sentence-transformers safetensors distilbert sentence-similarity feature-extraction dataset_size:10K<n<100K loss:CosineSimilarityLoss arxiv:1908.10084 base_model:pierreinalco/distilbert-base-uncased-sts base_model:finetune:pierreinalco/distilbert-base-uncased-sts model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: pierreinalco/distilbert-base-uncased-sts
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CosineSimilarityLoss
widget:
- source_sentence: '[SYNTAX] Inversion is a common syntactic feature in questions.'
  sentences:
  - '[SYNTAX] DNA transcription is a common biological mechanism regulating RNA synthesis.'
  - '[SYNTAX] Fermions and bosons are the two broad categories of subatomic particles.'
  - Extensive legislative debate is often required when amending existing public policies.
- source_sentence: The examination of meaning in language is known as semantics.
  sentences:
  - 'Semantics is the study of meaning in language. '
  - '[SYNTAX] Extreme weather events are becoming more frequent due to climate change.'
  - Regular practice is essential to ensure the success of musical performances.
- source_sentence: Marine life thrives in ecosystems teeming with diverse species.
  sentences:
  - Climate change modifies the balance of ecosystems around the globe.
  - '[SYNTAX] One key focus of archaeology is the exploration of ancient civilizations.'
  - 'By examining butcher marks, scientists can infer ancient dietary practices.  '
- source_sentence: Bicyclists rode swiftly in the park while a gentle breeze blew.
  sentences:
  - 'Urban parks offer residents vital green spaces for recreation and relaxation.  '
  - Contour farming follows the natural shape of the land to improve water retention.
  - Skipping breakfast can affect your energy levels and concentration throughout
    the day.
- source_sentence: Fossil fuel reserves are finite and will eventually be depleted.
  sentences:
  - Trace fossils, like footprints and burrows, reveal the behavior of ancient organisms.
  - Electric trains are more environmentally friendly compared to diesel-powered ones.
  - A declining atmospheric pressure frequently indicates the imminent arrival of
    a storm.
model-index:
- name: SentenceTransformer based on pierreinalco/distilbert-base-uncased-sts
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: custom dev
      type: custom-dev
    metrics:
    - type: pearson_cosine
      value: 0.9199550350229381
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8477353426901187
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.922270207368092
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8455601721195604
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9225814550760436
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8455566196441302
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9112758242260417
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8381909699446571
      name: Spearman Dot
    - type: pearson_max
      value: 0.9225814550760436
      name: Pearson Max
    - type: spearman_max
      value: 0.8477353426901187
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: custom test
      type: custom-test
    metrics:
    - type: pearson_cosine
      value: 0.9124658569127322
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8453565105014698
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9161256101176948
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.845382323611419
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9165265409472989
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8457233262812305
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.903021036040846
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8319052098219432
      name: Spearman Dot
    - type: pearson_max
      value: 0.9165265409472989
      name: Pearson Max
    - type: spearman_max
      value: 0.8457233262812305
      name: Spearman Max
---

# SentenceTransformer based on pierreinalco/distilbert-base-uncased-sts

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pierreinalco/distilbert-base-uncased-sts](https://huggingface.co/pierreinalco/distilbert-base-uncased-sts). It maps sentences & paragraphs to a 768-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:** [pierreinalco/distilbert-base-uncased-sts](https://huggingface.co/pierreinalco/distilbert-base-uncased-sts) <!-- at revision 5c3e1e82bd154604c8803ea705b7bc57712eab5b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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 = [
    'Fossil fuel reserves are finite and will eventually be depleted.',
    'Trace fossils, like footprints and burrows, reveal the behavior of ancient organisms.',
    'Electric trains are more environmentally friendly compared to diesel-powered ones.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.92       |
| **spearman_cosine** | **0.8477** |
| pearson_manhattan   | 0.9223     |
| spearman_manhattan  | 0.8456     |
| pearson_euclidean   | 0.9226     |
| spearman_euclidean  | 0.8456     |
| pearson_dot         | 0.9113     |
| spearman_dot        | 0.8382     |
| pearson_max         | 0.9226     |
| spearman_max        | 0.8477     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9125     |
| **spearman_cosine** | **0.8454** |
| pearson_manhattan   | 0.9161     |
| spearman_manhattan  | 0.8454     |
| pearson_euclidean   | 0.9165     |
| spearman_euclidean  | 0.8457     |
| pearson_dot         | 0.903      |
| spearman_dot        | 0.8319     |
| pearson_max         | 0.9165     |
| spearman_max        | 0.8457     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 19,352 training samples
* Columns: <code>s1</code>, <code>s2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | s1                                                                                 | s2                                                                                 | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 19.85 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 20.47 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>0: ~51.40%</li><li>1: ~48.60%</li></ul> |
* Samples:
  | s1                                                                                                       | s2                                                                                                                | label          |
