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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 2560,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": true,
+    "include_prompt": true
+}

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -0,0 +1,5 @@
+JevEmbed-Qwen3-Embedding-4B
+Based on Qwen/Qwen3-Embedding-4B, licensed under Apache License 2.0.
+Qwen source model: https://huggingface.co/Qwen/Qwen3-Embedding-4B
+The LoRA adapter was trained on HIT-TMG/JevEmbed-Data and merged into the base model.
+Dataset source licenses vary; see the dataset source report.

README.md

@@ -1,3 +1,47 @@
 ---
 license: apache-2.0
+base_model: Qwen/Qwen3-Embedding-4B
+base_model_relation: merge
+datasets:
+- HIT-TMG/JevEmbed-Data
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- jevembed
+- lora
 ---
+
+# JevEmbed-Qwen3-Embedding-4B
+
+JevEmbed-Qwen3-Embedding-4B is a fine-tuned version of [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) for [JevEmbed](https://github.com/HITsz-TMG/JevEmbed) Choice, Score, and Noul decisions. The LoRA adapter is merged into a standalone Sentence Transformers model; the adapter is also available in [`lora/`](lora/). Embeddings have 2,560 dimensions and use last-token pooling and normalization. Tested with `transformers==4.51.0` and `sentence-transformers==5.3.0`.
+
+## Use with JevEmbed
+
+Install JevEmbed with its local-model dependencies, download [`jevembed.yaml`](jevembed.yaml), and pass your own request JSON to the CLI:
+
+```bash
+python -m pip install 'jevembed[local] @ git+https://github.com/HITsz-TMG/JevEmbed.git'
+python -m jevembed --config jevembed.yaml --input /path/to/your/request.json
+```
+
+The weights download automatically from `HIT-TMG/JevEmbed-Qwen3-Embedding-4B`. If the request has a `model` field, use `jevembed-qwen3-embedding-4b` or the full Hugging Face ID. To use weights already on disk, set `model_name_or_path` to that directory and `local_files_only: true` in the YAML. The configuration uses 1,024-token truncation, Choice/Score temperature 0.1, and Noul slope 10. Raw embeddings can be loaded with `SentenceTransformer("HIT-TMG/JevEmbed-Qwen3-Embedding-4B")`; JevEmbed applies the task prompts and scoring.
+
+## Test-set performance
+
+Qwen3-Embedding-4B and the final JevEmbed LoRA checkpoint were evaluated on all 66,482 questions in the [JevEmbed-Data](https://huggingface.co/datasets/HIT-TMG/JevEmbed-Data) test split. These results are from the final LoRA checkpoint before merging. Both runs used BF16, identical JevEmbed prompts and scoring, and 1,024-token truncation. Accuracy uses hard labels; MAE also includes soft targets where present.
+
+| Metric | Base | Final LoRA checkpoint | Change |
+| --- | ---: | ---: | ---: |
+| Overall hard-label accuracy (64,110) | 36.29% | 85.86% | +49.57 pp |
+| Choice accuracy (17,487) | 38.11% | 90.31% | +52.20 pp |
+| Score level accuracy (24,260) | 30.55% | 73.41% | +42.86 pp |
+| Noul binary accuracy (22,363) | 41.09% | 95.88% | +54.78 pp |
+| Score MAE (24,287; lower is better) | 0.9211 | 0.3628 | -0.5583 |
+| Noul MAE (24,004; lower is better) | 0.5895 | 0.0689 | -0.5206 |
+
+## Training
+
+Training ran for one epoch on the 1,601,157-question JevEmbed-Data training split. The final checkpoint is step **3,127**. Training used 16 GPUs across four nodes, per-GPU batch 4, gradient accumulation 8 (effective batch 512), BF16, LoRA rank 64, alpha 32, dropout 0.05, and Q/K/V projection targets. The learning rate was 2 × 10⁻⁴ with 10% warmup. Inputs were truncated at 1,024 tokens. The training objective is described in the [JevEmbed implementation](https://github.com/HITsz-TMG/JevEmbed/blob/main/src/jevembed/training/objective.py).
+
+The source Qwen model is Apache 2.0 licensed. Training-data source licenses vary; see the dataset's [source report](https://huggingface.co/datasets/HIT-TMG/JevEmbed-Data/blob/main/docs/PROCESSING_REPORT.md).

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "Qwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "4.51.0",
+    "pytorch": "2.8.0+cu129"
+  }
+}

jevembed.yaml

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+model_id: jevembed-qwen3-embedding-4b
+backend: sentence_transformers
+model_name_or_path: HIT-TMG/JevEmbed-Qwen3-Embedding-4B
+aliases:
+- HIT-TMG/JevEmbed-Qwen3-Embedding-4B
+- JevEmbed-Qwen3-Embedding-4B
+trust_remote_code: false
+local_files_only: false
+device: auto
+dtype: auto
+batch_size: 8
+pooling: model_default
+normalize_embeddings: true
+expected_dimension: 2560
+max_input_tokens: 1024
+overflow_policy: truncate
+attention_implementation: sdpa
+prompts:
+  query_template: 'Instruct: {instruction}
+
+    Query: {text}'
+  document_template: '{text}'
+  similarity_instruction: Retrieve semantically similar text.
+scoring:
+  choice_temperature: 0.1
+  score_temperature: 0.1
+  noul_with_criteria:
+    slope: 10.0
+    intercept: 0.0
+  noul_without_criteria:
+    slope: 10.0
+    intercept: 0.0
+  calibration_status: uncalibrated

lora/adapter_config.json

@@ -0,0 +1,42 @@
+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-Embedding-4B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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+}