---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:18851
- loss:MultipleNegativesRankingLoss
base_model: microsoft/harrier-oss-v1-0.6b
widget:
- source_sentence: Salt-grilled Boneless Galbi
  sentences:
  - 겉절이(1kg)
  - 경포대밥상(떡갈비)(1인)
  - 갈비살소금구이
- source_sentence: 1인세트마늘탕수육와 쟁반짜장
  sentences:
  - 가리비전복숙회
  - C세트
  - 1인세트마늘탕수육+쟁반짜장
- source_sentence: Set Menu A (M, 3 Servings) - Braised Boneless Cutlassfish + Grilled
    Whole Cutlassfish
  sentences:
  - SET-A(중3인)순살갈치조림+통갈치구이
  - 갈치구이(중)
  - 가브리살(200g)
- source_sentence: 갈릭쉬림프피자 (M)
  sentences:
  - 경주법주생막걸리
  - 갈릭쉬림프피자(M)
  - 계란(1개)
- source_sentence: Braised Cutlassfish Set Menu
  sentences:
  - 갈치조림세트
  - 1인세트마늘탕수육+새우볶음밥
  - 1L보틀아메리카노(ICE)
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on microsoft/harrier-oss-v1-0.6b

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/harrier-oss-v1-0.6b](https://huggingface.co/microsoft/harrier-oss-v1-0.6b). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/harrier-oss-v1-0.6b](https://huggingface.co/microsoft/harrier-oss-v1-0.6b) <!-- at revision f9b9dc8d367d443f2479d27aa5d8d2850c0774ee -->
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True})
  (2): Normalize({})
)
```

## 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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Voctree/harrier-oss-v1-0.6b-finetuned")
# Run inference
sentences = [
    'Braised Cutlassfish Set Menu',
    '갈치조림세트',
    '1L보틀아메리카노(ICE)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5813, -0.1134],
#         [ 0.5813,  1.0000,  0.0059],
#         [-0.1134,  0.0059,  1.0000]])
```
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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 18,851 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 100 samples:
  |          | anchor                                                                            | positive                                                                          |
  |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type     | string                                                                            | string                                                                            |
  | modality | text                                                                              | text                                                                              |
  | details  | <ul><li>min: 3 tokens</li><li>mean: 12.14 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.44 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
  | anchor                                       | positive                   |
  |:---------------------------------------------|:---------------------------|
  | <code>Yemen Mocha Mattari (Signature)</code> | <code>*시그니처*예멘모카마타리</code> |
  | <code>시그니처예멘모카마타리</code>                     | <code>*시그니처*예멘모카마타리</code> |
  | <code>With Ice</code>                        | <code>+아이스</code>          |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 32
- `learning_rate`: 2e-05
- `warmup_steps`: 0.1
- `bf16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `per_device_train_batch_size`: 32
- `num_train_epochs`: 3
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `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
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.3390 | 200  | 0.6930        |
| 0.6780 | 400  | 0.1709        |
| 1.0169 | 600  | 0.1044        |
| 1.3559 | 800  | 0.0650        |
| 1.6949 | 1000 | 0.0613        |
| 2.0339 | 1200 | 0.0557        |
| 2.3729 | 1400 | 0.0407        |
| 2.7119 | 1600 | 0.0406        |


### Training Time
- **Training**: 30.8 minutes

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.5.1
- Transformers: 5.10.1
- PyTorch: 2.11.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

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