  |:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Resources and funding are essential for the successful rollout of any new curriculum.</code>       | <code>For any new curriculum to be successfully rolled out, it is essential to have resources and funding.</code> | <code>1</code> |
  | <code>Upgrading to LED lighting is a simple step toward improving energy efficiency in buildings.</code> | <code>Upgrading to new software is a simple step toward improving technology adoption in companies.</code>        | <code>0</code> |
  | <code>Ethnicity and language often intersect in interesting and complex ways.</code>                     | <code>Ethnicity and culture often diverge in unexpected and straightforward ways.</code>                          | <code>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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 2,419 evaluation samples
* Columns: <code>s1</code>, <code>s2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | s1                                                                                 | s2                                                                                 | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 19.91 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 20.41 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~52.90%</li><li>1: ~47.10%</li></ul> |
* Samples:
  | s1                                                                                                                                                     | s2                                                                                                                                               | label          |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>[SYNTAX] Consuming too much processed sugar can lead to insulin resistance and diabetes.</code>                                                  | <code>[SYNTAX] Drinking too much water can help maintain proper hydration and overall health.</code>                                             | <code>1</code> |
  | <code>Neutral tones and minimalist designs are staples of gender-neutral fashion. </code>                                                              | <code>Colorful patterns and intricate designs are staples of traditional ceremonial attire.</code>                                               | <code>0</code> |
  | <code>[SYNTAX] Policies focusing on sustainable agriculture practices are essential for ensuring food security in the face of climate change.  </code> | <code>[SYNTAX] Ensuring food security amidst climate change requires critical policies that emphasize sustainable agricultural practices.</code> | <code>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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 1.0
- `num_train_epochs`: 10
- `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`: 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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | custom-dev_spearman_cosine | custom-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
| 0.3300 | 100  | 0.2137        | 0.0971 | 0.8252                     | -                           |
| 0.6601 | 200  | 0.0722        | 0.0516 | 0.8445                     | -                           |
| 0.9901 | 300  | 0.0503        | 0.0440 | 0.8480                     | -                           |
| 1.3201 | 400  | 0.0353        | 0.0417 | 0.8479                     | -                           |
| 1.6502 | 500  | 0.032         | 0.0388 | 0.8500                     | -                           |
| 1.9802 | 600  | 0.0312        | 0.0375 | 0.8484                     | -                           |
| 2.3102 | 700  | 0.0175        | 0.0380 | 0.8494                     | -                           |
| 2.6403 | 800  | 0.016         | 0.0368 | 0.8486                     | -                           |
| 2.9703 | 900  | 0.0158        | 0.0367 | 0.8486                     | -                           |
| 3.3003 | 1000 | 0.0087        | 0.0394 | 0.8463                     | -                           |
| 3.6304 | 1100 | 0.0086        | 0.0371 | 0.8463                     | -                           |
| 3.9604 | 1200 | 0.0098        | 0.0368 | 0.8475                     | -                           |
| 4.2904 | 1300 | 0.0055        | 0.0384 | 0.8496                     | -                           |
| 4.6205 | 1400 | 0.0057        | 0.0379 | 0.8466                     | -                           |
| 4.9505 | 1500 | 0.0057        | 0.0389 | 0.8473                     | -                           |
| 5.2805 | 1600 | 0.0037        | 0.0391 | 0.8482                     | -                           |
| 5.6106 | 1700 | 0.0042        | 0.0379 | 0.8477                     | -                           |
| 5.9406 | 1800 | 0.0039        | 0.0380 | 0.8479                     | -                           |
| 6.2706 | 1900 | 0.0026        | 0.0390 | 0.8477                     | -                           |
| 6.6007 | 2000 | 0.0028        | 0.0390 | 0.8475                     | -                           |
| 6.9307 | 2100 | 0.0031        | 0.0385 | 0.8473                     | -                           |
| 7.2607 | 2200 | 0.0022        | 0.0393 | 0.8473                     | -                           |
| 7.5908 | 2300 | 0.0021        | 0.0391 | 0.8470                     | -                           |
| 7.9208 | 2400 | 0.002         | 0.0387 | 0.8482                     | -                           |
| 8.2508 | 2500 | 0.0013        | 0.0389 | 0.8482                     | -                           |
| 8.5809 | 2600 | 0.0014        | 0.0392 | 0.8484                     | -                           |
| 8.9109 | 2700 | 0.0018        | 0.0390 | 0.8479                     | -                           |
| 9.2409 | 2800 | 0.0015        | 0.0393 | 0.8480                     | -                           |
| 9.5710 | 2900 | 0.0012        | 0.0393 | 0.8479                     | -                           |
| 9.9010 | 3000 | 0.0013        | 0.0394 | 0.8477                     | -                           |
| 10.0   | 3030 | -             | -      | -                          | 0.8454                      |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## 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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