sentence-transformers onnx safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:429643 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:quantized:sentence-transformers/all-MiniLM-L6-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:429643
- loss:CosineSimilarityLoss
widget:
- source_sentence: Oracle Cloud - Infrastructure and Platform Services for Enterprises
  sentences:
  - PulseAudio - Ubuntu Wiki
  - Documentation page not found - Read the Docs
  - Dwarf Fortress beginner tips - Video Games on Sports Illustrated
- source_sentence: Suggest opt in User Test - Google Slides
  sentences:
  - ReleaseEngineering/TryServer - MozillaWiki
  - Dwarf Fortress beginner tips - Video Games on Sports Illustrated
  - Tutanota - Private Mailbox with End-to-End Encryption and Calendar
- source_sentence: https://portal.naviabenefits.com/part/prioritytasks.aspx
  sentences:
  - What to Expect - Pregnancy and Parenting Tips, Week-by-Week Guides
  - Parents.com - Articles, Recipes, and Ideas for Family Activities
  - Pinterest - Boards for Collecting and Sharing Inspiration on Any Topic
- source_sentence: ‎Apple Music - Web Player
  sentences:
  - BMW Connected Drive - Home Assistant
  - Mary Stewart Phillips (1862-1928) - Find a Grave Memorial
  - Sky Sports - Football, Formula 1, Cricket, and More
- source_sentence: Tidal - High-Fidelity Music Streaming with Master Quality Audio
  sentences:
  - Walmart - Everyday Low Prices on Groceries, Electronics, and More
  - Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis
  - Ambient Dreams Playlist on Amazon Music
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.9822505655251419
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.2607864200673379
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 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': 256, '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("vazish/all-MiniLM-L6-v2-fine-tuned_0")
# Run inference
sentences = [
    'Tidal - High-Fidelity Music Streaming with Master Quality Audio',
    'Walmart - Everyday Low Prices on Groceries, Electronics, and More',
    'Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis',
]
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]
```

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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9823     |
| **spearman_cosine** | **0.2608** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 49,800 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: 10 tokens</li><li>mean: 14.76 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 14.64 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                        | sentence_1                                                                   | label            |
  |:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-----------------|
  | <code>TripAdvisor - Hotel Reviews, Photos, and Travel Forums</code>               | <code>Docker Hub - Container Image Repository for DevOps Environments</code> | <code>0.0</code> |
  | <code>Mastodon - Decentralized Social Media for Niche Communities</code>          | <code>Allrecipes - User-Submitted Recipes, Reviews, and Cooking Tips</code>  | <code>0.0</code> |
  | <code>YouTube Music - Music Videos, Official Albums, and Live Performances</code> | <code>ESPN - Sports News, Live Scores, Stats, and Highlights</code>          | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 3
- `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
- `dispatch_batches`: None
- `split_batches`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss | spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0372 | 500   | 0.0218        | -               |
| 0.0745 | 1000  | 0.0151        | -               |
| 0.1117 | 1500  | 0.0113        | -               |
| 0.1490 | 2000  | 0.0076        | -               |
| 0.1862 | 2500  | 0.0063        | -               |
| 0.2234 | 3000  | 0.0054        | -               |
| 0.2607 | 3500  | 0.0045        | -               |
| 0.2979 | 4000  | 0.0041        | -               |
| 0.3351 | 4500  | 0.0027        | -               |
| 0.3724 | 5000  | 0.0028        | -               |
| 0.4096 | 5500  | 0.0026        | -               |
| 0.4469 | 6000  | 0.0021        | -               |
| 0.4841 | 6500  | 0.0019        | -               |
| 0.5213 | 7000  | 0.0022        | -               |
| 0.5586 | 7500  | 0.0017        | -               |
| 0.5958 | 8000  | 0.0018        | -               |
| 0.6331 | 8500  | 0.0015        | -               |
| 0.6703 | 9000  | 0.0015        | -               |
| 0.7075 | 9500  | 0.0018        | -               |
| 0.7448 | 10000 | 0.0014        | -               |
| 0.7820 | 10500 | 0.0017        | -               |
| 0.8192 | 11000 | 0.0012        | -               |
| 0.8565 | 11500 | 0.0014        | -               |
| 0.8937 | 12000 | 0.001         | -               |
| 0.9310 | 12500 | 0.0011        | -               |
| 0.9682 | 13000 | 0.001         | -               |
| 1.0054 | 13500 | 0.0009        | -               |
| 1.0427 | 14000 | 0.0011        | -               |
| 1.0799 | 14500 | 0.001         | -               |
| 1.1172 | 15000 | 0.0009        | -               |
| 1.1544 | 15500 | 0.0008        | -               |
| 1.1916 | 16000 | 0.001         | -               |
| 1.2289 | 16500 | 0.0011        | -               |
| 1.2661 | 17000 | 0.0011        | -               |
| 1.3033 | 17500 | 0.0006        | -               |
| 1.3406 | 18000 | 0.0011        | -               |
| 1.3778 | 18500 | 0.0008        | -               |
| 1.4151 | 19000 | 0.0011        | -               |
| 1.4523 | 19500 | 0.0009        | -               |
| 1.4895 | 20000 | 0.0011        | -               |
| 1.5268 | 20500 | 0.0009        | -               |
| 1.5640 | 21000 | 0.0009        | -               |
| 1.6013 | 21500 | 0.0008        | -               |
| 1.6385 | 22000 | 0.0005        | -               |
| 1.6757 | 22500 | 0.001         | -               |
| 1.7130 | 23000 | 0.0008        | -               |
| 1.7502 | 23500 | 0.0007        | -               |
| 1.7874 | 24000 | 0.0007        | -               |
| 1.8247 | 24500 | 0.0008        | -               |
| 1.8619 | 25000 | 0.001         | -               |
| 1.8992 | 25500 | 0.0009        | -               |
| 1.9364 | 26000 | 0.0008        | -               |
| 1.9736 | 26500 | 0.0009        | -               |
| 2.0109 | 27000 | 0.0007        | -               |
| 2.0481 | 27500 | 0.0006        | -               |
| 2.0854 | 28000 | 0.0007        | -               |
| 2.1226 | 28500 | 0.0006        | -               |
| 2.1598 | 29000 | 0.0007        | -               |
| 2.1971 | 29500 | 0.001         | -               |
| 2.2343 | 30000 | 0.0006        | -               |
| 2.2715 | 30500 | 0.0006        | -               |
| 2.3088 | 31000 | 0.001         | -               |
| 2.3460 | 31500 | 0.0007        | -               |
| 2.3833 | 32000 | 0.0008        | -               |
| 2.4205 | 32500 | 0.0006        | -               |
| 2.4577 | 33000 | 0.0007        | -               |
| 2.4950 | 33500 | 0.0007        | -               |
| 2.5322 | 34000 | 0.001         | -               |
| 2.5694 | 34500 | 0.0007        | -               |
| 2.6067 | 35000 | 0.0007        | -               |
| 2.6439 | 35500 | 0.0008        | -               |
| 2.6812 | 36000 | 0.0007        | -               |
| 2.7184 | 36500 | 0.0006        | -               |
| 2.7556 | 37000 | 0.0007        | -               |
| 2.7929 | 37500 | 0.0007        | -               |
| 2.8301 | 38000 | 0.0005        | -               |
| 2.8674 | 38500 | 0.0009        | -               |
| 2.9046 | 39000 | 0.0006        | -               |
| 2.9418 | 39500 | 0.0007        | -               |
| 2.9791 | 40000 | 0.0008        | -               |
| -1     | -1    | -             | 0.2608          |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

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