special": true
},
"128000": {
"content": "[MASK]",
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"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "[CLS]",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"split_by_punct": false,
"tokenizer_class": "DebertaV2Tokenizer",
"unk_token": "[UNK]",
"vocab_type": "spm"
}
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