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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: A baby is laughing.
sentences:
- The baby laughed in his car seat.
- A toddler walks down a hallway.
- Japan falls silent to mark 311 tragedy
- source_sentence: A woman is reading.
sentences:
- A woman is writing something.
- The man is in a deserted field.
- Obama urges no new sanctions on Iran
- source_sentence: A man is spitting.
sentences:
- A man is crying.
- A girl plays a wind instrument.
- Kids playing ball in the park.
- source_sentence: A man shoots a man.
sentences:
- A man is shooting off guns.
- A slow loris hanging on a cord.
- Finance minister promises no new taxes
- source_sentence: A boy is vacuuming.
sentences:
- A little boy is vacuuming the floor.
- A woman is applying eye shadow.
- Glorious triple-gold night for Britain
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 94.71657156591533
energy_consumed: 0.2436740010751561
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: 0.923
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 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.832978199459682
name: Pearson Cosine
- type: spearman_cosine
value: 0.8449812730792539
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8284059469034439
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8314151253676515
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8291459460248565
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8319080532683886
name: Spearman Euclidean
- type: pearson_dot
value: 0.7274279213358037
name: Pearson Dot
- type: spearman_dot
value: 0.7358272455513368
name: Spearman Dot
- type: pearson_max
value: 0.832978199459682
name: Pearson Max
- type: spearman_max
value: 0.8449812730792539
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8266436609310417
name: Pearson Cosine
- type: spearman_cosine
value: 0.841563547795295
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8250171666597236
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8276544602820737
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8255984422889996
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.828520082690129
name: Spearman Euclidean
- type: pearson_dot
value: 0.7120095981036954
name: Pearson Dot
- type: spearman_dot
value: 0.7163267085950832
name: Spearman Dot
- type: pearson_max
value: 0.8266436609310417
name: Pearson Max
- type: spearman_max
value: 0.841563547795295
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.817074395539638
name: Pearson Cosine
- type: spearman_cosine
value: 0.8355573303767316
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8175610864074738
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8212543828500742
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8175058817585
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8216438541895171
name: Spearman Euclidean
- type: pearson_dot
value: 0.6852246329807953
name: Pearson Dot
- type: spearman_dot
value: 0.6861394760239012
name: Spearman Dot
- type: pearson_max
value: 0.8175610864074738
name: Pearson Max
- type: spearman_max
value: 0.8355573303767316
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 32
type: sts-dev-32
metrics:
- type: pearson_cosine
value: 0.7963856490231295
name: Pearson Cosine
- type: spearman_cosine
value: 0.8243820415687734
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7982768947167747
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.804919985023919
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.800259304954162
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8069660671225415
name: Spearman Euclidean
- type: pearson_dot
value: 0.6311831976256888
name: Pearson Dot
- type: spearman_dot
value: 0.6277202377535699
name: Spearman Dot
- type: pearson_max
value: 0.800259304954162
name: Pearson Max
- type: spearman_max
value: 0.8243820415687734
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 16
type: sts-dev-16
metrics:
- type: pearson_cosine
value: 0.7401161630034654
name: Pearson Cosine
- type: spearman_cosine
value: 0.7871969780219474
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7609788932639057
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7761115272699121
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7645256699036285
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7794348361665424
