README.md

---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:106628
- loss:MultipleNegativesRankingLoss
base_model: Qwen/Qwen3-Embedding-0.6B
widget:
- source_sentence: ace-v
  sentences:
  - The floor plan was drafted at 1/4 inch scale where each quarter inch equals one
    foot.
  - Fingerprint examiners follow the ACE-V methodology for identification.
  - Most modern streaming services offer content in 1080p full HD quality.
- source_sentence: adult learner
  sentences:
  - The adult learner brings valuable life experience to the classroom.
  - Accounts payable represents money owed to suppliers and vendors.
  - The inspection confirmed all above grade work met code requirements.
- source_sentence: 1/4 inch scale
  sentences:
  - Precise adjustments require accurate action gauge readings.
  - The quality inspector identified adhesion failure in the sample.
  - The architect created drawings at 1/4 inch scale for the client presentation.
- source_sentence: acrylic paint
  sentences:
  - Artists prefer acrylic paint for its fast drying time.
  - The company reported strong adjusted EBITDA growth this quarter.
  - The clinic specializes in adolescent health services.
- source_sentence: adult learning
  sentences:
  - Solar developers calculate AEP, or annual energy production.
  - The course was designed using adult learning best practices.
  - The wizard cast Abi-Dalzim's horrid wilting, draining moisture from enemies.
datasets:
- electroglyph/technical
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) on the [technical](https://huggingface.co/datasets/electroglyph/technical) 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](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [technical](https://huggingface.co/datasets/electroglyph/technical)
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 512, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (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:

```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("reboo13/ad")
# Run inference
sentences = [
    'adult learning',
    'The course was designed using adult learning best practices.',
    'Solar developers calculate AEP, or annual energy production.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6213, 0.1227],
#         [0.6213, 1.0000, 0.1474],
#         [0.1227, 0.1474, 1.0000]])
```

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## Training Details

### Training Dataset

#### technical

* Dataset: [technical](https://huggingface.co/datasets/electroglyph/technical) at [05eeb90](https://huggingface.co/datasets/electroglyph/technical/tree/05eeb90e13d6bca725a5888f1ba206b2878f9c97)
* Size: 106,628 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            |
  | details | <ul><li>min: 2 tokens</li><li>mean: 3.83 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.66 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
  | anchor            | positive                                                                                            |
  |:------------------|:----------------------------------------------------------------------------------------------------|
  | <code>.308</code> | <code>The .308 Winchester is a popular rifle cartridge used for hunting and target shooting.</code> |
  | <code>.308</code> | <code>Many precision rifles are chambered in .308 for its excellent long-range accuracy.</code>     |
  | <code>.308</code> | <code>The sniper selected a .308 caliber round for the mission.</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",
      "gather_across_devices": false
  }
  ```

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

- `per_device_train_batch_size`: 256
- `learning_rate`: 3e-05
- `max_steps`: 60
- `lr_scheduler_type`: constant_with_warmup
- `warmup_ratio`: 0.03
- `bf16`: True
- `batch_sampler`: no_duplicates

#### 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`: 256
- `per_device_eval_batch_size`: 8
- `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`: 3e-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`: 3.0
- `max_steps`: 60
- `lr_scheduler_type`: constant_with_warmup
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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`: 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}
- `parallelism_config`: 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`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0024 | 1    | 2.9285        |
| 0.0048 | 2    | 2.9415        |
| 0.0072 | 3    | 2.7433        |
| 0.0096 | 4    | 2.8367        |
| 0.0120 | 5    | 2.7583        |
| 0.0144 | 6    | 2.8774        |
| 0.0168 | 7    | 2.7791        |
| 0.0192 | 8    | 2.5914        |
| 0.0216 | 9    | 2.5369        |
| 0.0240 | 10   | 2.5583        |
| 0.0264 | 11   | 2.428         |
| 0.0288 | 12   | 2.2281        |
| 0.0312 | 13   | 2.3207        |
| 0.0336 | 14   | 2.3152        |
| 0.0360 | 15   | 2.3222        |
| 0.0384 | 16   | 1.9328        |
| 0.0408 | 17   | 2.0254        |
| 0.0432 | 18   | 2.2076        |
| 0.0456 | 19   | 1.9551        |
| 0.0480 | 20   | 2.0753        |
| 0.0504 | 21   | 1.9028        |
| 0.0528 | 22   | 1.8977        |
| 0.0552 | 23   | 1.8852        |
| 0.0576 | 24   | 1.8288        |
| 0.0600 | 25   | 1.7363        |
| 0.0624 | 26   | 1.8455        |
| 0.0647 | 27   | 1.7129        |
| 0.0671 | 28   | 1.9365        |
| 0.0695 | 29   | 2.0386        |
| 0.0719 | 30   | 1.8644        |
| 0.0743 | 31   | 1.481         |
| 0.0767 | 32   | 1.8281        |
| 0.0791 | 33   | 1.5593        |
| 0.0815 | 34   | 1.7088        |
| 0.0839 | 35   | 1.7356        |
| 0.0863 | 36   | 1.6223        |
| 0.0887 | 37   | 1.6218        |
| 0.0911 | 38   | 1.4948        |
| 0.0935 | 39   | 1.6253        |
| 0.0959 | 40   | 1.553         |
| 0.0983 | 41   | 1.565         |
| 0.1007 | 42   | 1.6852        |
| 0.1031 | 43   | 1.4419        |
| 0.1055 | 44   | 1.4839        |
| 0.1079 | 45   | 1.4249        |
| 0.1103 | 46   | 1.4301        |
| 0.1127 | 47   | 1.5504        |
| 0.1151 | 48   | 1.4154        |
| 0.1175 | 49   | 1.3868        |
| 0.1199 | 50   | 1.601         |
| 0.1223 | 51   | 1.468         |
| 0.1247 | 52   | 1.4715        |
| 0.1271 | 53   | 1.6019        |
| 0.1295 | 54   | 1.4216        |
| 0.1319 | 55   | 1.3206        |
| 0.1343 | 56   | 1.4081        |
| 0.1367 | 57   | 1.2969        |
| 0.1391 | 58   | 1.5933        |
| 0.1415 | 59   | 1.4106        |
| 0.1439 | 60   | 1.7639        |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.0
- Transformers: 4.56.2
- PyTorch: 2.9.0+cu126
- Accelerate: 1.12.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2

## 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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