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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:268861 |
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- loss:MultipleNegativesRankingLoss |
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base_model: Qwen/Qwen3-0.6B-Base |
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widget: |
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- source_sentence: 'There are seven thieves. They stole diamonds from a diamond merchant |
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and ran away. While running, night sets in and they decide to rest in the jungle. |
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When everybody was sleeping, two of them woke up and decided to divide the diamonds |
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equally among themselves. But when they divided the diamonds equally, one diamond |
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is left. |
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So they woke up the 3rd thief and tried to divide the diamonds equally again but |
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still one diamond was left. Then they woke up the 4th thief to divide the diamonds |
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equally again, and again one diamond was left. This happened with the 5th and |
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6th thief – one diamond was still left. |
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Finally, they woke up the 7th thief and this time the diamonds were divided equally. |
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How many diamonds did they steal in total?' |
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sentences: |
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- '''' |
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- '''' |
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- e |
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- source_sentence: 'praveen starts business with rs . 3220 and after 5 months , hari |
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joins with praveen as his partner . after a year , the profit is divided in the |
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ratio 2 : 3 . what is hari ’ s contribution in the capital ?' |
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sentences: |
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- s |
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- '5' |
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- '[' |
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- source_sentence: 'Which of the following is material of choice in class V |
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cavity with abfraction?' |
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sentences: |
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- '[' |
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- t |
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- G |
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- source_sentence: A right circular cylinder has a height of 25 and a radius of 5. |
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A rectangular solid with a height of 15 and a square base, is placed in the cylinder |
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such that each of the corners of the solid is tangent to the cylinder wall. Liquid |
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is then poured into the cylinder such that it reaches the rim. What is the volume |
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of the liquid? |
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sentences: |
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- '5' |
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- '[' |
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- '2' |
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- source_sentence: Cerebral angiography was performed by - |
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sentences: |
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- S |
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- t |
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- '2' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on Qwen/Qwen3-0.6B-Base |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) <!-- at revision 11214f7f3465775dcce23c3752ecea5a42ee0ddc --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: Qwen3Model |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'Cerebral angiography was performed by -', |
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'S', |
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'2', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 268,861 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 48.3 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0 tokens</li><li>mean: 0.97 tokens</li><li>max: 1 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>A 1200 m long train crosses a tree in 120 sec, how much time will I take to pass a platform 1100 m long?</code> | <code>'</code> | |
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| <code>What is the opposite of rarefaction zones, where air molecules in waves are loosely packed?</code> | <code>[</code> | |
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| <code>if w is 40 percent less than e , e is 40 percent less than y , and z is 46 percent less than y , then z is greater than w by what percent of w ?</code> | <code>%</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 4 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:-----:|:-------------:| |
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| 0.1190 | 500 | 4.0939 | |
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| 0.2380 | 1000 | 3.7716 | |
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| 0.3571 | 1500 | 0.0 | |
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| 0.4761 | 2000 | 0.0 | |
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| 0.5951 | 2500 | 0.0 | |
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| 0.7141 | 3000 | 0.0 | |
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| 0.8331 | 3500 | 0.0 | |
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| 0.9522 | 4000 | 0.0 | |
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| 1.0712 | 4500 | 0.0 | |
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| 1.1902 | 5000 | 0.0 | |
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| 1.3092 | 5500 | 0.0 | |
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| 1.4282 | 6000 | 0.0 | |
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| 1.5473 | 6500 | 0.0 | |
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| 1.6663 | 7000 | 0.0 | |
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| 1.7853 | 7500 | 0.0 | |
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| 1.9043 | 8000 | 0.0 | |
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| 2.0233 | 8500 | 0.0 | |
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| 2.1423 | 9000 | 0.0 | |
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| 2.2614 | 9500 | 0.0 | |
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| 2.3804 | 10000 | 0.0 | |
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| 2.4994 | 10500 | 0.0 | |
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| 2.6184 | 11000 | 0.0 | |
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| 2.7374 | 11500 | 0.0 | |
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| 2.8565 | 12000 | 0.0 | |
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| 2.9755 | 12500 | 0.0 | |
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| 3.0945 | 13000 | 0.0 | |
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| 3.2135 | 13500 | 0.0 | |
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| 3.3325 | 14000 | 0.0 | |
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| 3.4516 | 14500 | 0.0 | |
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| 3.5706 | 15000 | 0.0 | |
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| 3.6896 | 15500 | 0.0 | |
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| 3.8086 | 16000 | 0.0 | |
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| 3.9276 | 16500 | 0.0 | |
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### Framework Versions |
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- Python: 3.11.13 |
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- Sentence Transformers: 4.1.0 |
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- Transformers: 4.52.4 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.7.0 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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