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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:725795
	- loss:MultipleNegativesRankingLoss
	base_model: Qwen/Qwen3-0.6B-Base
	widget:
	- source_sentence: What swims to female reproductive organs for fertilization?
	sentences:
	- '93.2'
	- male gametes
	- Quadrangular membrane
	- source_sentence: Items are all ultimately compromised of which?
	sentences:
	- triplets
	- Molecules
	- 2.5 cm
	- source_sentence: Which one of the following statements about chromatin is not true?
	sentences:
	- multicellular
	- Maple syrup urine disease
	- H2A-H2B bind to both the entry and exit ends of DNA in nucleosomes
	- source_sentence: 'Widal test is an example of.......... Test.:'
	sentences:
	- Agglutination
	- water
	- 150 m
	- source_sentence: The ratio of an object's mass to its volume is its
	sentences:
	- density.
	- 500 m
	- Oculocardiac reflex
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on Qwen/Qwen3-0.6B-Base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) <!-- at revision 11214f7f3465775dcce23c3752ecea5a42ee0ddc -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

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

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: Qwen3Model
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## 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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	"The ratio of an object's mass to its volume is its",
	'density.',
	'500 m',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 725,795 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: 3 tokens</li><li>mean: 36.99 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 1 tokens</li><li>mean: 4.56 tokens</li><li>max: 34 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------\|
	\| <code>A balance can measure the weight of</code> \| <code>sugar</code> \|
	\| <code>The average monthly salary of 20 employees in an organisation is Rs. 1500. If the manager's salary is added, then the average salary increases by Rs. 100. What is the manager's monthly salary?</code> \| <code>Rs.3600</code> \|
	\| <code>When a baby shakes a rattle, it makes a noise. Which form of energy was changed to sound energy?</code> \| <code>mechanical</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"
	}
	```

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

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `fp16`: True
	- `multi_dataset_batch_sampler`: round_robin

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: True
	- `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}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `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
	- `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
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0110 \| 500 \| 1.3593 \|
	\| 0.0220 \| 1000 \| 0.8335 \|
	\| 0.0331 \| 1500 \| 0.7774 \|
	\| 0.0441 \| 2000 \| 0.7507 \|
	\| 0.0551 \| 2500 \| 0.7108 \|
	\| 0.0661 \| 3000 \| 0.6946 \|
	\| 0.0772 \| 3500 \| 0.6644 \|
