Instructions to use Firdania/sindesa-indobert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Firdania/sindesa-indobert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Firdania/sindesa-indobert-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Firdania/sindesa-indobert-ner") model = AutoModelForTokenClassification.from_pretrained("Firdania/sindesa-indobert-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,510 Bytes
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"_num_labels": 5,
"add_cross_attention": false,
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"decoder_start_token_id": null,
"directionality": "bidi",
"dtype": "float32",
"eos_token_id": null,
"finetuning_task": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-DOKUMEN",
"2": "I-DOKUMEN",
"3": "B-INSTANSI",
"4": "I-INSTANSI",
"5": "B-LAYANAN",
"6": "I-LAYANAN",
"7": "B-WILAYAH",
"8": "I-WILAYAH"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"B-DOKUMEN": 1,
"B-INSTANSI": 3,
"B-LAYANAN": 5,
"B-WILAYAH": 7,
"I-DOKUMEN": 2,
"I-INSTANSI": 4,
"I-LAYANAN": 6,
"I-WILAYAH": 8,
"O": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"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",
"prefix": null,
"pruned_heads": {},
"task_specific_params": null,
"tie_word_embeddings": true,
"torchscript": false,
"transformers_version": "5.13.1",
"type_vocab_size": 2,
"use_bfloat16": false,
"use_cache": false,
"vocab_size": 50000
}
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