---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:133380
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Plant-Based Nutrition Guide
  sentences:
  - Streaming Videos about Miscellaneous
  - Honest John - Car Reviews & Buying Advice
  - Participating in online forums and communities about Seasonal Forecasts
- source_sentence: 'BBC iPlayer: Science and Nature'
  sentences:
  - Explained | Science and Technology on Netflix
  - Cosmic Convention Announcements and Details
  - Opportunities in Emerging Markets
- source_sentence: How Weather Changes Affect Veteran Wellbeing
  sentences:
  - Participating in online forums and communities about Space Exploration
  - 'Streaming: Innovative Backpacking Gear'
  - Participating in online forums and communities about Video Games
- source_sentence: Smith Optics
  sentences:
  - Researching or Booking Travel for New York City, USA
  - Casablanca Tours & Activities
  - 'Stream: Journey Across the Globe'
- source_sentence: 'Crunchyroll: Anime Trends 2023'
  sentences:
  - Search Hotels in Montreal
  - Energy Storage Forum
  - Online Shopping for Vacuum Cleaners
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dev eval
      type: dev-eval
    metrics:
    - type: pearson_cosine
      value: 0.9643510348078764
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6244978728197877
      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 c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **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-Mini-fine-tuned")
# Run inference
sentences = [
    'Crunchyroll: Anime Trends 2023',
    'Search Hotels in Montreal',
    'Online Shopping for Vacuum Cleaners',
]
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

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9644     |
| **spearman_cosine** | **0.6245** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 133,380 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: 3 tokens</li><li>mean: 8.75 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.55 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.16</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                      | sentence_1                                                   | label            |
  |:----------------------------------------------------------------|:-------------------------------------------------------------|:-----------------|
  | <code>Military Times</code>                                     | <code>Financial Analyst Resume Sample</code>                 | <code>0.0</code> |
  | <code>Outdoor Music Festivals for Adventurers</code>            | <code>Balancing Mental Health with Outdoor Adventures</code> | <code>0.0</code> |
  | <code>The Rise of Artificial Intelligence in Video Games</code> | <code>Winter Deals on Streaming Equipment</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
- `num_train_epochs`: 1
- `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`: 1
- `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}
- `tp_size`: 0
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | dev-eval_spearman_cosine |
|:------:|:----:|:-------------:|:------------------------:|
| 0.1199 | 500  | 0.0841        | -                        |
| 0.2399 | 1000 | 0.0769        | -                        |
| 0.3598 | 1500 | 0.0671        | -                        |
| 0.4797 | 2000 | 0.0623        | -                        |
| 0.5997 | 2500 | 0.0558        | -                        |
| 0.7196 | 3000 | 0.0502        | -                        |
| 0.8395 | 3500 | 0.046         | -                        |
| 0.9595 | 4000 | 0.0433        | -                        |
| -1     | -1   | -             | 0.6101                   |
| 0.1199 | 500  | 0.0362        | -                        |
| 0.2399 | 1000 | 0.0353        | -                        |
| 0.3598 | 1500 | 0.0337        | -                        |
| 0.4797 | 2000 | 0.0332        | -                        |
| 0.5997 | 2500 | 0.0327        | -                        |
| 0.7196 | 3000 | 0.0312        | -                        |
| 0.8395 | 3500 | 0.0287        | -                        |
| 0.9595 | 4000 | 0.0286        | -                        |
| -1     | -1   | -             | 0.6196                   |
| 0.1199 | 500  | 0.0253        | -                        |
| 0.2399 | 1000 | 0.0232        | -                        |