name: Spearman Euclidean
- type: pearson_dot
value: 0.5201701018366058
name: Pearson Dot
- type: spearman_dot
value: 0.511537896780009
name: Spearman Dot
- type: pearson_max
value: 0.7645256699036285
name: Pearson Max
- type: spearman_max
value: 0.7871969780219474
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8124139776213125
name: Pearson Cosine
- type: spearman_cosine
value: 0.8211087618006394
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7835377144525455
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7821679937822867
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.785247473429926
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7839505779526579
name: Spearman Euclidean
- type: pearson_dot
value: 0.5917356859640799
name: Pearson Dot
- type: spearman_dot
value: 0.5785063907246168
name: Spearman Dot
- type: pearson_max
value: 0.8124139776213125
name: Pearson Max
- type: spearman_max
value: 0.8211087618006394
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8079155052116238
name: Pearson Cosine
- type: spearman_cosine
value: 0.8190362316108264
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7794841536695422
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7786315620445202
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.781284034387115
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7812532216784576
name: Spearman Euclidean
- type: pearson_dot
value: 0.5714349767115854
name: Pearson Dot
- type: spearman_dot
value: 0.5601824337480018
name: Spearman Dot
- type: pearson_max
value: 0.8079155052116238
name: Pearson Max
- type: spearman_max
value: 0.8190362316108264
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.7987987273687178
name: Pearson Cosine
- type: spearman_cosine
value: 0.8128864395227673
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7727564778562619
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7727917251788465
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7734618345058613
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7751195654319647
name: Spearman Euclidean
- type: pearson_dot
value: 0.5397052344713898
name: Pearson Dot
- type: spearman_dot
value: 0.5279010425382445
name: Spearman Dot
- type: pearson_max
value: 0.7987987273687178
name: Pearson Max
- type: spearman_max
value: 0.8128864395227673
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 32
type: sts-test-32
metrics:
- type: pearson_cosine
value: 0.7720012222035324
name: Pearson Cosine
- type: spearman_cosine
value: 0.7936423982593883
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7561303110063385
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7597271202292094
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7580804607973455
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7628041180101269
name: Spearman Euclidean
- type: pearson_dot
value: 0.48898156184384284
name: Pearson Dot
- type: spearman_dot
value: 0.47793665423562026
name: Spearman Dot
- type: pearson_max
value: 0.7720012222035324
name: Pearson Max
- type: spearman_max
value: 0.7936423982593883
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 16
type: sts-test-16
metrics:
- type: pearson_cosine
value: 0.7137967594997888
name: Pearson Cosine
- type: spearman_cosine
value: 0.7485767932719462
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7254358927069169
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7339448581065434
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7274341928076351
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7382083636772965
name: Spearman Euclidean
- type: pearson_dot
value: 0.385573703763858
name: Pearson Dot
- type: spearman_dot
value: 0.3749226996833225
name: Spearman Dot
- type: pearson_max
value: 0.7274341928076351
name: Pearson Max
- type: spearman_max
value: 0.7485767932719462
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 256-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:** 256 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})
(reduced_dim): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## 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-reduced")
# Run inference
sentences = [
'A boy is vacuuming.',
'A little boy is vacuuming the floor.',
'A woman is applying eye shadow.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# 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-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.833 |
