Token Classification
Transformers
Safetensors
PyTorch
Italian
distilbert
ner
ristorazione
fine-tuning
Instructions to use software-si/horeca-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use software-si/horeca-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="software-si/horeca-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("software-si/horeca-ner") model = AutoModelForTokenClassification.from_pretrained("software-si/horeca-ner") - Notebooks
- Google Colab
- Kaggle
File size: 1,309 Bytes
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"activation": "gelu",
"architectures": [
"DistilBertForTokenClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "B-DISPOSIZIONE",
"1": "B-NUM_ZONE_COTTURA",
"2": "B-POTENZA",
"3": "B-PROFONDIT\u00c0",
"4": "B-TIPO_COTTURA",
"5": "B-TIPO_FORNO",
"6": "B-TIPO_VANO",
"7": "I-DISPOSIZIONE",
"8": "I-NUM_ZONE_COTTURA",
"9": "I-POTENZA",
"10": "I-PROFONDIT\u00c0",
"11": "I-TIPO_COTTURA",
"12": "I-TIPO_FORNO",
"13": "I-TIPO_VANO",
"14": "O"
},
"initializer_range": 0.02,
"label2id": {
"B-DISPOSIZIONE": 0,
"B-NUM_ZONE_COTTURA": 1,
"B-POTENZA": 2,
"B-PROFONDIT\u00c0": 3,
"B-TIPO_COTTURA": 4,
"B-TIPO_FORNO": 5,
"B-TIPO_VANO": 6,
"I-DISPOSIZIONE": 7,
"I-NUM_ZONE_COTTURA": 8,
"I-POTENZA": 9,
"I-PROFONDIT\u00c0": 10,
"I-TIPO_COTTURA": 11,
"I-TIPO_FORNO": 12,
"I-TIPO_VANO": 13,
"O": 14
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.52.4",
"vocab_size": 30522
}
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