| 0.3598 | 1500 | 0.0207        | -                        |
| 0.4797 | 2000 | 0.0195        | -                        |
| 0.5997 | 2500 | 0.0182        | -                        |
| 0.7196 | 3000 | 0.0162        | -                        |
| 0.8395 | 3500 | 0.0139        | -                        |
| 0.9595 | 4000 | 0.0139        | -                        |
| -1     | -1   | -             | 0.6221                   |
| 0.1199 | 500  | 0.0195        | -                        |
| 0.2399 | 1000 | 0.0166        | -                        |
| 0.3598 | 1500 | 0.0136        | -                        |
| 0.4797 | 2000 | 0.012         | -                        |
| 0.5997 | 2500 | 0.0108        | -                        |
| 0.7196 | 3000 | 0.0087        | -                        |
| 0.8395 | 3500 | 0.0072        | -                        |
| 0.9595 | 4000 | 0.0069        | -                        |
| -1     | -1   | -             | 0.6227                   |
| 0.1199 | 500  | 0.0162        | -                        |
| 0.2399 | 1000 | 0.0127        | -                        |
| 0.3598 | 1500 | 0.0096        | -                        |
| 0.4797 | 2000 | 0.0075        | -                        |
| 0.5997 | 2500 | 0.0065        | -                        |
| 0.7196 | 3000 | 0.0049        | -                        |
| 0.8395 | 3500 | 0.0043        | -                        |
| 0.9595 | 4000 | 0.0043        | -                        |
| -1     | -1   | -             | 0.6229                   |
| 0.1199 | 500  | 0.0139        | -                        |
| 0.2399 | 1000 | 0.0099        | -                        |
| 0.3598 | 1500 | 0.0069        | -                        |
| 0.4797 | 2000 | 0.005         | -                        |
| 0.5997 | 2500 | 0.0042        | -                        |
| 0.7196 | 3000 | 0.0031        | -                        |
| 0.8395 | 3500 | 0.0027        | -                        |
| 0.9595 | 4000 | 0.0029        | -                        |
| -1     | -1   | -             | 0.6234                   |
| 0.1199 | 500  | 0.0125        | -                        |
| 0.2399 | 1000 | 0.0078        | -                        |
| 0.3598 | 1500 | 0.005         | -                        |
| 0.4797 | 2000 | 0.0036        | -                        |
| 0.5997 | 2500 | 0.0028        | -                        |
| 0.7196 | 3000 | 0.0022        | -                        |
| 0.8395 | 3500 | 0.002         | -                        |
| 0.9595 | 4000 | 0.0022        | -                        |
| -1     | -1   | -             | 0.6248                   |
| 0.1199 | 500  | 0.0114        | -                        |
| 0.2399 | 1000 | 0.0068        | -                        |
| 0.3598 | 1500 | 0.004         | -                        |
| 0.4797 | 2000 | 0.0027        | -                        |
| 0.5997 | 2500 | 0.0023        | -                        |
| 0.7196 | 3000 | 0.0014        | -                        |
| 0.8395 | 3500 | 0.0015        | -                        |
| 0.9595 | 4000 | 0.0015        | -                        |
| -1     | -1   | -             | 0.6245                   |
| 0.1199 | 500  | 0.0107        | -                        |
| 0.2399 | 1000 | 0.0058        | -                        |
| 0.3598 | 1500 | 0.0034        | -                        |
| 0.4797 | 2000 | 0.0021        | -                        |
| 0.5997 | 2500 | 0.0016        | -                        |
| 0.7196 | 3000 | 0.0011        | -                        |
| 0.8395 | 3500 | 0.0013        | -                        |
| 0.9595 | 4000 | 0.0011        | -                        |
| -1     | -1   | -             | 0.6249                   |
| 0.1199 | 500  | 0.0097        | -                        |
| 0.2399 | 1000 | 0.0048        | -                        |
| 0.3598 | 1500 | 0.0024        | -                        |
| 0.4797 | 2000 | 0.0015        | -                        |
| 0.5997 | 2500 | 0.0013        | -                        |
| 0.7196 | 3000 | 0.0009        | -                        |
| 0.8395 | 3500 | 0.001         | -                        |
| 0.9595 | 4000 | 0.0009        | -                        |
| -1     | -1   | -             | 0.6245                   |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- 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",
}
```

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