KayaTechAI/Qwen3-0.6B-Fine-Tuned-Telecom-Technical-Documents-Retrieval-Embedding-With-Config

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the telecom-technical-documents-retrieval-embedding-dataset dataset. 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
• Maximum Sequence Length: 32768 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • telecom-technical-documents-retrieval-embedding-dataset

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("KayaTechAI/Qwen3-0.6B-Fine-Tuned-Telecom-Technical-Documents-Retrieval-Embedding-With-Config")
# Run inference
queries = [
    "What is the provisioning scope for the eMLPP service?",
]
documents = [
    'eMLPP is provisioned per subscriber.',
    'The main objective is to verify that the User Equipment (UE) tracks channel variations and selects the optimal transport format for frequency non-selective scheduling.',
    'SDP is used in SIP communications to describe the parameters and media capabilities of a session, such as audio/video codecs, transport protocols, and IP addresses, enabling participants to agree on the media types to be used.',
]
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([[ 0.6303, -0.0008, -0.0340]])

Evaluation

Metrics

Information Retrieval

• Dataset: dim_1024
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 1024
  }
  

Metric	Value
cosine_accuracy@1	0.7988
cosine_accuracy@3	0.912
cosine_accuracy@5	0.9404
cosine_accuracy@10	0.9636
cosine_precision@1	0.7988
cosine_precision@3	0.304
cosine_precision@5	0.1881
cosine_precision@10	0.0964
cosine_recall@1	0.7988
cosine_recall@3	0.912
cosine_recall@5	0.9404
cosine_recall@10	0.9636
cosine_ndcg@10	0.886
cosine_mrr@10	0.8606
cosine_map@100	0.8621

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.7996
cosine_accuracy@3	0.9148
cosine_accuracy@5	0.9408
cosine_accuracy@10	0.9624
cosine_precision@1	0.7996
cosine_precision@3	0.3049
cosine_precision@5	0.1882
cosine_precision@10	0.0962
cosine_recall@1	0.7996
cosine_recall@3	0.9148
cosine_recall@5	0.9408
cosine_recall@10	0.9624
cosine_ndcg@10	0.8859
cosine_mrr@10	0.8608
cosine_map@100	0.8625

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.7968
cosine_accuracy@3	0.9128
cosine_accuracy@5	0.9388
cosine_accuracy@10	0.962
cosine_precision@1	0.7968
cosine_precision@3	0.3043
cosine_precision@5	0.1878
cosine_precision@10	0.0962
cosine_recall@1	0.7968
cosine_recall@3	0.9128
cosine_recall@5	0.9388
cosine_recall@10	0.962
cosine_ndcg@10	0.8844
cosine_mrr@10	0.8589
cosine_map@100	0.8606

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.7804
cosine_accuracy@3	0.912
cosine_accuracy@5	0.9316
cosine_accuracy@10	0.9584
cosine_precision@1	0.7804
cosine_precision@3	0.304
cosine_precision@5	0.1863
cosine_precision@10	0.0958
cosine_recall@1	0.7804
cosine_recall@3	0.912
cosine_recall@5	0.9316
cosine_recall@10	0.9584
cosine_ndcg@10	0.8753
cosine_mrr@10	0.848
cosine_map@100	0.8496

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.7696
cosine_accuracy@3	0.898
cosine_accuracy@5	0.9268
cosine_accuracy@10	0.9524
cosine_precision@1	0.7696
cosine_precision@3	0.2993
cosine_precision@5	0.1854
cosine_precision@10	0.0952
cosine_recall@1	0.7696
cosine_recall@3	0.898
cosine_recall@5	0.9268
cosine_recall@10	0.9524
cosine_ndcg@10	0.8663
cosine_mrr@10	0.8381
cosine_map@100	0.8399

