Instructions to use AXKuhta/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXKuhta/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AXKuhta/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AXKuhta/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("AXKuhta/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
File size: 1,183 Bytes
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"add_cross_attention": false,
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "B-COMMENT",
"1": "B-INDEX",
"2": "B-NAME",
"3": "B-QTY",
"4": "B-RANGE_END",
"5": "B-UNIT",
"6": "I-COMMENT",
"7": "I-NAME",
"8": "I-UNIT",
"9": "OTHER"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"B-COMMENT": 0,
"B-INDEX": 1,
"B-NAME": 2,
"B-QTY": 3,
"B-RANGE_END": 4,
"B-UNIT": 5,
"I-COMMENT": 6,
"I-NAME": 7,
"I-UNIT": 8,
"OTHER": 9
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tie_word_embeddings": true,
"transformers_version": "5.6.2",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 30522
}
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