assesment_qwen3_embedding_06b_e3

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("sentence_transformers_model_id")
# Run inference
queries = [
    "hyperswitch router utility functions error handling",
]
documents = [
    '// PATH: data/code_corpus_hyperswitch/crates__router__src__utils.rs\n// MODULE: data::code_corpus_hyperswitch::crates__router__src__utils.rs\n// SYMBOL: get_error_response\n    fn get_error_response(self) -> RouterResult<types::Response> {\n        self.map_err(|error| error.change_context(errors::ApiErrorResponse::InternalServerError))\n            .attach_printable("Error while receiving response")\n            .and_then(|inner| match inner {\n                Ok(res) => {\n                    logger::error!(response=?res);\n                    Err(errors::ApiErrorResponse::InternalServerError).attach_printable(format!(\n                        "Expecting error response, received response: {res:?}"\n                    ))\n                }\n                Err(err_res) => Ok(err_res),\n            })\n    }',
    '// PATH: data/code_corpus_hyperswitch/crates__hyperswitch_domain_models__src__router_data.rs\n// MODULE: data::code_corpus_hyperswitch::crates__hyperswitch_domain_models__src__router_data.rs\n// SYMBOL: CustomerInfo\npub struct CustomerInfo {\n    pub customer_id: Option<id_type::CustomerId>,\n    pub customer_email: Option<common_utils::pii::Email>,\n    pub customer_name: Option<Secret<String>>,\n    pub customer_phone_number: Option<Secret<String>>,\n    pub customer_phone_country_code: Option<String>,\n}\n#[derive(Debug, Clone, Serialize, Deserialize)]',
    '// PATH: data/code_corpus_hyperswitch/crates__hyperswitch_connectors__src__connectors__zift.rs\n// MODULE: data::code_corpus_hyperswitch::crates__hyperswitch_connectors__src__connectors__zift.rs\n// SYMBOL: build_request\n    fn build_request(\n        &self,\n        req: &SetupMandateRouterData,\n        connectors: &Connectors,\n    ) -> CustomResult<Option<Request>, errors::ConnectorError> {\n        Ok(Some(\n            RequestBuilder::new()\n                .method(Method::Post)\n                .url(&types::SetupMandateType::get_url(self, req, connectors)?)\n                .attach_default_headers()\n                .headers(types::SetupMandateType::get_headers(self, req, connectors)?)\n                .set_body(types::SetupMandateType::get_request_body(\n                    self, req, connectors,\n                )?)\n                .build(),\n        ))\n    }',
]
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([[ 0.4434, -0.0422, -0.0713]], dtype=torch.bfloat16)

<!-- ### 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.* --> <!-- ## 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: 1,736 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 10.85 tokens</li><li>max: 18 tokens</li></ul>	<ul><li>min: 100 tokens</li><li>mean: 225.13 tokens</li><li>max: 854 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>how to override proxy settings in session state</code>	<code>// PATH: data/code_corpus_hyperswitch/crates__router__src__core__unified_connector_service.rs<br>// MODULE: data::code_corpus_hyperswitch::crates__router__src__core__unified_connector_service.rs<br>// SYMBOL: create_updated_session_state_with_proxy<br>fn create_updated_session_state_with_proxy(<br> state: SessionState,<br> proxy_override: &ProxyOverride,<br>) -> SessionState {<br> let mut updated_state = state;<br><br> // Create updated configuration with proxy overrides<br> let mut updated_conf = (*updated_state.conf).clone();<br><br> // Update proxy URLs with overrides, falling back to existing values<br> if let Some(ref http_url) = proxy_override.http_url {<br> updated_conf.proxy.http_url = Some(http_url.clone());<br> }<br> if let Some(ref https_url) = proxy_override.https_url {<br> updated_conf.proxy.https_url = Some(https_url.clone());<br> }<br><br> updated_state.conf = std::sync::Arc::new(updated_conf);<br><br> updated_state<br>}</code>
  <code>rust hyperswitch analytics clip_to_start time bucket function</code>	<code>// PATH: data/code_corpus_hyperswitch/crates__analytics__src__query.rs<br>// MODULE: data::code_corpus_hyperswitch::crates__analytics__src__query.rs<br>// SYMBOL: clip_to_start<br> fn clip_to_start(<br> &self,<br> value: Self::SeriesType,<br> ) -> error_stack::Result<Self::SeriesType, PostProcessingError> {<br> let clip_start = |value: u8, modulo: u8| -> u8 { value - value % modulo };<br><br> let clipped_time = match (<br> self.get_lowest_common_granularity_level(),<br> self.get_bucket_size(),<br> ) {<br> (TimeGranularityLevel::Minute, i) => time::Time::MIDNIGHT<br> .replace_second(clip_start(value.second(), i))<br> .and_then(|t| t.replace_minute(value.minute()))<br> .and_then(|t| t.replace_hour(value.hour())),<br> (TimeGranularityLevel::Hour, i) => time::Time::MIDNIGHT<br> .replace_minute(clip_start(value.minute(), i))<br> .and_then(|t| t.replace_hour(value.hour())),<br> (Tim...</code>
  <code>rust function to get payment method type from domain models</code>	<code>// PATH: data/code_corpus_hyperswitch/crates__hyperswitch_domain_models__src__payment_method_data.rs<br>// MODULE: data::code_corpus_hyperswitch::crates__hyperswitch_domain_models__src__payment_method_data.rs<br>// SYMBOL: get_payment_method<br> pub fn get_payment_method(&self) -> Option<common_enums::PaymentMethod> {<br> match self {<br> Self::Card()<br> | Self::NetworkToken()<br> | Self::CardDetailsForNetworkTransactionId()<br> | Self::NetworkTokenDetailsForNetworkTransactionId()<br> | Self::DecryptedWalletTokenDetailsForNetworkTransactionId()<br> | Self::CardWithLimitedDetails() => Some(common_enums::PaymentMethod::Card),<br> Self::CardRedirect() => Some(common_enums::PaymentMethod::CardRedirect),<br> Self::Wallet() => Some(common_enums::PaymentMethod::Wallet),<br> Self::PayLater() => Some(common_enums::PaymentMethod::PayLater),<br> Self::BankRedirect() => Some(common_enums::PaymentMethod::BankRe...</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• per_device_train_batch_size: 8
• num_train_epochs: 3
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• 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
• label_smoothing_factor: 0.0
• bf16: False
• 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: trackio
• eval_strategy: no
• 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_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• 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: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.2.3
• Transformers: 5.2.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.12.0
• Datasets: 4.5.0
• Tokenizers: 0.22.2

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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