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.75
cosine_accuracy@3	0.8816
cosine_accuracy@5	0.9124
cosine_accuracy@10	0.9456
cosine_precision@1	0.75
cosine_precision@3	0.2939
cosine_precision@5	0.1825
cosine_precision@10	0.0946
cosine_recall@1	0.75
cosine_recall@3	0.8816
cosine_recall@5	0.9124
cosine_recall@10	0.9456
cosine_ndcg@10	0.8522
cosine_mrr@10	0.8218
cosine_map@100	0.8236

Training Details

Training Dataset

telecom-technical-documents-retrieval-embedding-dataset

• Dataset: telecom-technical-documents-retrieval-embedding-dataset at 3ebf34a
• Size: 127,731 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 18.79 tokens max: 68 tokens	min: 4 tokens mean: 26.09 tokens max: 77 tokens

• Samples:

  anchor	positive
  What is the estimated Transmit power considered sufficient for achieving 95% Downlink coverage with a single Base Station?	Approximately 14 dBm Transmit power is considered sufficient.
  What is the primary goal of the Nominal Accuracy requirement?	The primary goal of the Nominal Accuracy requirement is to ensure good accuracy when signal conditions are ideal.
  What happens on the mobile station side if contention resolution fails because the G-RNTI value in the network's acknowledgement message differs from what the mobile station sent?	If the mobile station receives a PACKET UPLINK ACK/NACK message with a G-RNTI value different from the one it included in its first RLC data blocks, it signifies a contention resolution failure, and the mobile station will not transmit a PACKET CONTROL ACKNOWLEDGEMENT.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• 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: 4
• max_steps: -1
• lr_scheduler_type: cosine
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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: True
• 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}
• 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: {}

