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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- 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: The gate is yellow.
sentences:
- A yellow dog is playing in the snow.
- A turtle walks over the ground.
- Three men are on stage playing guitars.
- source_sentence: A woman is reading.
sentences:
- A woman is writing something.
- A tiger walks around aimlessly.
- Gunmen 'kill 10 tourists' in Kashmir
- source_sentence: A man jumping rope
sentences:
- A man is climbing a rope.
- Bombings kill 19 people in Iraq
- Kittens are eating from dishes.
- source_sentence: A baby is laughing.
sentences:
- A baby is crawling happily.
- Kittens are eating from dishes.
- SFG meeting reviews situation in Mali
- source_sentence: A man shoots a man.
sentences:
- A man is shooting off guns.
- A man is erasing a chalk board.
- A girl is riding a bicycle.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 134.46101750442273
energy_consumed: 0.34592314293320514
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: 1.296
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 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.8481251400932781
name: Pearson Cosine
- type: spearman_cosine
value: 0.851870210632031
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8393267568646925
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8384807951588668
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8409860761844343
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8402437232149903
name: Spearman Euclidean
- type: pearson_dot
value: 0.778375740024104
name: Pearson Dot
- type: spearman_dot
value: 0.7779671330832745
name: Spearman Dot
- type: pearson_max
value: 0.8481251400932781
name: Pearson Max
- type: spearman_max
value: 0.851870210632031
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8481027005283404
name: Pearson Cosine
- type: spearman_cosine
value: 0.8523762836460506
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8386304289845581
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8377488866945335
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8402060724091132
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8394674780683281
name: Spearman Euclidean
- type: pearson_dot
value: 0.7711669414347555
name: Pearson Dot
- type: spearman_dot
value: 0.7713442697629354
name: Spearman Dot
- type: pearson_max
value: 0.8481027005283404
name: Pearson Max
- type: spearman_max
value: 0.8523762836460506
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.842129976172463
name: Pearson Cosine
- type: spearman_cosine
value: 0.8488334736505414
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8313278330554295
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8315716535622544
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8333448222091957
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8335338271135746
name: Spearman Euclidean
- type: pearson_dot
value: 0.7445817504026263
name: Pearson Dot
- type: spearman_dot
value: 0.7450058498333884
name: Spearman Dot
- type: pearson_max
value: 0.842129976172463
name: Pearson Max
- type: spearman_max
value: 0.8488334736505414
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8346971467711455
name: Pearson Cosine
- type: spearman_cosine
value: 0.8445473333837453
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8240728025222037
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8248062249521573
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8254381823447683
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8261820268848477
name: Spearman Euclidean
- type: pearson_dot
value: 0.7083986436033697
name: Pearson Dot
- type: spearman_dot
value: 0.7093343189476312
name: Spearman Dot
- type: pearson_max
value: 0.8346971467711455
name: Pearson Max
- type: spearman_max
value: 0.8445473333837453
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.8201235619233855
name: Pearson Cosine
- type: spearman_cosine
value: 0.8352180907883887
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8032422421113089
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8047180797117756
