Token Classification
Transformers
Safetensors
English
bert_gat_pii
feature-extraction
pii
privacy
redaction
ner
bert
gat
graph-attention-network
custom_code
Instructions to use manikrishneshwar/pii-redactor-bert-gat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manikrishneshwar/pii-redactor-bert-gat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="manikrishneshwar/pii-redactor-bert-gat", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("manikrishneshwar/pii-redactor-bert-gat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertGATForTokenClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_bert_gat.BertGATConfig", | |
| "AutoModel": "modeling_bert_gat.BertGATForTokenClassification" | |
| }, | |
| "bert_model_name": "distilbert-base-uncased", | |
| "dropout": 0.0, | |
| "dtype": "float32", | |
| "gat_heads": 4, | |
| "gat_hidden": 128, | |
| "gat_layers": 2, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2", | |
| "3": "LABEL_3", | |
| "4": "LABEL_4", | |
| "5": "LABEL_5", | |
| "6": "LABEL_6", | |
| "7": "LABEL_7", | |
| "8": "LABEL_8", | |
| "9": "LABEL_9", | |
| "10": "LABEL_10", | |
| "11": "LABEL_11", | |
| "12": "LABEL_12", | |
| "13": "LABEL_13", | |
| "14": "LABEL_14", | |
| "15": "LABEL_15", | |
| "16": "LABEL_16", | |
| "17": "LABEL_17", | |
| "18": "LABEL_18", | |
| "19": "LABEL_19", | |
| "20": "LABEL_20", | |
| "21": "LABEL_21", | |
| "22": "LABEL_22", | |
| "23": "LABEL_23", | |
| "24": "LABEL_24", | |
| "25": "LABEL_25", | |
| "26": "LABEL_26", | |
| "27": "LABEL_27", | |
| "28": "LABEL_28", | |
| "29": "LABEL_29", | |
| "30": "LABEL_30" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_10": 10, | |
| "LABEL_11": 11, | |
| "LABEL_12": 12, | |
| "LABEL_13": 13, | |
| "LABEL_14": 14, | |
| "LABEL_15": 15, | |
| "LABEL_16": 16, | |
| "LABEL_17": 17, | |
| "LABEL_18": 18, | |
| "LABEL_19": 19, | |
| "LABEL_2": 2, | |
| "LABEL_20": 20, | |
| "LABEL_21": 21, | |
| "LABEL_22": 22, | |
| "LABEL_23": 23, | |
| "LABEL_24": 24, | |
| "LABEL_25": 25, | |
| "LABEL_26": 26, | |
| "LABEL_27": 27, | |
| "LABEL_28": 28, | |
| "LABEL_29": 29, | |
| "LABEL_3": 3, | |
| "LABEL_30": 30, | |
| "LABEL_4": 4, | |
| "LABEL_5": 5, | |
| "LABEL_6": 6, | |
| "LABEL_7": 7, | |
| "LABEL_8": 8, | |
| "LABEL_9": 9 | |
| }, | |
| "max_length": 256, | |
| "model_type": "bert_gat_pii", | |
| "top_k_attn": 5, | |
| "transformers_version": "5.1.0", | |
| "window": 3 | |
| } | |