| **spearman_cosine** | **0.845** |
| pearson_manhattan | 0.8284 |
| spearman_manhattan | 0.8314 |
| pearson_euclidean | 0.8291 |
| spearman_euclidean | 0.8319 |
| pearson_dot | 0.7274 |
| spearman_dot | 0.7358 |
| pearson_max | 0.833 |
| spearman_max | 0.845 |
#### 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 | 0.8266 |
| **spearman_cosine** | **0.8416** |
| pearson_manhattan | 0.825 |
| spearman_manhattan | 0.8277 |
| pearson_euclidean | 0.8256 |
| spearman_euclidean | 0.8285 |
| pearson_dot | 0.712 |
| spearman_dot | 0.7163 |
| pearson_max | 0.8266 |
| spearman_max | 0.8416 |
#### 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 | 0.8171 |
| **spearman_cosine** | **0.8356** |
| pearson_manhattan | 0.8176 |
| spearman_manhattan | 0.8213 |
| pearson_euclidean | 0.8175 |
| spearman_euclidean | 0.8216 |
| pearson_dot | 0.6852 |
| spearman_dot | 0.6861 |
| pearson_max | 0.8176 |
| spearman_max | 0.8356 |
#### Semantic Similarity
* Dataset: `sts-dev-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7964 |
| **spearman_cosine** | **0.8244** |
| pearson_manhattan | 0.7983 |
| spearman_manhattan | 0.8049 |
| pearson_euclidean | 0.8003 |
| spearman_euclidean | 0.807 |
| pearson_dot | 0.6312 |
| spearman_dot | 0.6277 |
| pearson_max | 0.8003 |
| spearman_max | 0.8244 |
#### Semantic Similarity
* Dataset: `sts-dev-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7401 |
| **spearman_cosine** | **0.7872** |
| pearson_manhattan | 0.761 |
| spearman_manhattan | 0.7761 |
| pearson_euclidean | 0.7645 |
| spearman_euclidean | 0.7794 |
| pearson_dot | 0.5202 |
| spearman_dot | 0.5115 |
| pearson_max | 0.7645 |
| spearman_max | 0.7872 |
#### 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 | 0.8124 |
| **spearman_cosine** | **0.8211** |
| pearson_manhattan | 0.7835 |
| spearman_manhattan | 0.7822 |
| pearson_euclidean | 0.7852 |
| spearman_euclidean | 0.784 |
| pearson_dot | 0.5917 |
| spearman_dot | 0.5785 |
| pearson_max | 0.8124 |
| spearman_max | 0.8211 |
#### 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 | 0.8079 |
| **spearman_cosine** | **0.819** |
| pearson_manhattan | 0.7795 |
| spearman_manhattan | 0.7786 |
| pearson_euclidean | 0.7813 |
| spearman_euclidean | 0.7813 |
| pearson_dot | 0.5714 |
| spearman_dot | 0.5602 |
| pearson_max | 0.8079 |
| spearman_max | 0.819 |
#### 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 | 0.7988 |
| **spearman_cosine** | **0.8129** |
| pearson_manhattan | 0.7728 |
| spearman_manhattan | 0.7728 |
| pearson_euclidean | 0.7735 |
| spearman_euclidean | 0.7751 |
| pearson_dot | 0.5397 |
| spearman_dot | 0.5279 |
| pearson_max | 0.7988 |
| spearman_max | 0.8129 |
#### Semantic Similarity
* Dataset: `sts-test-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.772 |
| **spearman_cosine** | **0.7936** |
| pearson_manhattan | 0.7561 |
| spearman_manhattan | 0.7597 |
| pearson_euclidean | 0.7581 |
| spearman_euclidean | 0.7628 |
| pearson_dot | 0.489 |
| spearman_dot | 0.4779 |
| pearson_max | 0.772 |
| spearman_max | 0.7936 |
#### Semantic Similarity
* Dataset: `sts-test-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7138 |
| **spearman_cosine** | **0.7486** |
| pearson_manhattan | 0.7254 |
| spearman_manhattan | 0.7339 |
| pearson_euclidean | 0.7274 |
| spearman_euclidean | 0.7382 |
| pearson_dot | 0.3856 |
| spearman_dot | 0.3749 |
| pearson_max | 0.7274 |
| spearman_max | 0.7486 |
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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": [
256,
128,
64,
32,
16
],
"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": [
256,
128,
64,
32,
16
],
"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-16_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-32_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-128_spearman_cosine | sts-test-16_spearman_cosine | sts-test-256_spearman_cosine | sts-test-32_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:---------------------------:|
| 0.0229 | 100 | 21.0363 | 14.2448 | 0.7856 | 0.7417 | 0.7873 | 0.7751 | 0.7846 | - | - | - | - | - |
| 0.0459 | 200 | 11.1093 | 13.4736 | 0.7877 | 0.7298 | 0.7861 | 0.7687 | 0.7798 | - | - | - | - | - |
| 0.0688 | 300 | 10.1847 | 13.7191 | 0.7877 | 0.7284 | 0.7898 | 0.7617 | 0.7755 | - | - | - | - | - |