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8059536263441476
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8072309964597537
name: Spearman Euclidean
- type: pearson_dot
value: 0.6360301824635421
name: Pearson Dot
- type: spearman_dot
value: 0.6388601952951507
name: Spearman Dot
- type: pearson_max
value: 0.8201235619233855
name: Pearson Max
- type: spearman_max
value: 0.8352180907883887
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.8262197279185375
name: Pearson Cosine
- type: spearman_cosine
value: 0.8297611922199533
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8103738584802076
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8032653500693283
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8113711464219397
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8047844488402207
name: Spearman Euclidean
- type: pearson_dot
value: 0.7351063083543349
name: Pearson Dot
- type: spearman_dot
value: 0.7222898603318773
name: Spearman Dot
- type: pearson_max
value: 0.8262197279185375
name: Pearson Max
- type: spearman_max
value: 0.8297611922199533
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8265289700873992
name: Pearson Cosine
- type: spearman_cosine
value: 0.8303420710627304
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8092042518460232
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8021561300791633
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8099517575676378
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8034311442407586
name: Spearman Euclidean
- type: pearson_dot
value: 0.7239156858292818
name: Pearson Dot
- type: spearman_dot
value: 0.7141021600172974
name: Spearman Dot
- type: pearson_max
value: 0.8265289700873992
name: Pearson Max
- type: spearman_max
value: 0.8303420710627304
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8247713863827557
name: Pearson Cosine
- type: spearman_cosine
value: 0.8304669772286988
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8012313573943666
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7951476656544464
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8028104839960224
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7974260171623634
name: Spearman Euclidean
- type: pearson_dot
value: 0.7011271518071694
name: Pearson Dot
- type: spearman_dot
value: 0.6946104528279369
name: Spearman Dot
- type: pearson_max
value: 0.8247713863827557
name: Pearson Max
- type: spearman_max
value: 0.8304669772286988
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8205553018873636
name: Pearson Cosine
- type: spearman_cosine
value: 0.8283987535951244
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7931877193499666
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7878356187942884
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7946730313407452
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7891423743206649
name: Spearman Euclidean
- type: pearson_dot
value: 0.6617612604436709
name: Pearson Dot
- type: spearman_dot
value: 0.658567255717814
name: Spearman Dot
- type: pearson_max
value: 0.8205553018873636
name: Pearson Max
- type: spearman_max
value: 0.8283987535951244
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.8118818737650724
name: Pearson Cosine
- type: spearman_cosine
value: 0.8241392189948019
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7761319753952881
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7738169467058665
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7777045912119006
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7745630850628562
name: Spearman Euclidean
- type: pearson_dot
value: 0.5934162536230442
name: Pearson Dot
- type: spearman_dot
value: 0.5884207612393454
name: Spearman Dot
- type: pearson_max
value: 0.8118818737650724
name: Pearson Max
- type: spearman_max
value: 0.8241392189948019
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-matryoshka-v3")
# Run inference
sentences = [
'A man shoots a man.',
'A man is shooting off guns.',
'A man is erasing a chalk board.',
]
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-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8481 |
