---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CosineSimilarityLoss
base_model: distilbert/distilbert-base-uncased
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: The long jump pit had to be raked after every few attempts.
  sentences:
  - The high jumper cleared the bar on his first attempt.
  - Chemists use quantum mechanics to predict electron behavior and molecular bonding.
  - Eczema frequently appears as inflamed, tender spots on several parts of the body.
- source_sentence: Street art transforms empty rural barns into lively murals.
  sentences:
  - Traditional folk music plays a significant role in preserving a community's history.
  - '[SYNTAX] The saxophone offers the high-pitched, thrilling elements in a jazz
    trio.'
  - Atmospheric pressure decreases as you move higher above sea level.
- source_sentence: Proteins are synthesized through the process of translation.
  sentences:
  - Molecular genetics studies the structure and function of genes at a molecular
    level.
  - The mathematics lecture is a compelling method for introducing integral equations.
  - 'The correlation between air pollution and increased mortality rates is well-documented.  '
- source_sentence: '[SYNTAX] A barometer is used to measure atmospheric pressure.'
  sentences:
  - '[SYNTAX] Colonialism is a primary subject in several political science research
    papers.'
  - '[SYNTAX] Ordinary urban walls are turned into vibrant masterpieces by street
    art.'
  - Email remains a significant device for academic and fictional correspondence.
- source_sentence: Salinity gradients in oceans affect local wildlife habitats.
  sentences:
  - The distribution of wildlife in different habitats has fascinated ecologists for
    decades.
  - '[SYNTAX] Bioenergy plants can convert agricultural waste into valuable electricity.'
  - Proper management of irrigation schedules is crucial for crop health.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: custom dev
      type: custom-dev
    metrics:
    - type: pearson_cosine
      value: 0.9117000984572255
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8442193394453843
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9156511082976959
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8440889792296263
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9159884478218315
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8445673615230997
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9046139794819923
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8327655787489855
      name: Spearman Dot
    - type: pearson_max
      value: 0.9159884478218315
      name: Pearson Max
    - type: spearman_max
      value: 0.8445673615230997
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: custom test
      type: custom-test
    metrics:
    - type: pearson_cosine
      value: 0.919801732989496
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8500534773438543
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9282084953416339
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8493690342081703
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9284184436823353
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.849759760833697
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9141474471982576
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8410969822964006
      name: Spearman Dot
    - type: pearson_max
      value: 0.9284184436823353
      name: Pearson Max
    - type: spearman_max
      value: 0.8500534773438543
      name: Spearman Max
---

# SentenceTransformer based on distilbert/distilbert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **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 = [
    'Salinity gradients in oceans affect local wildlife habitats.',
    'The distribution of wildlife in different habitats has fascinated ecologists for decades.',
    '[SYNTAX] Bioenergy plants can convert agricultural waste into valuable electricity.',
]
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      | 0.9117     |
| **spearman_cosine** | **0.8442** |
| pearson_manhattan   | 0.9157     |
| spearman_manhattan  | 0.8441     |
| pearson_euclidean   | 0.916      |
| spearman_euclidean  | 0.8446     |
| pearson_dot         | 0.9046     |
| spearman_dot        | 0.8328     |
| pearson_max         | 0.916      |
| spearman_max        | 0.8446     |

#### 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      | 0.9198     |
| **spearman_cosine** | **0.8501** |
| pearson_manhattan   | 0.9282     |
| spearman_manhattan  | 0.8494     |
| pearson_euclidean   | 0.9284     |
| spearman_euclidean  | 0.8498     |
| pearson_dot         | 0.9141     |
| spearman_dot        | 0.8411     |
| pearson_max         | 0.9284     |
| spearman_max        | 0.8501     |