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.0401	10	1.5256	-	-	-	-	-	-
0.0802	20	0.8247	-	-	-	-	-	-
0.1202	30	0.4102	-	-	-	-	-	-
0.1603	40	0.27	-	-	-	-	-	-
0.2004	50	0.2182	-	-	-	-	-	-
0.2405	60	0.1998	-	-	-	-	-	-
0.2806	70	0.2017	-	-	-	-	-	-
0.3206	80	0.1672	-	-	-	-	-	-
0.3607	90	0.2029	-	-	-	-	-	-
0.4008	100	0.1609	-	-	-	-	-	-
0.4409	110	0.1565	-	-	-	-	-	-
0.4810	120	0.1476	-	-	-	-	-	-
0.5210	130	0.1278	-	-	-	-	-	-
0.5611	140	0.1669	-	-	-	-	-	-
0.6012	150	0.1642	-	-	-	-	-	-
0.6413	160	0.1307	-	-	-	-	-	-
0.6814	170	0.1487	-	-	-	-	-	-
0.7214	180	0.1329	-	-	-	-	-	-
0.7615	190	0.13	-	-	-	-	-	-
0.8016	200	0.1393	-	-	-	-	-	-
0.8417	210	0.1344	-	-	-	-	-	-
0.8818	220	0.1184	-	-	-	-	-	-
0.9218	230	0.1147	-	-	-	-	-	-
0.9619	240	0.1283	-	-	-	-	-	-
1.0	250	0.1228	0.8693	0.8683	0.8634	0.8535	0.8430	0.8082
1.0401	260	0.0613	-	-	-	-	-	-
1.0802	270	0.0559	-	-	-	-	-	-
1.1202	280	0.0704	-	-	-	-	-	-
1.1603	290	0.0578	-	-	-	-	-	-
1.2004	300	0.0588	-	-	-	-	-	-
1.2405	310	0.079	-	-	-	-	-	-
1.2806	320	0.0602	-	-	-	-	-	-
1.3206	330	0.0553	-	-	-	-	-	-
1.3607	340	0.0663	-	-	-	-	-	-
1.4008	350	0.0513	-	-	-	-	-	-
1.4409	360	0.0615	-	-	-	-	-	-
1.4810	370	0.0462	-	-	-	-	-	-
1.5210	380	0.0674	-	-	-	-	-	-
1.5611	390	0.0558	-	-	-	-	-	-
1.6012	400	0.0562	-	-	-	-	-	-
1.6413	410	0.0688	-	-	-	-	-	-
1.6814	420	0.0905	-	-	-	-	-	-
1.7214	430	0.0463	-	-	-	-	-	-
1.7615	440	0.0581	-	-	-	-	-	-
1.8016	450	0.0586	-	-	-	-	-	-
1.8417	460	0.0712	-	-	-	-	-	-
1.8818	470	0.041	-	-	-	-	-	-
1.9218	480	0.0578	-	-	-	-	-	-
1.9619	490	0.063	-	-	-	-	-	-
2.0	500	0.0505	0.8771	0.8780	0.8764	0.8690	0.8587	0.8353
2.0401	510	0.032	-	-	-	-	-	-
2.0802	520	0.0239	-	-	-	-	-	-
2.1202	530	0.029	-	-	-	-	-	-
2.1603	540	0.0236	-	-	-	-	-	-
2.2004	550	0.0381	-	-	-	-	-	-
2.2405	560	0.028	-	-	-	-	-	-
2.2806	570	0.0366	-	-	-	-	-	-
2.3206	580	0.0372	-	-	-	-	-	-
2.3607	590	0.0306	-	-	-	-	-	-
2.4008	600	0.0294	-	-	-	-	-	-
2.4409	610	0.0269	-	-	-	-	-	-
2.4810	620	0.0411	-	-	-	-	-	-
2.5210	630	0.0251	-	-	-	-	-	-
2.5611	640	0.0299	-	-	-	-	-	-
2.6012	650	0.0275	-	-	-	-	-	-
2.6413	660	0.0267	-	-	-	-	-	-
2.6814	670	0.0304	-	-	-	-	-	-
2.7214	680	0.0246	-	-	-	-	-	-
2.7615	690	0.025	-	-	-	-	-	-
2.8016	700	0.037	-	-	-	-	-	-
2.8417	710	0.0393	-	-	-	-	-	-
2.8818	720	0.0405	-	-	-	-	-	-
2.9218	730	0.0279	-	-	-	-	-	-
2.9619	740	0.0243	-	-	-	-	-	-
3.0	750	0.0284	0.8870	0.8858	0.8827	0.8745	0.8648	0.8499
3.0401	760	0.0166	-	-	-	-	-	-
3.0802	770	0.024	-	-	-	-	-	-
3.1202	780	0.0302	-	-	-	-	-	-
3.1603	790	0.0263	-	-	-	-	-	-
3.2004	800	0.0172	-	-	-	-	-	-
3.2405	810	0.023	-	-	-	-	-	-
3.2806	820	0.0313	-	-	-	-	-	-
3.3206	830	0.0253	-	-	-	-	-	-
3.3607	840	0.0189	-	-	-	-	-	-
3.4008	850	0.0177	-	-	-	-	-	-
3.4409	860	0.0187	-	-	-	-	-	-
3.4810	870	0.0142	-	-	-	-	-	-
3.5210	880	0.0281	-	-	-	-	-	-
3.5611	890	0.0253	-	-	-	-	-	-
3.6012	900	0.0184	-	-	-	-	-	-
3.6413	910	0.0217	-	-	-	-	-	-
3.6814	920	0.027	-	-	-	-	-	-
3.7214	930	0.0192	-	-	-	-	-	-
3.7615	940	0.0183	-	-	-	-	-	-
3.8016	950	0.0242	-	-	-	-	-	-
3.8417	960	0.0223	-	-	-	-	-	-
3.8818	970	0.0161	-	-	-	-	-	-
3.9218	980	0.0219	-	-	-	-	-	-
3.9619	990	0.0236	-	-	-	-	-	-
4.0	1000	0.0278	0.886	0.8859	0.8844	0.8753	0.8663	0.8522

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.3
• Transformers: 4.55.4
• PyTorch: 2.10.0+cu128
• Accelerate: 1.12.0
• Datasets: 3.6.0
• Tokenizers: 0.21.4

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}