| **spearman_cosine** | **0.8519** |
| pearson_manhattan | 0.8393 |
| spearman_manhattan | 0.8385 |
| pearson_euclidean | 0.841 |
| spearman_euclidean | 0.8402 |
| pearson_dot | 0.7784 |
| spearman_dot | 0.778 |
| pearson_max | 0.8481 |
| spearman_max | 0.8519 |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8481 |
| **spearman_cosine** | **0.8524** |
| pearson_manhattan | 0.8386 |
| spearman_manhattan | 0.8377 |
| pearson_euclidean | 0.8402 |
| spearman_euclidean | 0.8395 |
| pearson_dot | 0.7712 |
| spearman_dot | 0.7713 |
| pearson_max | 0.8481 |
| spearman_max | 0.8524 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8421 |
| **spearman_cosine** | **0.8488** |
| pearson_manhattan | 0.8313 |
| spearman_manhattan | 0.8316 |
| pearson_euclidean | 0.8333 |
| spearman_euclidean | 0.8335 |
| pearson_dot | 0.7446 |
| spearman_dot | 0.745 |
| pearson_max | 0.8421 |
| spearman_max | 0.8488 |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8347 |
| **spearman_cosine** | **0.8445** |
| pearson_manhattan | 0.8241 |
| spearman_manhattan | 0.8248 |
| pearson_euclidean | 0.8254 |
| spearman_euclidean | 0.8262 |
| pearson_dot | 0.7084 |
| spearman_dot | 0.7093 |
| pearson_max | 0.8347 |
| spearman_max | 0.8445 |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8201 |
| **spearman_cosine** | **0.8352** |
| pearson_manhattan | 0.8032 |
| spearman_manhattan | 0.8047 |
| pearson_euclidean | 0.806 |
| spearman_euclidean | 0.8072 |
| pearson_dot | 0.636 |
| spearman_dot | 0.6389 |
| pearson_max | 0.8201 |
| spearman_max | 0.8352 |
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8262 |
| **spearman_cosine** | **0.8298** |
| pearson_manhattan | 0.8104 |
| spearman_manhattan | 0.8033 |
| pearson_euclidean | 0.8114 |
| spearman_euclidean | 0.8048 |
| pearson_dot | 0.7351 |
| spearman_dot | 0.7223 |
| pearson_max | 0.8262 |
| spearman_max | 0.8298 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8265 |
| **spearman_cosine** | **0.8303** |
| pearson_manhattan | 0.8092 |
| spearman_manhattan | 0.8022 |
| pearson_euclidean | 0.81 |
| spearman_euclidean | 0.8034 |
| pearson_dot | 0.7239 |
| spearman_dot | 0.7141 |
| pearson_max | 0.8265 |
| spearman_max | 0.8303 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8248 |
| **spearman_cosine** | **0.8305** |
| pearson_manhattan | 0.8012 |
| spearman_manhattan | 0.7951 |
| pearson_euclidean | 0.8028 |
| spearman_euclidean | 0.7974 |
| pearson_dot | 0.7011 |
| spearman_dot | 0.6946 |
| pearson_max | 0.8248 |
| spearman_max | 0.8305 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8206 |
| **spearman_cosine** | **0.8284** |
| pearson_manhattan | 0.7932 |
| spearman_manhattan | 0.7878 |
| pearson_euclidean | 0.7947 |
| spearman_euclidean | 0.7891 |
| pearson_dot | 0.6618 |
| spearman_dot | 0.6586 |
| pearson_max | 0.8206 |
| spearman_max | 0.8284 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8119 |
| **spearman_cosine** | **0.8241** |
| pearson_manhattan | 0.7761 |
| spearman_manhattan | 0.7738 |
| pearson_euclidean | 0.7777 |
| spearman_euclidean | 0.7746 |
| pearson_dot | 0.5934 |
| spearman_dot | 0.5884 |
| pearson_max | 0.8119 |
| spearman_max | 0.8241 |
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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>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"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>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"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-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 100 | 19.9245 | 11.3900 | 0.7772 | 0.7998 | 0.8049 | 0.7902 | 0.7919 | - | - | - | - | - |
| 0.0459 | 200 | 10.6055 | 11.1510 | 0.7809 | 0.7996 | 0.8055 | 0.7954 | 0.7954 | - | - | - | - | - |
| 0.0688 | 300 | 9.6389 | 11.1229 | 0.7836 | 0.8029 | 0.8114 | 0.7923 | 0.8083 | - | - | - | - | - |
| 0.0918 | 400 | 8.6917 | 11.0299 | 0.7976 | 0.8117 | 0.8142 | 0.8002 | 0.8087 | - | - | - | - | - |
| 0.1147 | 500 | 8.3064 | 11.3586 | 0.7895 | 0.8058 | 0.8120 | 0.7978 | 0.8065 | - | - | - | - | - |
| 0.1376 | 600 | 7.8026 | 11.5047 | 0.7876 | 0.8015 | 0.8065 | 0.7934 | 0.8016 | - | - | - | - | - |