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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: 19.92 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.53 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>0: ~50.50%</li><li>1: ~49.50%</li></ul> |
* Samples:
  | s1                                                                                                                                       | s2                                                                                                                                       | label          |
  |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>According to labeling theory, individuals are considered deviant once society has tagged them with that label.</code>              | <code>Labeling theory posits that corporations become powerful when labeled as such by stakeholders.</code>                              | <code>0</code> |
  | <code>Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.</code> | <code>Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.</code> | <code>1</code> |
  | <code>Higher education institutions play a critical role in advancing research and innovation.</code>                                    | <code>Advancement in research and innovation is significantly driven by the contributions of higher education institutions.</code>       | <code>1</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: 11 tokens</li><li>mean: 19.91 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 20.46 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>0: ~49.70%</li><li>1: ~50.30%</li></ul> |
* Samples:
  | s1                                                                                                                          | s2                                                                                                                          | label          |
  |:----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.</code>                 | <code>Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.</code>                 | <code>1</code> |
  | <code>Urban areas frequently exhibit a different age distribution pattern compared to rural areas.</code>                   | <code>Urban areas frequently exhibit a different age distribution pattern compared to rural areas.</code>                   | <code>1</code> |
  | <code>Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.</code> | <code>Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.</code> | <code>1</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  | 0.2961        | 0.1185 | 0.8063                     | -                           |
| 0.6601 | 200  | 0.0772        | 0.0504 | 0.8461                     | -                           |
| 0.9901 | 300  | 0.0502        | 0.0454 | 0.8486                     | -                           |
| 1.3201 | 400  | 0.0376        | 0.0402 | 0.8481                     | -                           |
| 1.6502 | 500  | 0.0344        | 0.0400 | 0.8501                     | -                           |
| 1.9802 | 600  | 0.0329        | 0.0390 | 0.8518                     | -                           |
| 2.3102 | 700  | 0.0185        | 0.0387 | 0.8496                     | -                           |
| 2.6403 | 800  | 0.0164        | 0.0371 | 0.8492                     | -                           |
| 2.9703 | 900  | 0.0179        | 0.0393 | 0.8428                     | -                           |
| 3.3003 | 1000 | 0.0099        | 0.0389 | 0.8466                     | -                           |
| 3.6304 | 1100 | 0.0092        | 0.0395 | 0.8480                     | -                           |
| 3.9604 | 1200 | 0.0101        | 0.0368 | 0.8492                     | -                           |
| 4.2904 | 1300 | 0.0067        | 0.0385 | 0.8474                     | -                           |
| 4.6205 | 1400 | 0.0056        | 0.0393 | 0.8456                     | -                           |
| 4.9505 | 1500 | 0.0068        | 0.0401 | 0.8466                     | -                           |
| 5.2805 | 1600 | 0.0041        | 0.0410 | 0.8462                     | -                           |
| 5.6106 | 1700 | 0.0043        | 0.0399 | 0.8469                     | -                           |
| 5.9406 | 1800 | 0.0039        | 0.0406 | 0.8463                     | -                           |
| 6.2706 | 1900 | 0.003         | 0.0400 | 0.8456                     | -                           |
| 6.6007 | 2000 | 0.0026        | 0.0416 | 0.8438                     | -                           |
| 6.9307 | 2100 | 0.0027        | 0.0420 | 0.8437                     | -                           |
| 7.2607 | 2200 | 0.0028        | 0.0424 | 0.8449                     | -                           |
| 7.5908 | 2300 | 0.0021        | 0.0422 | 0.8458                     | -                           |
| 7.9208 | 2400 | 0.002         | 0.0414 | 0.8451                     | -                           |
| 8.2508 | 2500 | 0.0015        | 0.0421 | 0.8451                     | -                           |
| 8.5809 | 2600 | 0.0015        | 0.0427 | 0.8451                     | -                           |
| 8.9109 | 2700 | 0.0016        | 0.0429 | 0.8444                     | -                           |
| 9.2409 | 2800 | 0.0011        | 0.0432 | 0.8442                     | -                           |
| 9.5710 | 2900 | 0.0014        | 0.0432 | 0.8444                     | -                           |
| 9.9010 | 3000 | 0.0011        | 0.0432 | 0.8442                     | -                           |
| 10.0   | 3030 | -             | -      | -                          | 0.8501                      |


### 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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