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---
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]
```
<!--
### 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/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 |
<!--
## 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
#### 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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