Token Classification
Transformers
Safetensors
MultiLabelBert
multilabel
multilabel-token-classification
custom_code
Instructions to use jvaquet/multilabel-classification-bert-ace2004 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvaquet/multilabel-classification-bert-ace2004 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jvaquet/multilabel-classification-bert-ace2004", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("jvaquet/multilabel-classification-bert-ace2004", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,555 Bytes
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"add_cross_attention": false,
"architectures": [
"BertForMultiLabelTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"auto_map": {
"AutoConfig": "configuration_multilabelbert.MultiLabelBertConfig",
"AutoModelForTokenClassification": "modeling_multilabelbert.BertForMultiLabelTokenClassification"
},
"bos_token_id": null,
"classifier_dropout": null,
"custom_pipelines": {
"multilabel-ner": {
"default": {
"model": {
"pt": [
"jvaquet/multilabel-classification-bert",
"main"
]
}
},
"impl": "multilabel_ner.MultilabelNerPipeline",
"pt": [
"AutoModelForTokenClassification"
],
"type": "text"
}
},
"directionality": "bidi",
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "B-GPE",
"1": "I-GPE",
"2": "E-GPE",
"3": "S-GPE",
"4": "B-VEH",
"5": "I-VEH",
"6": "E-VEH",
"7": "S-VEH",
"8": "B-PER",
"9": "I-PER",
"10": "E-PER",
"11": "S-PER",
"12": "B-WEA",
"13": "I-WEA",
"14": "E-WEA",
"15": "S-WEA",
"16": "B-FAC",
"17": "I-FAC",
"18": "E-FAC",
"19": "S-FAC",
"20": "B-LOC",
"21": "I-LOC",
"22": "E-LOC",
"23": "S-LOC",
"24": "B-ORG",
"25": "I-ORG",
"26": "E-ORG",
"27": "S-ORG"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"label2id": {
"B-FAC": 16,
"B-GPE": 0,
"B-LOC": 20,
"B-ORG": 24,
"B-PER": 8,
"B-VEH": 4,
"B-WEA": 12,
"E-FAC": 18,
"E-GPE": 2,
"E-LOC": 22,
"E-ORG": 26,
"E-PER": 10,
"E-VEH": 6,
"E-WEA": 14,
"I-FAC": 17,
"I-GPE": 1,
"I-LOC": 21,
"I-ORG": 25,
"I-PER": 9,
"I-VEH": 5,
"I-WEA": 13,
"S-FAC": 19,
"S-GPE": 3,
"S-LOC": 23,
"S-ORG": 27,
"S-PER": 11,
"S-VEH": 7,
"S-WEA": 15
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "MultiLabelBert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"position_embedding_type": "absolute",
"tie_word_embeddings": true,
"transformers_version": "5.5.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 28996
}
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