Token Classification
Transformers
Safetensors
English
bert
NER
named-entity-recognition
central-bank
BIS
speeches
finance
economics
monetary policy
Instructions to use bilalzafar/CentralBank-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CentralBank-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bilalzafar/CentralBank-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CentralBank-NER") model = AutoModelForTokenClassification.from_pretrained("bilalzafar/CentralBank-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "B-AFFILIATION", | |
| "1": "B-AUTHOR", | |
| "2": "B-COUNTRY", | |
| "3": "B-POSITION", | |
| "4": "I-AFFILIATION", | |
| "5": "I-AUTHOR", | |
| "6": "I-COUNTRY", | |
| "7": "I-POSITION", | |
| "8": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-AFFILIATION": 0, | |
| "B-AUTHOR": 1, | |
| "B-COUNTRY": 2, | |
| "B-POSITION": 3, | |
| "I-AFFILIATION": 4, | |
| "I-AUTHOR": 5, | |
| "I-COUNTRY": 6, | |
| "I-POSITION": 7, | |
| "O": 8 | |
| }, | |
| "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": "4.53.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |