Token Classification
Transformers
ONNX
Safetensors
PEFT
English
bert
ner
legal
legal-bert
nigerian-law
lora
Instructions to use WhiteRoomProdigy/amicus-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhiteRoomProdigy/amicus-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WhiteRoomProdigy/amicus-ner-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WhiteRoomProdigy/amicus-ner-v2") model = AutoModelForTokenClassification.from_pretrained("WhiteRoomProdigy/amicus-ner-v2") - PEFT
How to use WhiteRoomProdigy/amicus-ner-v2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,490 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,
"eos_token_ids": 0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-CASE_NAME",
"2": "I-CASE_NAME",
"3": "B-CITATION",
"4": "I-CITATION",
"5": "B-STATUTE",
"6": "I-STATUTE",
"7": "B-COURT",
"8": "I-COURT",
"9": "B-DATE",
"10": "I-DATE",
"11": "B-JUDGE",
"12": "I-JUDGE",
"13": "B-RATIO",
"14": "I-RATIO",
"15": "B-HELD",
"16": "I-HELD"
},
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"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
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"O": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 30522
}
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