--- 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) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 725,795 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------| | A balance can measure the weight of | sugar | | 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? | Rs.3600 | | When a baby shakes a rattle, it makes a noise. Which form of energy was changed to sound energy? | mechanical | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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
### 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} } ```