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Voctree/harrier-oss-v1-0.6b-finetuned

sourceHugging Faceupdated 4mo agoView on Hugging Face

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Model Card

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

This is a sentence-transformers model finetuned from 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
—Maximum Sequence Length: 32768 tokens
—Output Dimensionality: 1024 dimensions
—Similarity Function: Cosine Similarity
—Supported Modality: Text

Model Sources

—Documentation: Sentence Transformers Documentation
—Repository: Sentence Transformers on GitHub
—Hugging Face: Sentence Transformers on Hugging Face

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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Downstream Usage (Sentence Transformers)

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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>시그니처예멘모카마타리</code> | | <code>시그니처예멘모카마타리</code> | <code>시그니처예멘모카마타리</code> | | <code>With Ice</code> | <code>+아이스</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> 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: adamwtorchfused
—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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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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