---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- loss:Matryoshka2dLoss
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: distilbert/distilroberta-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: A woman is reading.
  sentences:
  - A woman is writing something.
  - A man helps a boy ride a bike.
  - A group wading across a ditch
- source_sentence: A man shoots a man.
  sentences:
  - A man with a pistol shoots another man.
  - Suicide bomber strikes in Syria
  - China and Taiwan hold historic talks
- source_sentence: A boy is vacuuming.
  sentences:
  - A little boy is vacuuming the floor.
  - 'Breivik: Jail term ''ridiculous'''
  - Glorious triple-gold night for Britain
- source_sentence: A man is spitting.
  sentences:
  - A man is speaking.
  - The boy is jumping into a lake.
  - 10 Things to Know for Thursday
- source_sentence: A plane in the sky.
  sentences:
  - Two airplanes in the sky.
  - Nelson Mandela undergoes surgery
  - Nelson Mandela undergoes surgery
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 69.2573690422145
  energy_consumed: 0.1781760038338226
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.626
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.8395203447657347
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8424556124488326
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8432537220190851
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8435994230515586
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8440900768179745
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8449067313707376
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.763767029856877
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7569706383510251
      name: Spearman Dot
    - type: pearson_max
      value: 0.8440900768179745
      name: Pearson Max
    - type: spearman_max
      value: 0.8449067313707376
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8186702838538092
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8170686920551
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8117192659894803
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.804879002947593
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8127154744140831
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8058410028545979
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7396245702595934
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7256120569318246
      name: Spearman Dot
    - type: pearson_max
      value: 0.8186702838538092
      name: Pearson Max
    - type: spearman_max
      value: 0.8170686920551
      name: Spearman Max
---

# SentenceTransformer based on distilbert/distilroberta-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) dataset. 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/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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: RobertaModel 
  (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("tomaarsen/distilroberta-base-nli-2d-matryoshka")
# Run inference
sentences = [
    'A plane in the sky.',
    'Two airplanes in the sky.',
    'Nelson Mandela undergoes surgery',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8395     |
| **spearman_cosine** | **0.8425** |
| pearson_manhattan   | 0.8433     |
| spearman_manhattan  | 0.8436     |
| pearson_euclidean   | 0.8441     |
| spearman_euclidean  | 0.8449     |
| pearson_dot         | 0.7638     |
| spearman_dot        | 0.757      |
| pearson_max         | 0.8441     |
| spearman_max        | 0.8449     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8187     |
| **spearman_cosine** | **0.8171** |
| pearson_manhattan   | 0.8117     |
| spearman_manhattan  | 0.8049     |
| pearson_euclidean   | 0.8127     |
| spearman_euclidean  | 0.8058     |
| pearson_dot         | 0.7396     |
| spearman_dot        | 0.7256     |
| pearson_max         | 0.8187     |
| spearman_max        | 0.8171     |

