---
language:
- en
tags:
- unsloth
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:106628
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
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

This model was finetuned with [Unsloth](https://github.com/unslothai/unsloth).

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 based on Alibaba-NLP/gte-modernbert-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [technical](https://huggingface.co/datasets/electroglyph/technical) dataset. It maps sentences & paragraphs to a 768-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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 768, 'pooling_mode_cls_token': True, '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': 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 = [
    '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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7228, 0.1468],
#         [0.7228, 1.0000, 0.1683],
#         [0.1468, 0.1683, 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: 3 tokens</li><li>mean: 4.93 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 13.75 tokens</li><li>max: 25 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`: 333
- `learning_rate`: 3e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: constant_with_warmup
- `warmup_steps`: 100
- `fp16`: 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`: 333
- `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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: constant_with_warmup
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `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
- `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}
- `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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: no
- `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`: True
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1558 | 50   | 3.4086        |
| 0.3115 | 100  | 3.3329        |
| 0.4673 | 150  | 3.2148        |
| 0.6231 | 200  | 2.9797        |
| 0.7788 | 250  | 2.7541        |
| 0.9346 | 300  | 2.5277        |
| 1.0903 | 350  | 2.3069        |
| 1.2461 | 400  | 2.1593        |
| 1.4019 | 450  | 2.0781        |
| 1.5576 | 500  | 1.9385        |
| 1.7134 | 550  | 1.9052        |
| 1.8692 | 600  | 1.8768        |
| 2.0249 | 650  | 1.8272        |
| 2.1807 | 700  | 1.7906        |
| 2.3364 | 750  | 1.7607        |
| 2.4922 | 800  | 1.7375        |
| 2.6480 | 850  | 1.6952        |
| 2.8037 | 900  | 1.6664        |
| 2.9595 | 950  | 1.6216        |
| 3.1153 | 1000 | 1.5601        |
| 3.2710 | 1050 | 1.571         |
| 3.4268 | 1100 | 1.5735        |
| 3.5826 | 1150 | 1.5455        |
| 3.7383 | 1200 | 1.5577        |
| 3.8941 | 1250 | 1.5426        |
| 4.0498 | 1300 | 1.5276        |
| 4.2056 | 1350 | 1.5178        |
| 4.3614 | 1400 | 1.4611        |
| 4.5171 | 1450 | 1.4822        |
| 4.6729 | 1500 | 1.4987        |
| 4.8287 | 1550 | 1.4507        |
| 4.9844 | 1600 | 1.4501        |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.0+cu126
- Accelerate: 1.12.0
- Datasets: 4.3.0
- Tokenizers: 0.22.1

## 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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