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distilroberta-base-paraphrases-multi

Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- 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: She was buried in Breda .
sentences:
- Anna was buried in Breda .
- Jackson Township is a township found in Will County , Illinois .
- Saint-Genis-Pouilly is a commune in the Ain department in eastern France .
- source_sentence: Have you never been mellow? No, I'm just a grumpy sumbitch
sentences:
- How many of you retards have ever had wooopi.? Not me... I'm saving myself...
- Has anyone heard of the marketing company Vector? If so what is the company about
and is it a good place to work?
- I want to make hearts on the computer too?!? How do i do it!!!!i tried doing alt
3 but i couldn't see my heart!!!Is that normal!!
- source_sentence: Are there UFOs?
sentences:
- Who has seen aliens or UFOs?
- How do people become famous?
- How do I learn math?
- source_sentence: The dog runs.
sentences:
- A dog running.
- A man eats a sandwich.
- The people are sitting.
- source_sentence: guy on a bike
sentences:
- Man riding a bike
- A man cooks on a grill.
- The woman is indoors.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 78.69029495412121
energy_consumed: 0.2024437614268031
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: 0.601
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: 0.8415424335219892
name: Pearson Cosine
- type: spearman_cosine
value: 0.845236449663091
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8502275215819475
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.851659983857617
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8534543309306831
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8555429338051269
name: Spearman Euclidean
- type: pearson_dot
value: 0.6505488321872611
name: Pearson Dot
- type: spearman_dot
value: 0.6489555708500816
name: Spearman Dot
- type: pearson_max
value: 0.8534543309306831
name: Pearson Max
- type: spearman_max
value: 0.8555429338051269
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8105817065758533
name: Pearson Cosine
- type: spearman_cosine
value: 0.8144723448926713
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8225264118038157
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8131121443026537
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.825469313508584
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8164637881262432
name: Spearman Euclidean
- type: pearson_dot
value: 0.5910799174044387
name: Pearson Dot
- type: spearman_dot
value: 0.5760606722387962
name: Spearman Dot
- type: pearson_max
value: 0.825469313508584
name: Pearson Max
- type: spearman_max
value: 0.8164637881262432
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 [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli), [sentence-compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [simple-wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki), [altlex](https://huggingface.co/datasets/sentence-transformers/altlex), [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates), [coco-captions](https://huggingface.co/datasets/sentence-transformers/coco-captions), [flickr30k-captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions), [yahoo-answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers) and [stack-exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates) datasets. 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:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- [sentence-compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression)
- [simple-wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki)
- [altlex](https://huggingface.co/datasets/sentence-transformers/altlex)
- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- [coco-captions](https://huggingface.co/datasets/sentence-transformers/coco-captions)
- [flickr30k-captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions)
- [yahoo-answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers)
- [stack-exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates)
- **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': 128, '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-paraphrases-multi")
# Run inference
sentences = [
'guy on a bike',
'Man riding a bike',
'A man cooks on a grill.',
]
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 | 0.8415 |
| **spearman_cosine** | **0.8452** |
| pearson_manhattan | 0.8502 |
| spearman_manhattan | 0.8517 |
| pearson_euclidean | 0.8535 |
| spearman_euclidean | 0.8555 |
| pearson_dot | 0.6505 |
| spearman_dot | 0.649 |
| pearson_max | 0.8535 |
| spearman_max | 0.8555 |
#### 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 | 0.8106 |
| **spearman_cosine** | **0.8145** |
| pearson_manhattan | 0.8225 |
| spearman_manhattan | 0.8131 |
| pearson_euclidean | 0.8255 |
| spearman_euclidean | 0.8165 |
| pearson_dot | 0.5911 |
| spearman_dot | 0.5761 |
| pearson_max | 0.8255 |
| spearman_max | 0.8165 |
<!--
## 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 Datasets
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [cc6c526](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/cc6c526380e29912b5c6fa03682da4daf773c013)
* 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### sentence-compression
* Dataset: [sentence-compression](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90)
* Size: 180,000 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 33.13 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.13 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| text | simplified |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|
| <code>The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.</code> | <code>USHL completes expansion draft</code> |
| <code>Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.</code> | <code>Bud Selig to speak at St. Norbert College</code> |
| <code>It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.</code> | <code>It's cherry time</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### simple-wiki
* Dataset: [simple-wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki) at [60fd9b4](https://huggingface.co/datasets/sentence-transformers/simple-wiki/tree/60fd9b4680642ace0e2604cc2de44d376df419a7)
* Size: 102,225 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 35.19 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.1 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| text | simplified |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular the North ( i.e. , Northern Ireland ) .</code> | <code>The greatest example has been in his present job ( then , Minister for Foreign Affairs ) , where he has perforce concentrated on Anglo-Irish relations and , in particular Northern Ireland ( .</code> |
| <code>His reputation rose further when opposition leaders under parliamentary privilege alleged that Taoiseach Charles Haughey , who in January 1982 had been Leader of the Opposition , had not merely rung the President 's Office but threatened to end the career of the army officer who took the call and who , on Hillery 's explicit instructions , had refused to put through the call to the President .</code> | <code>President Hillery refused to speak to any opposition party politicians , but when Charles Haughey , who was Leader of the Opposition , had rang the President 's Office he threatened to end the career of the army officer answered and refused on Hillery 's explicit orders to put the call through to the President .</code> |
