Hugging Face's logo

samuerio
/
lora_model

Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
dense
Generated from Trainer
dataset_size:106628
loss:MultipleNegativesRankingLoss
Model card Files
xet
 Community
lora_model / README.md
samuerio's picture
samuerio
Add new SentenceTransformer model
23500ab verified
preview code
|
raw
history blame contribute delete
14.6 kB
---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:106628
- loss:MultipleNegativesRankingLoss
base_model: unsloth/Qwen3-Embedding-4B
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 unsloth/Qwen3-Embedding-4B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/Qwen3-Embedding-4B](https://huggingface.co/unsloth/Qwen3-Embedding-4B) on the [technical](https://huggingface.co/datasets/electroglyph/technical) dataset. It maps sentences & paragraphs to a 2560-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:** [unsloth/Qwen3-Embedding-4B](https://huggingface.co/unsloth/Qwen3-Embedding-4B) <!-- at revision 8edd3dda8d779908e835f772b7290680593edc7d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 2560 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': 2560, '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("samuerio/lora_model")
# 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, 2560]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6051, 0.0748],
# [0.6051, 1.0000, 0.0859],
# [0.0748, 0.0859, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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
- `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`: 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`: 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
- `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 | 1.9348 |
| 0.0048 | 2 | 2.2633 |
| 0.0072 | 3 | 2.0006 |
| 0.0096 | 4 | 2.1364 |
| 0.0120 | 5 | 1.9707 |
| 0.0144 | 6 | 2.0678 |
| 0.0168 | 7 | 1.8564 |
| 0.0192 | 8 | 1.9699 |
| 0.0216 | 9 | 1.8498 |
| 0.0240 | 10 | 1.7396 |
| 0.0264 | 11 | 1.7891 |
| 0.0288 | 12 | 1.5642 |
| 0.0312 | 13 | 1.5153 |
| 0.0336 | 14 | 1.4762 |
| 0.0360 | 15 | 1.4062 |
| 0.0384 | 16 | 1.2813 |
| 0.0408 | 17 | 1.2423 |
| 0.0432 | 18 | 1.2104 |
| 0.0456 | 19 | 0.9924 |
| 0.0480 | 20 | 1.2527 |
| 0.0504 | 21 | 1.0565 |
| 0.0528 | 22 | 0.9497 |
| 0.0552 | 23 | 0.9633 |
| 0.0576 | 24 | 0.8434 |
| 0.0600 | 25 | 1.0153 |
| 0.0624 | 26 | 0.8398 |
| 0.0647 | 27 | 0.8843 |
| 0.0671 | 28 | 1.0023 |
| 0.0695 | 29 | 0.9449 |
| 0.0719 | 30 | 0.9488 |
| 0.0743 | 31 | 0.7369 |
| 0.0767 | 32 | 0.9053 |
| 0.0791 | 33 | 0.8601 |
| 0.0815 | 34 | 0.7028 |
| 0.0839 | 35 | 0.8224 |
| 0.0863 | 36 | 0.8759 |
| 0.0887 | 37 | 0.7351 |
| 0.0911 | 38 | 0.7604 |
| 0.0935 | 39 | 0.7229 |
| 0.0959 | 40 | 0.6302 |
| 0.0983 | 41 | 0.6978 |
| 0.1007 | 42 | 0.7879 |
| 0.1031 | 43 | 0.6713 |
| 0.1055 | 44 | 0.7428 |
| 0.1079 | 45 | 0.6153 |
| 0.1103 | 46 | 0.7535 |
| 0.1127 | 47 | 0.6687 |
| 0.1151 | 48 | 0.6581 |
| 0.1175 | 49 | 0.6117 |
| 0.1199 | 50 | 0.7821 |
| 0.1223 | 51 | 0.7456 |
| 0.1247 | 52 | 0.5658 |
| 0.1271 | 53 | 0.8558 |
| 0.1295 | 54 | 0.5125 |
| 0.1319 | 55 | 0.599 |
| 0.1343 | 56 | 0.6182 |
| 0.1367 | 57 | 0.6189 |
| 0.1391 | 58 | 0.7712 |
| 0.1415 | 59 | 0.6419 |
| 0.1439 | 60 | 0.8471 |
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->