Upload lora_intent_classifier_bert-base-uncased_model LoRA model

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- lora
+- semantic-router
+- intent-classification
+- text-classification
+- candle
+- rust
+language:
+- en
+pipeline_tag: text-classification
+library_name: candle
+---
+
+# lora_intent_classifier_bert-base-uncased_model
+
+## Model Description
+
+This is a LoRA (Low-Rank Adaptation) fine-tuned model based on **bert-base-uncased** for Intent Classification - Classifies text into categories like business, technology, science, etc..
+
+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.
+
+## Model Details
+
+- **Base Model**: bert-base-uncased
+- **Task**: Intent Classification
+- **Framework**: Candle (Rust)
+- **Model Size**: ~418MB
+- **LoRA Rank**: N/A
+- **LoRA Alpha**: N/A
+- **Target Modules**: 
+
+## Usage
+
+### With semantic-router (Recommended)
+
+```python
+from semantic_router import SemanticRouter
+
+# The model will be automatically downloaded and used
+router = SemanticRouter()
+results = router.classify_batch(["Your text here"])
+```
+
+### With Candle (Rust)
+
+```rust
+use candle_core::{Device, Tensor};
+use candle_transformers::models::bert::BertModel;
+
+// Load the model using Candle
+let device = Device::Cpu;
+let model = BertModel::load(&device, &config, &weights)?;
+```
+
+## Training Details
+
+This model was fine-tuned using LoRA (Low-Rank Adaptation) technique:
+
+- **Rank**: 16
+- **Alpha**: 32 
+- **Dropout**: 0.1
+- **Target Modules**: 
+
+## Performance
+
+Intent Classification - Classifies text into categories like business, technology, science, etc.
+
+For detailed performance metrics, see the [training results](https://github.com/vllm-project/semantic-router/blob/main/training-result.md).
+
+## Files
+
+- `model.safetensors`: LoRA adapter weights
+- `config.json`: Model configuration
+- `lora_config.json`: LoRA-specific configuration
+- `tokenizer.json`: Tokenizer configuration
+- `label_mapping.json`: Label mappings for classification
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{semantic-router-lora,
+  title={LoRA Fine-tuned Models for Semantic Router},
+  author={Semantic Router Team},
+  year={2025},
+  url={https://github.com/vllm-project/semantic-router}
+}
+```
+
+## License
+
+Apache 2.0

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "business",
+    "1": "law",
+    "2": "psychology"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "business": 0,
+    "law": 1,
+    "psychology": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

label_mapping.json

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+{
+  "category_to_idx": {
+    "business": 0,
+    "law": 1,
+    "psychology": 2
+  },
+  "idx_to_category": {
+    "0": "business",
+    "1": "law",
+    "2": "psychology"
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8890bf7cb4ddac2b91fa6fe86e46656ee3c6042c548dbba13306498a140d1df8
+size 437961724

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}