	\| 0.0882 \| 4000 \| 0.621 \|
	\| 0.0992 \| 4500 \| 0.6124 \|
	\| 0.1102 \| 5000 \| 0.576 \|
	\| 0.1212 \| 5500 \| 0.5787 \|
	\| 0.1323 \| 6000 \| 0.5502 \|
	\| 0.1433 \| 6500 \| 0.5653 \|
	\| 0.1543 \| 7000 \| 0.5315 \|
	\| 0.1653 \| 7500 \| 0.5198 \|
	\| 0.1764 \| 8000 \| 0.5114 \|
	\| 0.1874 \| 8500 \| 0.4775 \|
	\| 0.1984 \| 9000 \| 0.4803 \|
	\| 0.2094 \| 9500 \| 0.4876 \|
	\| 0.2204 \| 10000 \| 0.4824 \|
	\| 0.2315 \| 10500 \| 0.4587 \|
	\| 0.2425 \| 11000 \| 0.4521 \|
	\| 0.2535 \| 11500 \| 0.4565 \|
	\| 0.2645 \| 12000 \| 0.448 \|
	\| 0.2756 \| 12500 \| 0.4475 \|
	\| 0.2866 \| 13000 \| 0.4313 \|
	\| 0.2976 \| 13500 \| 0.4226 \|
	\| 0.3086 \| 14000 \| 0.4079 \|
	\| 0.3196 \| 14500 \| 0.3869 \|
	\| 0.3307 \| 15000 \| 0.4001 \|
	\| 0.3417 \| 15500 \| 0.3815 \|
	\| 0.3527 \| 16000 \| 0.3769 \|
	\| 0.3637 \| 16500 \| 0.3526 \|
	\| 0.3748 \| 17000 \| 0.3839 \|
	\| 0.3858 \| 17500 \| 0.3647 \|
	\| 0.3968 \| 18000 \| 0.3616 \|
	\| 0.4078 \| 18500 \| 0.3615 \|
	\| 0.4188 \| 19000 \| 0.3592 \|
	\| 0.4299 \| 19500 \| 0.322 \|
	\| 0.4409 \| 20000 \| 0.3352 \|
	\| 0.4519 \| 20500 \| 0.3228 \|
	\| 0.4629 \| 21000 \| 0.3213 \|
	\| 0.4740 \| 21500 \| 0.3129 \|
	\| 0.4850 \| 22000 \| 0.3086 \|
	\| 0.4960 \| 22500 \| 0.3011 \|
	\| 0.5070 \| 23000 \| 0.3112 \|
	\| 0.5180 \| 23500 \| 0.308 \|
	\| 0.5291 \| 24000 \| 0.3002 \|
	\| 0.5401 \| 24500 \| 0.2805 \|
	\| 0.5511 \| 25000 \| 0.2809 \|
	\| 0.5621 \| 25500 \| 0.2666 \|
	\| 0.5732 \| 26000 \| 0.2772 \|
	\| 0.5842 \| 26500 \| 0.2783 \|
	\| 0.5952 \| 27000 \| 0.2704 \|
	\| 0.6062 \| 27500 \| 0.2696 \|
	\| 0.6172 \| 28000 \| 0.2667 \|
	\| 0.6283 \| 28500 \| 0.2561 \|
	\| 0.6393 \| 29000 \| 0.2546 \|
	\| 0.6503 \| 29500 \| 0.2491 \|
	\| 0.6613 \| 30000 \| 0.2405 \|
	\| 0.6724 \| 30500 \| 0.2376 \|
	\| 0.6834 \| 31000 \| 0.2236 \|
	\| 0.6944 \| 31500 \| 0.246 \|
	\| 0.7054 \| 32000 \| 0.2418 \|
	\| 0.7164 \| 32500 \| 0.2271 \|
	\| 0.7275 \| 33000 \| 0.2308 \|
	\| 0.7385 \| 33500 \| 0.2162 \|
	\| 0.7495 \| 34000 \| 0.2135 \|
	\| 0.7605 \| 34500 \| 0.2157 \|
	\| 0.7716 \| 35000 \| 0.2177 \|
	\| 0.7826 \| 35500 \| 0.2242 \|
	\| 0.7936 \| 36000 \| 0.22 \|
	\| 0.8046 \| 36500 \| 0.2026 \|
	\| 0.8156 \| 37000 \| 0.1988 \|
	\| 0.8267 \| 37500 \| 0.1845 \|
	\| 0.8377 \| 38000 \| 0.1955 \|
	\| 0.8487 \| 38500 \| 0.2115 \|
	\| 0.8597 \| 39000 \| 0.2026 \|
	\| 0.8708 \| 39500 \| 0.1861 \|
	\| 0.8818 \| 40000 \| 0.1882 \|
	\| 0.8928 \| 40500 \| 0.1861 \|
	\| 0.9038 \| 41000 \| 0.1921 \|
	\| 0.9148 \| 41500 \| 0.1778 \|
	\| 0.9259 \| 42000 \| 0.1779 \|
	\| 0.9369 \| 42500 \| 0.1782 \|
	\| 0.9479 \| 43000 \| 0.1748 \|
	\| 0.9589 \| 43500 \| 0.168 \|
	\| 0.9700 \| 44000 \| 0.1717 \|
	\| 0.9810 \| 44500 \| 0.1699 \|
	\| 0.9920 \| 45000 \| 0.1697 \|


	### Framework Versions
	- Python: 3.11.12
	- Sentence Transformers: 4.1.0
	- Transformers: 4.52.3
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.7.0
	- Datasets: 3.6.0
	- Tokenizers: 0.21.1

	## 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{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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