Token Classification
Transformers
Safetensors
English
bert
finance
terminology
term-extraction
english
ner
Instructions to use owen4512/bert-base-cased-finance-term-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use owen4512/bert-base-cased-finance-term-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="owen4512/bert-base-cased-finance-term-extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("owen4512/bert-base-cased-finance-term-extractor") model = AutoModelForTokenClassification.from_pretrained("owen4512/bert-base-cased-finance-term-extractor") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- LICENSE.txt +5 -0
- README.md +9 -0
- config.json +40 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
LICENSE.txt
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This model is fine-tuned from google-bert/bert-base-cased on terminology data released under CC BY-NC 4.0.
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This model is intended for non-commercial use only.
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Users must comply with the original dataset license and provide appropriate attribution.
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README.md
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---
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language:
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- en
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license: other
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license_name: cc-by-nc-4.0-derived
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base_model: google-bert/bert-base-cased
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library_name: transformers
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pipeline_tag: token-classification
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---
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config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": 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": "B-TERM",
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"1": "I-TERM",
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"2": "O"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"label2id": {
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"B-TERM": 0,
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"I-TERM": 1,
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"O": 2
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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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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 28996
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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:b90df6ca802b65b9797e4864b5f4d0797fbc2942a41c32fb49b3895698707720
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size 430911260
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"is_local": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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