Instructions to use Sebbones/bert-finetuned-ner-requirements with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebbones/bert-finetuned-ner-requirements with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sebbones/bert-finetuned-ner-requirements")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Sebbones/bert-finetuned-ner-requirements") model = AutoModelForTokenClassification.from_pretrained("Sebbones/bert-finetuned-ner-requirements", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,038 Bytes
d8c7905 27a62ce d8c7905 | 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 42 43 44 45 46 47 | {
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-KNOWLEDGE",
"2": "I-KNOWLEDGE",
"3": "B-COMPETENCE",
"4": "I-COMPETENCE",
"5": "B-LEVEL",
"6": "I-LEVEL",
"7": "B-QUALIFICATION",
"8": "I-QUALIFICATION"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"B-COMPETENCE": 3,
"B-KNOWLEDGE": 1,
"B-LEVEL": 5,
"B-QUALIFICATION": 7,
"I-COMPETENCE": 4,
"I-KNOWLEDGE": 2,
"I-LEVEL": 6,
"I-QUALIFICATION": 8,
"O": 0
},
"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",
"torch_dtype": "float32",
"transformers_version": "4.50.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30000
}
|