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
| { | |
| "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 | |
| } | |