qwen3-ai-faq-embeddings-v2

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Qwen/Qwen3-Embedding-0.6B <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 -->
• Maximum Sequence Length: 32768 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kevinadityai/qwen3-ai-faq-embeddings-v2")
# Run inference
queries = [
    "Alamat SHSR dimana?",
]
documents = [
    'Alamat SHSR dimana? Lokasinya di Jl. Kompol Maksum No.296, Peterongan, Kec. Semarang Sel., Kota Semarang, Jawa Tengah 50242',
    'Rumah sakit Siloam Hospitals Jambi punya nama lain apa? Nama lain Siloam Hospitals Jambi adalah SHJB atau Siloam Jambi Selatan',
    'Ketersediaan prosedur pada rumah sakit Anda dapat langsung mengecek pada website / aplikasi MySiloam untuk ketersediaan dari masing-masing prosedur di rumah sakit. Jika informasi yang Anda butuhkan tidak tersedia di website / aplikasi MySiloam, Anda dapat langsung menghubungi contact center 1-500-911.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6989, 0.0864, 0.0848]])

<!-- ### 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

Triplet

• Datasets: ai-faq-validation and ai-faq-test
• Evaluated with <code>TripletEvaluator</code>

<!-- ## 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: 304 training samples
• Columns: <code>query</code>, <code>answer_positive</code>, and <code>answer_negative</code>
• Approximate statistics based on the first 304 samples:

  	query	answer_positive	answer_negative
  type	string	string	string
  details	<ul><li>min: 3 tokens</li><li>mean: 13.72 tokens</li><li>max: 54 tokens</li></ul>	<ul><li>min: 19 tokens</li><li>mean: 55.18 tokens</li><li>max: 552 tokens</li></ul>	<ul><li>min: 19 tokens</li><li>mean: 55.18 tokens</li><li>max: 552 tokens</li></ul>

• Samples:

  query	answer_positive	answer_negative
  <code>Apa nama lain Siloam Kuta?</code>	<code>Apa nama lain Siloam Kuta? Nama lain Siloam Kuta adalah Siloam Hospitals Denpasar atau SHDP</code>	<code>Jam operasional untuk konsultasi BPJS? Jadwal operasional untuk pasien BPJS Kesehatan berbeda tergantung Siloam rumah sakit tertentu. Pasien disarankan untuk melakukan pendaftaran sebelum hari H kedatangan atau pada pagi harinya. Untuk jadwal pasti, dapat langsung menghubungi nomor kontak Rumah Sakit Siloam Hospitals yang dituju atau menghubungi contact center kami di 1-500-911.</code>
  <code>Rumah sakit Siloam Hospitals Banjarmasin punya nama lain apa?</code>	<code>Rumah sakit Siloam Hospitals Banjarmasin punya nama lain apa? Nama lain Siloam Hospitals Banjarmasin adalah SHBJ atau Siloam Banjarmasin Tengah</code>	<code>Alamat SHLV dimana? Lokasinya di Jl. Siloam Lippo Karawaci No.6, Bencongan, Kec. Klp. Dua, Kabupaten Tangerang, Banten 15810</code>
  <code>Apa itu MRCCC?</code>	<code>Apa itu MRCCC? MRCCC Siloam Hospitals Semanggi adalah kepanjangan dari MRCCC</code>	<code>Alamat SHAB dimana? Lokasinya di Jl. Sultan Hasanudin No.Kel, Hative Kecil, Kec. Sirimau, Kota Ambon, Maluku, 97118, Indonesia</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 30 evaluation samples
• Columns: <code>query</code>, <code>answer_positive</code>, and <code>answer_negative</code>
• Approximate statistics based on the first 30 samples:

  	query	answer_positive	answer_negative
  type	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 13.7 tokens</li><li>max: 24 tokens</li></ul>	<ul><li>min: 21 tokens</li><li>mean: 50.13 tokens</li><li>max: 123 tokens</li></ul>	<ul><li>min: 22 tokens</li><li>mean: 59.87 tokens</li><li>max: 170 tokens</li></ul>

• Samples:

  query	answer_positive	answer_negative
  <code>Apa nama lain Siloam Kuta?</code>	<code>Apa nama lain Siloam Kuta? Nama lain Siloam Kuta adalah Siloam Hospitals Denpasar atau SHDP</code>	<code>Jam operasional untuk konsultasi BPJS? Jadwal operasional untuk pasien BPJS Kesehatan berbeda tergantung Siloam rumah sakit tertentu. Pasien disarankan untuk melakukan pendaftaran sebelum hari H kedatangan atau pada pagi harinya. Untuk jadwal pasti, dapat langsung menghubungi nomor kontak Rumah Sakit Siloam Hospitals yang dituju atau menghubungi contact center kami di 1-500-911.</code>
  <code>Rumah sakit Siloam Hospitals Banjarmasin punya nama lain apa?</code>	<code>Rumah sakit Siloam Hospitals Banjarmasin punya nama lain apa? Nama lain Siloam Hospitals Banjarmasin adalah SHBJ atau Siloam Banjarmasin Tengah</code>	<code>Alamat SHLV dimana? Lokasinya di Jl. Siloam Lippo Karawaci No.6, Bencongan, Kec. Klp. Dua, Kabupaten Tangerang, Banten 15810</code>
  <code>Apa itu MRCCC?</code>	<code>Apa itu MRCCC? MRCCC Siloam Hospitals Semanggi adalah kepanjangan dari MRCCC</code>	<code>Alamat SHAB dimana? Lokasinya di Jl. Sultan Hasanudin No.Kel, Hative Kecil, Kec. Sirimau, Kota Ambon, Maluku, 97118, Indonesia</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• batch_sampler: no_duplicates

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: 8
• per_device_eval_batch_size: 8
• 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: 1
• 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
• use_ipex: False
• bf16: False
• fp16: False
• 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
• 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: False
• 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: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.12.10
• Sentence Transformers: 5.1.1
• Transformers: 4.56.2
• PyTorch: 2.8.0+cpu
• Accelerate: 1.10.1
• Datasets: 4.1.1
• Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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