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## Training Details

### Training Dataset

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [65dd388](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1.0,
      "prior_layers_weight": 1.0,
      "kl_div_weight": 1.0,
      "kl_temperature": 0.3,
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": 1
  }
  ```

### Evaluation Dataset

#### sentence-transformers/stsb

* Dataset: [sentence-transformers/stsb](https://huggingface.co./datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co./datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation 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: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1.0,
      "prior_layers_weight": 1.0,
      "kl_div_weight": 1.0,
      "kl_temperature": 0.3,
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": 1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: False
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: 1
- `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
- `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`: None
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
| 0.0229 | 100  | 6.2779        | 3.9959 | 0.8008                  | -                        |
| 0.0459 | 200  | 4.3212        | 3.5818 | 0.7956                  | -                        |
| 0.0688 | 300  | 3.7135        | 3.4422 | 0.7940                  | -                        |
| 0.0918 | 400  | 3.5567        | 3.5458 | 0.7951                  | -                        |
| 0.1147 | 500  | 3.1297        | 3.1253 | 0.8050                  | -                        |
| 0.1376 | 600  | 2.7001        | 3.4366 | 0.7996                  | -                        |
| 0.1606 | 700  | 2.8664        | 3.6609 | 0.8033                  | -                        |
| 0.1835 | 800  | 2.6656        | 3.3736 | 0.7975                  | -                        |
| 0.2065 | 900  | 2.633         | 3.3735 | 0.8076                  | -                        |
| 0.2294 | 1000 | 2.4335        | 3.6499 | 0.7996                  | -                        |
| 0.2524 | 1100 | 2.4165        | 3.6301 | 0.8015                  | -                        |
| 0.2753 | 1200 | 2.2942        | 3.1541 | 0.7994                  | -                        |
| 0.2982 | 1300 | 2.2402        | 3.4284 | 0.7977                  | -                        |
| 0.3212 | 1400 | 2.2148        | 3.3775 | 0.7988                  | -                        |
| 0.3441 | 1500 | 2.2285        | 3.6097 | 0.8016                  | -                        |
| 0.3671 | 1600 | 2.0591        | 3.3839 | 0.7926                  | -                        |
| 0.3900 | 1700 | 2.0253        | 3.1113 | 0.7981                  | -                        |
| 0.4129 | 1800 | 2.0244        | 3.8289 | 0.7954                  | -                        |
| 0.4359 | 1900 | 1.8582        | 3.3515 | 0.8000                  | -                        |
| 0.4588 | 2000 | 1.977         | 3.3054 | 0.7917                  | -                        |
| 0.4818 | 2100 | 1.9028        | 3.2166 | 0.7927                  | -                        |
| 0.5047 | 2200 | 1.8316        | 3.6504 | 0.7955                  | -                        |
| 0.5276 | 2300 | 1.8404        | 3.2822 | 0.7843                  | -                        |
| 0.5506 | 2400 | 1.8455        | 3.2583 | 0.7941                  | -                        |
| 0.5735 | 2500 | 1.9488        | 3.3970 | 0.7971                  | -                        |
| 0.5965 | 2600 | 1.9403        | 2.8948 | 0.7959                  | -                        |
| 0.6194 | 2700 | 1.8884        | 3.2227 | 0.8008                  | -                        |
| 0.6423 | 2800 | 1.8655        | 3.1948 | 0.7920                  | -                        |
| 0.6653 | 2900 | 1.8567        | 3.4374 | 0.7913                  | -                        |
| 0.6882 | 3000 | 1.8423        | 3.1118 | 0.7949                  | -                        |
| 0.7112 | 3100 | 1.7475        | 3.1359 | 0.8062                  | -                        |
| 0.7341 | 3200 | 1.8166        | 2.9927 | 0.7984                  | -                        |
| 0.7571 | 3300 | 1.5626        | 3.5143 | 0.8405                  | -                        |
| 0.7800 | 3400 | 1.2038        | 3.3909 | 0.8411                  | -                        |
| 0.8029 | 3500 | 1.1579        | 3.2458 | 0.8413                  | -                        |
| 0.8259 | 3600 | 1.0978        | 3.1592 | 0.8404                  | -                        |
| 0.8488 | 3700 | 1.0283        | 2.9557 | 0.8408                  | -                        |
| 0.8718 | 3800 | 0.9993        | 3.4073 | 0.8430                  | -                        |
| 0.8947 | 3900 | 0.9727        | 3.0570 | 0.8434                  | -                        |
| 0.9176 | 4000 | 0.9692        | 2.9357 | 0.8439                  | -                        |
| 0.9406 | 4100 | 0.9412        | 2.9494 | 0.8428                  | -                        |
| 0.9635 | 4200 | 1.0063        | 3.4047 | 0.8422                  | -                        |
| 0.9865 | 4300 | 0.9678        | 3.4299 | 0.8425                  | -                        |
| 1.0    | 4359 | -             | -      | -                       | 0.8171                   |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.178 kWh
- **Carbon Emitted**: 0.069 kg of CO2
- **Hours Used**: 0.626 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
```

#### Matryoshka2dLoss
```bibtex
@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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## Model Card Contact

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