| 0.1606 | 700 | 7.9978 | 11.5823 | 0.7944 | 0.8067 | 0.8072 | 0.7994 | 0.8045 | - | - | - | - | - |
| 0.1835 | 800 | 6.9249 | 11.5862 | 0.7945 | 0.8054 | 0.8085 | 0.8012 | 0.8033 | - | - | - | - | - |
| 0.2065 | 900 | 7.1059 | 11.2365 | 0.7895 | 0.8035 | 0.8072 | 0.7956 | 0.8031 | - | - | - | - | - |
| 0.2294 | 1000 | 6.5483 | 11.3770 | 0.7853 | 0.7994 | 0.8039 | 0.7894 | 0.8024 | - | - | - | - | - |
| 0.2524 | 1100 | 6.6684 | 11.5038 | 0.7968 | 0.8087 | 0.8115 | 0.8002 | 0.8065 | - | - | - | - | - |
| 0.2753 | 1200 | 6.4661 | 11.4057 | 0.7980 | 0.8082 | 0.8103 | 0.8057 | 0.8070 | - | - | - | - | - |
| 0.2982 | 1300 | 6.501 | 11.2521 | 0.7974 | 0.8100 | 0.8111 | 0.8025 | 0.8079 | - | - | - | - | - |
| 0.3212 | 1400 | 6.0769 | 11.1458 | 0.7971 | 0.8103 | 0.8124 | 0.7982 | 0.8082 | - | - | - | - | - |
| 0.3441 | 1500 | 6.1919 | 11.3180 | 0.8039 | 0.8129 | 0.8144 | 0.8094 | 0.8098 | - | - | - | - | - |
| 0.3671 | 1600 | 5.8213 | 11.6196 | 0.7924 | 0.8072 | 0.8090 | 0.8003 | 0.8012 | - | - | - | - | - |
| 0.3900 | 1700 | 5.534 | 11.0700 | 0.7979 | 0.8104 | 0.8132 | 0.8028 | 0.8101 | - | - | - | - | - |
| 0.4129 | 1800 | 5.7536 | 11.0916 | 0.7934 | 0.8087 | 0.8149 | 0.8008 | 0.8085 | - | - | - | - | - |
| 0.4359 | 1900 | 5.3778 | 11.2658 | 0.7942 | 0.8084 | 0.8104 | 0.7980 | 0.8049 | - | - | - | - | - |
| 0.4588 | 2000 | 5.4925 | 11.4851 | 0.7932 | 0.8062 | 0.8086 | 0.7932 | 0.8057 | - | - | - | - | - |
| 0.4818 | 2100 | 5.3125 | 11.4833 | 0.7987 | 0.8119 | 0.8154 | 0.8012 | 0.8124 | - | - | - | - | - |
| 0.5047 | 2200 | 5.1914 | 11.2848 | 0.7784 | 0.7971 | 0.8037 | 0.7911 | 0.8004 | - | - | - | - | - |
| 0.5276 | 2300 | 5.2921 | 11.5364 | 0.7698 | 0.7910 | 0.7974 | 0.7839 | 0.7900 | - | - | - | - | - |
| 0.5506 | 2400 | 5.288 | 11.3944 | 0.7873 | 0.8011 | 0.8051 | 0.7877 | 0.8003 | - | - | - | - | - |
| 0.5735 | 2500 | 5.3697 | 11.4532 | 0.7949 | 0.8077 | 0.8111 | 0.7955 | 0.8069 | - | - | - | - | - |
| 0.5965 | 2600 | 5.1521 | 11.2788 | 0.7973 | 0.8095 | 0.8130 | 0.7940 | 0.8088 | - | - | - | - | - |
| 0.6194 | 2700 | 5.2316 | 11.2472 | 0.7948 | 0.8077 | 0.8102 | 0.7939 | 0.8053 | - | - | - | - | - |
| 0.6423 | 2800 | 5.2599 | 11.4171 | 0.7882 | 0.8029 | 0.8065 | 0.7888 | 0.8019 | - | - | - | - | - |
| 0.6653 | 2900 | 5.4052 | 11.4026 | 0.7871 | 0.8005 | 0.8021 | 0.7833 | 0.7985 | - | - | - | - | - |
| 0.6882 | 3000 | 5.3474 | 11.2084 | 0.7895 | 0.8047 | 0.8079 | 0.7928 | 0.8050 | - | - | - | - | - |
| 0.7112 | 3100 | 5.0336 | 11.3999 | 0.8023 | 0.8150 | 0.8182 | 0.8024 | 0.8168 | - | - | - | - | - |
| 0.7341 | 3200 | 5.2496 | 11.2307 | 0.8015 | 0.8137 | 0.8167 | 0.8000 | 0.8140 | - | - | - | - | - |
| 0.7571 | 3300 | 3.8712 | 10.9468 | 0.8396 | 0.8440 | 0.8471 | 0.8284 | 0.8479 | - | - | - | - | - |
| 0.7800 | 3400 | 2.7068 | 10.9292 | 0.8414 | 0.8453 | 0.8489 | 0.8305 | 0.8497 | - | - | - | - | - |
| 0.8029 | 3500 | 2.3418 | 10.8626 | 0.8427 | 0.8467 | 0.8504 | 0.8322 | 0.8504 | - | - | - | - | - |
| 0.8259 | 3600 | 2.2419 | 10.9065 | 0.8421 | 0.8467 | 0.8504 | 0.8320 | 0.8502 | - | - | - | - | - |
| 0.8488 | 3700 | 2.125 | 10.9517 | 0.8424 | 0.8472 | 0.8509 | 0.8324 | 0.8510 | - | - | - | - | - |
| 0.8718 | 3800 | 1.9942 | 11.0142 | 0.8438 | 0.8482 | 0.8519 | 0.8337 | 0.8517 | - | - | - | - | - |
| 0.8947 | 3900 | 2.031 | 10.9662 | 0.8433 | 0.8480 | 0.8519 | 0.8340 | 0.8515 | - | - | - | - | - |
| 0.9176 | 4000 | 1.9734 | 11.0054 | 0.8452 | 0.8495 | 0.8531 | 0.8354 | 0.8528 | - | - | - | - | - |
| 0.9406 | 4100 | 1.9468 | 11.0183 | 0.8447 | 0.8490 | 0.8526 | 0.8348 | 0.8522 | - | - | - | - | - |
| 0.9635 | 4200 | 1.9008 | 11.0154 | 0.8445 | 0.8485 | 0.8521 | 0.8352 | 0.8517 | - | - | - | - | - |
| 0.9865 | 4300 | 1.8511 | 10.9966 | 0.8445 | 0.8488 | 0.8524 | 0.8352 | 0.8519 | - | - | - | - | - |
| 1.0 | 4359 | - | - | - | - | - | - | - | 0.8284 | 0.8305 | 0.8303 | 0.8241 | 0.8298 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.346 kWh
- **Carbon Emitted**: 0.134 kg of CO2
- **Hours Used**: 1.296 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",
}
```
#### 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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