| 0.0918 | 400 | 9.356 | 13.2955 | 0.7906 | 0.7385 | 0.7914 | 0.7715 | 0.7799 | - | - | - | - | - |
| 0.1147 | 500 | 8.9318 | 12.8099 | 0.7889 | 0.7346 | 0.7910 | 0.7690 | 0.7801 | - | - | - | - | - |
| 0.1376 | 600 | 8.5293 | 13.7384 | 0.7814 | 0.7362 | 0.7866 | 0.7656 | 0.7736 | - | - | - | - | - |
| 0.1606 | 700 | 8.7589 | 13.4466 | 0.7899 | 0.7467 | 0.7945 | 0.7770 | 0.7847 | - | - | - | - | - |
| 0.1835 | 800 | 7.7941 | 13.6734 | 0.7960 | 0.7526 | 0.7986 | 0.7800 | 0.7894 | - | - | - | - | - |
| 0.2065 | 900 | 7.9183 | 12.9082 | 0.7885 | 0.7470 | 0.7966 | 0.7705 | 0.7803 | - | - | - | - | - |
| 0.2294 | 1000 | 7.3669 | 13.2827 | 0.7751 | 0.7181 | 0.7822 | 0.7557 | 0.7675 | - | - | - | - | - |
| 0.2524 | 1100 | 7.6205 | 13.0227 | 0.7875 | 0.7373 | 0.7914 | 0.7730 | 0.7828 | - | - | - | - | - |
| 0.2753 | 1200 | 7.4308 | 13.4980 | 0.7844 | 0.7373 | 0.7890 | 0.7709 | 0.7755 | - | - | - | - | - |
| 0.2982 | 1300 | 7.3625 | 12.8380 | 0.7984 | 0.7520 | 0.8032 | 0.7824 | 0.7915 | - | - | - | - | - |
| 0.3212 | 1400 | 6.9421 | 12.7016 | 0.7912 | 0.7358 | 0.7960 | 0.7749 | 0.7850 | - | - | - | - | - |
| 0.3441 | 1500 | 7.0635 | 13.2198 | 0.8018 | 0.7578 | 0.8070 | 0.7861 | 0.7961 | - | - | - | - | - |
| 0.3671 | 1600 | 6.6682 | 13.3225 | 0.7906 | 0.7522 | 0.7944 | 0.7763 | 0.7849 | - | - | - | - | - |
| 0.3900 | 1700 | 6.42 | 12.7381 | 0.7984 | 0.7449 | 0.8021 | 0.7806 | 0.7911 | - | - | - | - | - |
| 0.4129 | 1800 | 6.659 | 13.0247 | 0.7947 | 0.7461 | 0.8002 | 0.7808 | 0.7876 | - | - | - | - | - |
| 0.4359 | 1900 | 6.1664 | 12.6814 | 0.7893 | 0.7312 | 0.7959 | 0.7700 | 0.7807 | - | - | - | - | - |
| 0.4588 | 2000 | 6.392 | 13.0238 | 0.7935 | 0.7354 | 0.7987 | 0.7758 | 0.7860 | - | - | - | - | - |
| 0.4818 | 2100 | 6.177 | 12.8833 | 0.7891 | 0.7428 | 0.7924 | 0.7723 | 0.7801 | - | - | - | - | - |
| 0.5047 | 2200 | 6.0411 | 12.5269 | 0.7836 | 0.7400 | 0.7875 | 0.7664 | 0.7765 | - | - | - | - | - |
| 0.5276 | 2300 | 6.1506 | 13.4349 | 0.7741 | 0.7350 | 0.7803 | 0.7556 | 0.7634 | - | - | - | - | - |
| 0.5506 | 2400 | 6.109 | 12.6996 | 0.7808 | 0.7326 | 0.7860 | 0.7663 | 0.7735 | - | - | - | - | - |
| 0.5735 | 2500 | 6.2849 | 13.2831 | 0.7874 | 0.7365 | 0.7932 | 0.7727 | 0.7794 | - | - | - | - | - |
| 0.5965 | 2600 | 6.0658 | 12.9425 | 0.7988 | 0.7481 | 0.8042 | 0.7818 | 0.7889 | - | - | - | - | - |
| 0.6194 | 2700 | 6.0646 | 13.0144 | 0.7965 | 0.7509 | 0.8010 | 0.7800 | 0.7875 | - | - | - | - | - |
| 0.6423 | 2800 | 6.0795 | 12.7602 | 0.7912 | 0.7472 | 0.7937 | 0.7778 | 0.7818 | - | - | - | - | - |
| 0.6653 | 2900 | 6.2407 | 13.2381 | 0.7829 | 0.7381 | 0.7873 | 0.7664 | 0.7765 | - | - | - | - | - |
| 0.6882 | 3000 | 6.1872 | 12.9064 | 0.7942 | 0.7516 | 0.7965 | 0.7793 | 0.7857 | - | - | - | - | - |
| 0.7112 | 3100 | 5.8987 | 12.9323 | 0.8065 | 0.7585 | 0.8087 | 0.7909 | 0.7989 | - | - | - | - | - |
| 0.7341 | 3200 | 5.996 | 13.1017 | 0.7971 | 0.7566 | 0.8005 | 0.7811 | 0.7889 | - | - | - | - | - |
| 0.7571 | 3300 | 5.3748 | 12.7601 | 0.8398 | 0.7881 | 0.8441 | 0.8232 | 0.8337 | - | - | - | - | - |
| 0.7800 | 3400 | 4.0798 | 12.7221 | 0.8400 | 0.7908 | 0.8440 | 0.8255 | 0.8342 | - | - | - | - | - |
| 0.8029 | 3500 | 3.6024 | 12.5445 | 0.8408 | 0.7892 | 0.8447 | 0.8247 | 0.8347 | - | - | - | - | - |
| 0.8259 | 3600 | 3.4619 | 12.6025 | 0.8405 | 0.7883 | 0.8442 | 0.8255 | 0.8347 | - | - | - | - | - |
| 0.8488 | 3700 | 3.2288 | 12.6636 | 0.8388 | 0.7872 | 0.8433 | 0.8226 | 0.8330 | - | - | - | - | - |
| 0.8718 | 3800 | 3.0543 | 12.6475 | 0.8386 | 0.7834 | 0.8427 | 0.8229 | 0.8330 | - | - | - | - | - |
| 0.8947 | 3900 | 3.0368 | 12.5390 | 0.8407 | 0.7845 | 0.8444 | 0.8227 | 0.8346 | - | - | - | - | - |
| 0.9176 | 4000 | 2.9591 | 12.5709 | 0.8419 | 0.7864 | 0.8456 | 0.8245 | 0.8359 | - | - | - | - | - |
| 0.9406 | 4100 | 2.944 | 12.6029 | 0.8415 | 0.7868 | 0.8452 | 0.8245 | 0.8359 | - | - | - | - | - |
| 0.9635 | 4200 | 2.9032 | 12.5514 | 0.8423 | 0.7888 | 0.8455 | 0.8254 | 0.8363 | - | - | - | - | - |
| 0.9865 | 4300 | 2.838 | 12.6054 | 0.8416 | 0.7872 | 0.8450 | 0.8244 | 0.8356 | - | - | - | - | - |
| 1.0 | 4359 | - | - | - | - | - | - | - | 0.8190 | 0.7486 | 0.8211 | 0.7936 | 0.8129 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.244 kWh
- **Carbon Emitted**: 0.095 kg of CO2
- **Hours Used**: 0.923 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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