README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1128
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: connective tissue cell
  sentences:
  - GM18507
  - GM18526
  - GM08714
- source_sentence: blood
  sentences:
  - AG04449
  - T cell
  - GM12868
- source_sentence: mammary gland
  sentences:
  - MCF-7
  - leukocyte
  - GM10847
- source_sentence: GM18526
  sentences:
  - digestive system
  - CMK
  - KOPT-K1
- source_sentence: GM12873
  sentences:
  - KOPT-K1
  - pancreas
  - leukocyte
datasets:
- databio/mock-stsb
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: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.7058652030883807
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.69543787652822
      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) on the [mock-stsb](https://huggingface.co/datasets/databio/mock-stsb) dataset. 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:**
    - [mock-stsb](https://huggingface.co/datasets/databio/mock-stsb)
<!-- - **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("sentence_transformers_model_id")
# Run inference
sentences = [
    'GM12873',
    'leukocyte',
    'pancreas',
]
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.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7059     |
| **spearman_cosine** | **0.6954** |

<!--
## 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

#### mock-stsb

* Dataset: [mock-stsb](https://huggingface.co/datasets/databio/mock-stsb) at [d5ba748](https://huggingface.co/datasets/databio/mock-stsb/tree/d5ba748c12ecb4eb2178b42c9735506a50de9f86)
* Size: 1,128 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.46 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.55 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 0.9</li></ul> |
* Samples:
  | sentence1                              | sentence2                       | score              |
  |:---------------------------------------|:--------------------------------|:-------------------|
  | <code>OVCAR3</code>                    | <code>pancreas</code>           | <code>0.05</code>  |
  | <code>L1-S8</code>                     | <code>respiratory system</code> | <code>0.001</code> |
  | <code>peripheral nervous system</code> | <code>22Rv1</code>              | <code>0.001</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"
  }
  ```

### Evaluation Dataset

#### mock-stsb

* Dataset: [mock-stsb](https://huggingface.co/datasets/databio/mock-stsb) at [d5ba748](https://huggingface.co/datasets/databio/mock-stsb/tree/d5ba748c12ecb4eb2178b42c9735506a50de9f86)
* Size: 284 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 284 samples:
  |         | sentence1                                                                      | sentence2                                                                       | score                                                          |
  |:--------|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                         | string                                                                          | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.6 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 0.9</li></ul> |
* Samples:
  | sentence1                    | sentence2                   | score             |
  |:-----------------------------|:----------------------------|:------------------|
  | <code>SJCRH30</code>         | <code>cancer cell</code>    | <code>0.9</code>  |
  | <code>CWRU1</code>           | <code>exocrine gland</code> | <code>0.05</code> |
  | <code>epithelial cell</code> | <code>Caki2</code>          | <code>0.9</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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `learning_rate`: 1e-05
- `num_train_epochs`: 50
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `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`: 1e-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`: 50
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `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`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine |
|:-----:|:----:|:-------------:|:---------------:|:-----------------------:|
| 1.0   | 282  | 0.2157        | 0.1413          | 0.4340                  |
| 2.0   | 564  | 0.1402        | 0.1207          | 0.6198                  |
| 3.0   | 846  | 0.1239        | 0.0973          | 0.6541                  |
| 4.0   | 1128 | 0.1102        | 0.0858          | 0.6820                  |
| 5.0   | 1410 | 0.1006        | 0.0867          | 0.6664                  |
| 6.0   | 1692 | 0.0882        | 0.0886          | 0.6547                  |
| 7.0   | 1974 | 0.076         | 0.0842          | 0.6660                  |
| 8.0   | 2256 | 0.0639        | 0.0883          | 0.6392                  |
| 9.0   | 2538 | 0.0538        | 0.0896          | 0.6300                  |
| 10.0  | 2820 | 0.046         | 0.0884          | 0.6424                  |
| 11.0  | 3102 | 0.0427        | 0.0858          | 0.6600                  |
| 12.0  | 3384 | 0.0363        | 0.0878          | 0.6454                  |
| 13.0  | 3666 | 0.0331        | 0.0838          | 0.6710                  |
| 14.0  | 3948 | 0.0309        | 0.0839          | 0.6534                  |
| 15.0  | 4230 | 0.0277        | 0.0841          | 0.6650                  |
| 16.0  | 4512 | 0.026         | 0.0843          | 0.6933                  |
| 17.0  | 4794 | 0.0238        | 0.0884          | 0.6557                  |
| 18.0  | 5076 | 0.0229        | 0.0868          | 0.6649                  |
| 19.0  | 5358 | 0.022         | 0.0867          | 0.6629                  |
| 20.0  | 5640 | 0.021         | 0.0809          | 0.6815                  |
| 21.0  | 5922 | 0.0196        | 0.0827          | 0.6844                  |
| 22.0  | 6204 | 0.0189        | 0.0857          | 0.6770                  |
| 23.0  | 6486 | 0.0186        | 0.0833          | 0.6868                  |
| 24.0  | 6768 | 0.0172        | 0.0889          | 0.6710                  |
| 25.0  | 7050 | 0.0171        | 0.0806          | 0.6954                  |


### Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.0
- Datasets: 3.1.0
- 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",
}
```

<!--
## 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.*
-->