Token Classification
Transformers
Safetensors
MultiLabelBert
multilabel
multilabel-token-classification
custom_code
Instructions to use jvaquet/multilabel-classification-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvaquet/multilabel-classification-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jvaquet/multilabel-classification-bert", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("jvaquet/multilabel-classification-bert", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,168 Bytes
5bb7ef5 | 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 34 35 36 37 38 39 40 41 | {
"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,
"directionality": "bidi",
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"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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