Add new CrossEncoder model

.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

README.md

@@ -0,0 +1,453 @@
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:1047
+- loss:MultipleNegativesRankingLoss
+base_model: Qwen/Qwen3-Embedding-0.6B
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- accuracy
+- accuracy_threshold
+- f1
+- f1_threshold
+- precision
+- recall
+- average_precision
+model-index:
+- name: CrossEncoder based on Qwen/Qwen3-Embedding-0.6B
+  results:
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: accuracy
+      value: 0.9389312977099237
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.726271390914917
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.9391634980988592
+      name: F1
+    - type: f1_threshold
+      value: 0.726271390914917
+      name: F1 Threshold
+    - type: precision
+      value: 0.9356060606060606
+      name: Precision
+    - type: recall
+      value: 0.9427480916030534
+      name: Recall
+    - type: average_precision
+      value: 0.9508539647615596
+      name: Average Precision
+    - type: accuracy
+      value: 0.9435975609756098
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.8168901205062866
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.944693572496263
+      name: F1
+    - type: f1_threshold
+      value: 0.7354934215545654
+      name: F1 Threshold
+    - type: precision
+      value: 0.9266862170087976
+      name: Precision
+    - type: recall
+      value: 0.9634146341463414
+      name: Recall
+    - type: average_precision
+      value: 0.9544295903264528
+      name: Average Precision
+---
+
+# CrossEncoder based on Qwen/Qwen3-Embedding-0.6B
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 -->
+- **Maximum Sequence Length:** 32768 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("vkimbris/qwen3_06b_items_reranker")
+# Get scores for pairs of texts
+pairs = [
+    ['Васаби порошок горчичный Премиум Fumiko Resfood 1кг, 10шт/кор, Кихай', 'Васаби Fumiko Premium  грейд А, 85% хрена'],
+    ['Соус Терияки Genso 1,5n/1,7кг, бшт/кор, Россия', 'Соус Терияки Genso'],
+    ['Уксус рисовый Padam Prem Resfood 20л, Россия', 'Уксус рисовый Padam Premium'],
+    ['Имбирь маринованный розовый Tabuko Restood 1,5 кг, вес сухого вещ-ва 1кг, 10шт/кор, Китай', 'Имбирь маринованный Tabuko розовый'],
+    ["Паста Том Ям 'Genso' пакет (0,400 кг) упак. 24 шт. Тайланд", 'Паста Том Ям Genso'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Васаби порошок горчичный Премиум Fumiko Resfood 1кг, 10шт/кор, Кихай',
+    [
+        'Васаби Fumiko Premium  грейд А, 85% хрена',
+        'Соус Терияки Genso',
+        'Уксус рисовый Padam Premium',
+        'Имбирь маринованный Tabuko розовый',
+        'Паста Том Ям Genso',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Cross Encoder Classification
+
+* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| accuracy              | 0.9389     |
+| accuracy_threshold    | 0.7263     |
+| f1                    | 0.9392     |
+| f1_threshold          | 0.7263     |
+| precision             | 0.9356     |
+| recall                | 0.9427     |
+| **average_precision** | **0.9509** |
+
+#### Cross Encoder Classification
+
+* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| accuracy              | 0.9436     |
+| accuracy_threshold    | 0.8169     |
+| f1                    | 0.9447     |
+| f1_threshold          | 0.7355     |
+| precision             | 0.9267     |
+| recall                | 0.9634     |
+| **average_precision** | **0.9544** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,047 training samples
+* Columns: <code>premise</code> and <code>hypothesis</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | premise                                                                                         | hypothesis                                                                                    |
+  |:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                        |
+  | details | <ul><li>min: 11 characters</li><li>mean: 49.72 characters</li><li>max: 107 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 27.71 characters</li><li>max: 62 characters</li></ul> |
+* Samples:
+  | premise                                                                                | hypothesis                                                                  |
+  |:---------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|
+  | <code>Смесь мучная темпурная 'KANESHIRO' 1кг</code>                                    | <code>Мука темпурная Kaneshiro</code>                                       |
+  | <code>Смесь темпурная Kaneshiro Resfood 1xr. 10шт/кор</code>                           | <code>Мука темпурная Kaneshiro</code>                                       |
+  | <code>Имбирь маринованный розовый 'Hansey' 1,4 кг*10 (в.с. КОРОБОК ПО 10 ПАЧЕК)</code> | <code>Имбирь маринованный розовый Hansey, вес сухого вещества 1000 г</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 10.0,
+      "num_negatives": 4,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 262 evaluation samples
+* Columns: <code>premise</code> and <code>hypothesis</code>
+* Approximate statistics based on the first 262 samples:
+  |         | premise                                                                                         | hypothesis                                                                                     |
+  |:--------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                         |
+  | details | <ul><li>min: 14 characters</li><li>mean: 50.15 characters</li><li>max: 111 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 26.98 characters</li><li>max: 62 characters</li></ul> |
+* Samples:
+  | premise                                                                           | hypothesis                                             |
+  |:----------------------------------------------------------------------------------|:-------------------------------------------------------|
+  | <code>Васаби порошок горчичный Премиум Fumiko Resfood 1кг, 10шт/кор, Кихай</code> | <code>Васаби Fumiko Premium  грейд А, 85% хрена</code> |
+  | <code>Соус Терияки Genso 1,5n/1,7кг, бшт/кор, Россия</code>                       | <code>Соус Терияки Genso</code>                        |
+  | <code>Уксус рисовый Padam Prem Resfood 20л, Россия</code>                         | <code>Уксус рисовый Padam Premium</code>               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 10.0,
+      "num_negatives": 4,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 15
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 15
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step | Training Loss | Validation Loss | average_precision |
+|:-------:|:----:|:-------------:|:---------------:|:-----------------:|
+| 1.5152  | 100  | 0.4864        | 0.1104          | 0.8944            |
+| 3.0303  | 200  | 0.1238        | 0.0983          | 0.9240            |
+| 4.5455  | 300  | 0.1106        | 0.0934          | 0.9466            |
+| 6.0606  | 400  | 0.1068        | 0.0939          | 0.9378            |
+| 7.5758  | 500  | 0.1135        | 0.1023          | 0.9232            |
+| 9.0909  | 600  | 0.1061        | 0.1187          | 0.9186            |
+| 10.6061 | 700  | 0.1074        | 0.0808          | 0.9445            |
+| 12.1212 | 800  | 0.1039        | 0.1153          | 0.9403            |
+| 13.6364 | 900  | 0.1082        | 0.0900          | 0.9509            |
+| -1      | -1   | -             | -               | 0.9544            |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.3
+- PyTorch: 2.9.1+cu128
+- Accelerate: 1.12.0
+- Datasets: 4.4.2
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|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
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,70 @@
+{
+  "architectures": [
+    "Qwen3ForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
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