Text Classification
Transformers
Safetensors
English
bert
CBDC
Central Bank Digital Currencies
Central Bank Digital Currency
Classification
Wholesale CBDC
Retail CBDC
Central Bank
Tone
Finance
NLP
Finance NLP
BERT
Transformers
Digital Currency
text-embeddings-inference
Instructions to use bilalzafar/CBDC-Type with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CBDC-Type with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bilalzafar/CBDC-Type")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CBDC-Type") model = AutoModelForSequenceClassification.from_pretrained("bilalzafar/CBDC-Type") - Notebooks
- Google Colab
- Kaggle
Model file
Browse files- config.json +35 -0
- label_mapping.json +12 -0
- model.safetensors +3 -0
- special_tokens_map.json +37 -0
- test_classification_report.txt +9 -0
- test_confusion_matrix.csv +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -0
- vocab.txt +0 -0
config.json
ADDED
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Retail CBDC",
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"1": "Wholesale CBDC",
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"2": "General/Unspecified"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"General/Unspecified": 2,
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"Retail CBDC": 0,
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"Wholesale CBDC": 1
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.55.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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label_mapping.json
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{
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"label2id": {
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"Retail CBDC": 0,
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"Wholesale CBDC": 1,
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"General/Unspecified": 2
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},
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"id2label": {
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"0": "Retail CBDC",
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"1": "Wholesale CBDC",
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"2": "General/Unspecified"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a7232da17bc326b3eb02dfdc753f0d88ff2018cc281cab8fc9aeda25a1bcbe33
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size 437961724
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special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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test_classification_report.txt
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precision recall f1-score support
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Retail CBDC 0.86 0.87 0.86 55
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Wholesale CBDC 0.97 0.97 0.97 33
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General/Unspecified 0.87 0.85 0.86 54
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accuracy 0.89 142
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macro avg 0.90 0.90 0.90 142
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weighted avg 0.89 0.89 0.89 142
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test_confusion_matrix.csv
ADDED
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48,0,7
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1,32,0
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7,1,46
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tokenizer.json
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The diff for this file is too large to render.
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tokenizer_config.json
ADDED
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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| 18 |
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},
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"101": {
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"content": "[CLS]",
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| 21 |
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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| 25 |
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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| 32 |
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"single_word": false,
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| 33 |
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"special": true
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| 34 |
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},
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"103": {
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"content": "[MASK]",
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| 37 |
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"lstrip": false,
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"normalized": false,
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| 39 |
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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| 45 |
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"cls_token": "[CLS]",
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| 46 |
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"do_lower_case": true,
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| 47 |
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"extra_special_tokens": {},
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| 48 |
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"mask_token": "[MASK]",
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| 49 |
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"model_max_length": 512,
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| 50 |
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"pad_token": "[PAD]",
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| 51 |
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"sep_token": "[SEP]",
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| 52 |
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"strip_accents": null,
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| 53 |
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"tokenize_chinese_chars": true,
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| 54 |
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"tokenizer_class": "BertTokenizer",
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| 55 |
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"unk_token": "[UNK]"
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| 56 |
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}
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vocab.txt
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