Upload lora_pii_detector_bert-base-uncased_model LoRA model
Browse files- README.md +96 -0
- config.json +99 -0
- label_mapping.json +1 -0
- lora_config.json +1 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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base_model: bert-base-uncased
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tags:
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- lora
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- semantic-router
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- pii-classification
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- text-classification
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- candle
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- rust
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language:
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- en
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pipeline_tag: text-classification
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library_name: candle
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---
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# lora_pii_detector_bert-base-uncased_model
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## Model Description
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This is a LoRA (Low-Rank Adaptation) fine-tuned model based on **bert-base-uncased** for PII Detection - Detects personally identifiable information in text using token classification.
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This model is part of the [semantic-router](https://github.com/vllm-project/semantic-router) project and is optimized for use with the Candle framework in Rust.
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## Model Details
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- **Base Model**: bert-base-uncased
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- **Task**: Pii Classification
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- **Framework**: Candle (Rust)
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- **Model Size**: ~416MB
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- **LoRA Rank**: 16
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- **LoRA Alpha**: 32
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- **Target Modules**: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense
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## Usage
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### With semantic-router (Recommended)
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```python
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from semantic_router import SemanticRouter
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# The model will be automatically downloaded and used
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router = SemanticRouter()
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results = router.classify_batch(["Your text here"])
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```
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### With Candle (Rust)
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```rust
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use candle_core::{Device, Tensor};
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use candle_transformers::models::bert::BertModel;
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// Load the model using Candle
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let device = Device::Cpu;
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let model = BertModel::load(&device, &config, &weights)?;
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```
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## Training Details
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This model was fine-tuned using LoRA (Low-Rank Adaptation) technique:
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- **Rank**: 16
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- **Alpha**: 32
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- **Dropout**: 0.1
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- **Target Modules**: attention.self.query, attention.self.value, attention.output.dense, intermediate.dense, output.dense
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## Performance
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PII Detection - Detects personally identifiable information in text using token classification
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For detailed performance metrics, see the [training results](https://github.com/vllm-project/semantic-router/blob/main/training-result.md).
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## Files
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- `model.safetensors`: LoRA adapter weights
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- `config.json`: Model configuration
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- `lora_config.json`: LoRA-specific configuration
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- `tokenizer.json`: Tokenizer configuration
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- `label_mapping.json`: Label mappings for classification
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{semantic-router-lora,
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title={LoRA Fine-tuned Models for Semantic Router},
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author={Semantic Router Team},
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year={2025},
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url={https://github.com/vllm-project/semantic-router}
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}
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```
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## License
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Apache 2.0
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config.json
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{
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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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"classifier_dropout": null,
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"dtype": "float32",
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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": "O",
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"1": "B-AGE",
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"2": "I-AGE",
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"3": "B-CREDIT_CARD",
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"4": "I-CREDIT_CARD",
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"5": "B-DATE_TIME",
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"6": "I-DATE_TIME",
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"7": "B-DOMAIN_NAME",
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"8": "I-DOMAIN_NAME",
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"9": "B-EMAIL_ADDRESS",
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"10": "I-EMAIL_ADDRESS",
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"11": "B-GPE",
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"12": "I-GPE",
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"13": "B-IBAN_CODE",
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"14": "I-IBAN_CODE",
