---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:439290
- loss:DualThresholdEnforcedMNRL1
base_model: flax-sentence-embeddings/all_datasets_v4_MiniLM-L6
widget:
- source_sentence: compression therapy benefits
  sentences:
  - 'edema: what is, causes, symptoms, and treatment'
  - How VIN Data Enhances Market Value Assessments
  - Daily Iron Intake from Leafy Greens and Fortified Cereals
- source_sentence: liver function improvement tips
  sentences:
  - Antioxidants' Role in Liver Enzyme Regulation
  - Vitamin K2 and Its Role in Artery Calcification
  - Fatty Acids' Role in Liver Health
- source_sentence: back pain prevention exercises
  sentences:
  - 'Medication Side Effects: Dizziness, Fatigue, and More'
  - 'Strengthening Moves: Lunges, Squats, and Leg Raises'
  - 'Natural Anti-Inflammatories: Foods That May Help'
- source_sentence: weekly ad shopping tips
  sentences:
  - Investor Responses to Surplus Capital in Tech Firms
  - How Glycemic Index Affects Blood Sugar Levels
  - Evaluating Household Essentials Promotions in Weekly Circulars
- source_sentence: vitamin B12 for nerve health
  sentences:
  - 'Minoxidil: Side Effects and Use Cases'
  - Emerging Patterns in Roblox Code Distribution Channels
  - The Role of Magnesium in Muscle and Nerve Function
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on flax-sentence-embeddings/all_datasets_v4_MiniLM-L6

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6). It maps sentences & paragraphs to a 384-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:** [flax-sentence-embeddings/all_datasets_v4_MiniLM-L6](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6) <!-- at revision a407cc0b7d85eec9a5617eaf51dbe7b353b0c79f -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("Auto-opts/flax-TMNRLB_CVR")
# Run inference
sentences = [
    'vitamin B12 for nerve health',
    'The Role of Magnesium in Muscle and Nerve Function',
    'Emerging Patterns in Roblox Code Distribution Channels',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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

#### Unnamed Dataset

* Size: 439,290 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                           |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                           |
  | details | <ul><li>min: 5 tokens</li><li>mean: 7.43 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.34 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.94</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                   | sentence_1                                                           | label            |
  |:---------------------------------------------|:---------------------------------------------------------------------|:-----------------|
  | <code>common UTI misconceptions</code>       | <code>How Antibiotics Like Fosfomycin Target Infections</code>       | <code>1.0</code> |
  | <code>diuretics for swelling</code>          | <code>Venous Insufficiency and Its Impact on Leg Swelling</code>     | <code>1.0</code> |
  | <code>pelvic floor exercises benefits</code> | <code>Testosterone Levels and Their Impact on Erectile Health</code> | <code>1.0</code> |
* Loss: <code>__main__.DualThresholdEnforcedMNRL1</code>

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

- `per_device_train_batch_size`: 90
- `per_device_eval_batch_size`: 90
- `num_train_epochs`: 5
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 90
- `per_device_eval_batch_size`: 90
- `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`: 5
- `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`: 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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.1024 | 500   | 2.4422        |
| 0.2049 | 1000  | 1.8481        |
| 0.3073 | 1500  | 1.5855        |
| 0.4098 | 2000  | 1.4325        |
| 0.5122 | 2500  | 1.332         |
| 0.6146 | 3000  | 1.2434        |
| 0.7171 | 3500  | 1.1842        |
| 0.8195 | 4000  | 1.1338        |
| 0.9219 | 4500  | 1.0779        |
| 1.0244 | 5000  | 1.0283        |
| 1.1268 | 5500  | 0.996         |
| 1.2293 | 6000  | 0.954         |
| 1.3317 | 6500  | 0.9362        |
| 1.4341 | 7000  | 0.895         |
| 1.5366 | 7500  | 0.8776        |
| 1.6390 | 8000  | 0.8624        |
| 1.7414 | 8500  | 0.8438        |
| 1.8439 | 9000  | 0.8158        |
| 1.9463 | 9500  | 0.7958        |
| 2.0488 | 10000 | 0.7779        |
| 2.1512 | 10500 | 0.754         |
| 2.2536 | 11000 | 0.7332        |
| 2.3561 | 11500 | 0.722         |
| 2.4585 | 12000 | 0.711         |
| 2.5610 | 12500 | 0.6945        |
| 2.6634 | 13000 | 0.6965        |
| 2.7658 | 13500 | 0.6834        |
| 2.8683 | 14000 | 0.6676        |
| 2.9707 | 14500 | 0.6635        |
| 3.0731 | 15000 | 0.6484        |
| 3.1756 | 15500 | 0.6282        |
| 3.2780 | 16000 | 0.6297        |
| 3.3805 | 16500 | 0.6241        |
| 3.4829 | 17000 | 0.6214        |
| 3.5853 | 17500 | 0.61          |
| 3.6878 | 18000 | 0.6106        |
| 3.7902 | 18500 | 0.6006        |
| 3.8926 | 19000 | 0.6062        |
| 3.9951 | 19500 | 0.6022        |
| 4.0975 | 20000 | 0.5808        |
| 4.2000 | 20500 | 0.5855        |
| 4.3024 | 21000 | 0.5852        |
| 4.4048 | 21500 | 0.5757        |
| 4.5073 | 22000 | 0.5768        |
| 4.6097 | 22500 | 0.5715        |
| 4.7121 | 23000 | 0.5764        |
| 4.8146 | 23500 | 0.5732        |
| 4.9170 | 24000 | 0.5777        |


### Framework Versions
- Python: 3.12.3
- 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",
}
```

<!--
## 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.*
-->