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"15": "B-IP_ADDRESS",
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"16": "I-IP_ADDRESS",
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"17": "B-NRP",
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"18": "I-NRP",
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"19": "B-ORGANIZATION",
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"20": "I-ORGANIZATION",
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"21": "B-PERSON",
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"22": "I-PERSON",
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"23": "B-PHONE_NUMBER",
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"24": "I-PHONE_NUMBER",
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"25": "B-STREET_ADDRESS",
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"26": "I-STREET_ADDRESS",
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"27": "B-TITLE",
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"28": "I-TITLE",
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"29": "B-US_DRIVER_LICENSE",
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"30": "I-US_DRIVER_LICENSE",
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"31": "B-US_SSN",
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"32": "I-US_SSN",
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"33": "B-ZIP_CODE",
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"34": "I-ZIP_CODE"
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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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"O": 0,
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"B-AGE": 1,
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"I-AGE": 2,
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"B-CREDIT_CARD": 3,
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"I-CREDIT_CARD": 4,
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"B-DATE_TIME": 5,
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"I-DATE_TIME": 6,
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"B-DOMAIN_NAME": 7,
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"I-DOMAIN_NAME": 8,
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"B-EMAIL_ADDRESS": 9,
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"I-EMAIL_ADDRESS": 10,
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"B-GPE": 11,
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"I-GPE": 12,
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"B-IBAN_CODE": 13,
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"I-IBAN_CODE": 14,
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"B-IP_ADDRESS": 15,
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"I-IP_ADDRESS": 16,
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"B-NRP": 17,
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"I-NRP": 18,
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"B-ORGANIZATION": 19,
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"I-ORGANIZATION": 20,
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"B-PERSON": 21,
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"I-PERSON": 22,
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"B-PHONE_NUMBER": 23,
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"I-PHONE_NUMBER": 24,
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"B-STREET_ADDRESS": 25,
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"I-STREET_ADDRESS": 26,
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"B-TITLE": 27,
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"I-TITLE": 28,
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"B-US_DRIVER_LICENSE": 29,
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"I-US_DRIVER_LICENSE": 30,
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"B-US_SSN": 31,
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"I-US_SSN": 32,
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"B-ZIP_CODE": 33,
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"I-ZIP_CODE": 34
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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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"transformers_version": "4.56.1",
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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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{"label_to_id": {"O": 0, "B-AGE": 1, "I-AGE": 2, "B-CREDIT_CARD": 3, "I-CREDIT_CARD": 4, "B-DATE_TIME": 5, "I-DATE_TIME": 6, "B-DOMAIN_NAME": 7, "I-DOMAIN_NAME": 8, "B-EMAIL_ADDRESS": 9, "I-EMAIL_ADDRESS": 10, "B-GPE": 11, "I-GPE": 12, "B-IBAN_CODE": 13, "I-IBAN_CODE": 14, "B-IP_ADDRESS": 15, "I-IP_ADDRESS": 16, "B-NRP": 17, "I-NRP": 18, "B-ORGANIZATION": 19, "I-ORGANIZATION": 20, "B-PERSON": 21, "I-PERSON": 22, "B-PHONE_NUMBER": 23, "I-PHONE_NUMBER": 24, "B-STREET_ADDRESS": 25, "I-STREET_ADDRESS": 26, "B-TITLE": 27, "I-TITLE": 28, "B-US_DRIVER_LICENSE": 29, "I-US_DRIVER_LICENSE": 30, "B-US_SSN": 31, "I-US_SSN": 32, "B-ZIP_CODE": 33, "I-ZIP_CODE": 34}, "id_to_label": {"0": "O", "1": "B-AGE", "2": "I-AGE", "3": "B-CREDIT_CARD", "4": "I-CREDIT_CARD", "5": "B-DATE_TIME", "6": "I-DATE_TIME", "7": "B-DOMAIN_NAME", "8": "I-DOMAIN_NAME", "9": "B-EMAIL_ADDRESS", "10": "I-EMAIL_ADDRESS", "11": "B-GPE", "12": "I-GPE", "13": "B-IBAN_CODE", "14": "I-IBAN_CODE", "15": "B-IP_ADDRESS", "16": "I-IP_ADDRESS", "17": "B-NRP", "18": "I-NRP", "19": "B-ORGANIZATION", "20": "I-ORGANIZATION", "21": "B-PERSON", "22": "I-PERSON", "23": "B-PHONE_NUMBER", "24": "I-PHONE_NUMBER", "25": "B-STREET_ADDRESS", "26": "I-STREET_ADDRESS", "27": "B-TITLE", "28": "I-TITLE", "29": "B-US_DRIVER_LICENSE", "30": "I-US_DRIVER_LICENSE", "31": "B-US_SSN", "32": "I-US_SSN", "33": "B-ZIP_CODE", "34": "I-ZIP_CODE"}}
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lora_config.json
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{"rank": 16, "alpha": 32, "dropout": 0.1, "target_modules": ["attention.self.query", "attention.self.value", "attention.output.dense", "intermediate.dense", "output.dense"]}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:68536cf27db976e89cdf1dbb5b36f409932be494dd180020580b9d008d907605
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size 435697596
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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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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},
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"101": {
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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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"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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"single_word": false,
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"special": true
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},
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"103": {
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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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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"extra_special_tokens": {},
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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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vocab.txt
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