| <code>He considered returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .</code> | <code>He thought about returning to medicine , perhaps moving with his wife , Maeve ( also a doctor ) to Africa .</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### altlex
* Dataset: [altlex](https://huggingface.co/datasets/sentence-transformers/altlex) at [97eb209](https://huggingface.co/datasets/sentence-transformers/altlex/tree/97eb20963455c361d5a81c107c3596cff9e0cd82)
* Size: 112,696 training samples
* Columns: <code>text</code> and <code>simplified</code>
* Approximate statistics based on the first 1000 samples:
| | text | simplified |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 31.8 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 26.49 tokens</li><li>max: 114 tokens</li></ul> |
* Samples:
| text | simplified |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria , who was a frequent visitor .</code> | <code>A set of 31 guns , cast 1729-1749 by the first master founder at the Royal Foundry , later the Royal Arsenal , Woolwich , were used to fire salutes until 1907 , often for Queen Victoria who was a frequent visitor .</code> |
| <code>In 1929 , the building became vacant , and was given to Prince Edward , Prince of Wales , by his father , King George V . This became the Prince 's chief residence and was used extensively by him for entertaining and as a country retreat .</code> | <code>In 1929 , the building became vacant , and was given to Prince Edward , the Prince of Wales by his father , King George V . This became the Prince 's chief residence , and was used extensively by the Prince for entertaining and as a country retreat .</code> |
| <code>Additions included an octagon room in the north-east side , in which the King regularly had dinner .</code> | <code>Additions included an octagon room in the North-East side , where the King regularly had dinner .</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 101,762 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: 6 tokens</li><li>mean: 13.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.5 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.56 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
| <code>What is OnePlus One?</code> | <code>How is oneplus one?</code> | <code>Why is OnePlus One so good?</code> |
| <code>Does our mind control our emotions?</code> | <code>How do smart and successful people control their emotions?</code> | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### coco-captions
* Dataset: [coco-captions](https://huggingface.co/datasets/sentence-transformers/coco-captions) at [bd26018](https://huggingface.co/datasets/sentence-transformers/coco-captions/tree/bd2601822b9af9a41656d678ffbd5c80d81e276a)
* Size: 414,010 training samples
* Columns: <code>caption1</code> and <code>caption2</code>
* Approximate statistics based on the first 1000 samples:
| | caption1 | caption2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 13.65 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.65 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
| caption1 | caption2 |
|:-------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|
| <code>A clock that blends in with the wall hangs in a bathroom. </code> | <code>A very clean and well decorated empty bathroom</code> |
| <code>A very clean and well decorated empty bathroom</code> | <code>A bathroom with a border of butterflies and blue paint on the walls above it.</code> |
| <code>A bathroom with a border of butterflies and blue paint on the walls above it.</code> | <code>An angled view of a beautifully decorated bathroom.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### flickr30k-captions
* Dataset: [flickr30k-captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions) at [0ef0ce3](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions/tree/0ef0ce31492fd8dc161ed483a40d3c4894f9a8c1)
* Size: 158,881 training samples
* Columns: <code>caption1</code> and <code>caption2</code>
* Approximate statistics based on the first 1000 samples:
| | caption1 | caption2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.22 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.22 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| caption1 | caption2 |
|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| <code>Two men in green shirts are standing in a yard.</code> | <code>Two young, White males are outside near many bushes.</code> |
| <code>Two young, White males are outside near many bushes.</code> | <code>Two young guys with shaggy hair look at their hands while hanging out in the yard.</code> |
| <code>Two young guys with shaggy hair look at their hands while hanging out in the yard.</code> | <code>A man in a blue shirt standing in a garden.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### yahoo-answers
* Dataset: [yahoo-answers](https://huggingface.co/datasets/sentence-transformers/yahoo-answers) at [93b3605](https://huggingface.co/datasets/sentence-transformers/yahoo-answers/tree/93b3605c508cf93e3666c9d3e34640b5fe62b507)
* Size: 599,417 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 52.48 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 83.5 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>why doesn't an optical mouse work on a glass table? or even on some surfaces?</code> | <code>why doesn't an optical mouse work on a glass table? Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse. The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse. \nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion. Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.</code> |
| <code>What is the best off-road motorcycle trail ? long-distance trail throughout CA</code> | <code>What is the best off-road motorcycle trail ? i hear that the mojave road is amazing!<br />\nsearch for it online.</code> |
| <code>What is Trans Fat? How to reduce that? I heard that tras fat is bad for the body. Why is that? Where can we find it in our daily food?</code> | <code>What is Trans Fat? How to reduce that? Trans fats occur in manufactured foods during the process of partial hydrogenation, when hydrogen gas is bubbled through vegetable oil to increase shelf life and stabilize the original polyunsatured oil. The resulting fat is similar to saturated fat, which raises "bad" LDL cholesterol and can lead to clogged arteries and heart disease. \nUntil very recently, food labels were not required to list trans fats, and this health risk remained hidden to consumers. In early July, FDA regulations changed, and food labels will soon begin identifying trans fat content in processed foods.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### stack-exchange
* Dataset: [stack-exchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates) at [1c9657a](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates/tree/1c9657aec12d9e101667bb9593efcc623c4a68ff)
* Size: 304,525 training samples
* Columns: <code>title1</code> and <code>title2</code>
* Approximate statistics based on the first 1000 samples:
| | title1 | title2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.04 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.91 tokens</li><li>max: 80 tokens</li></ul> |
* Samples:
| title1 | title2 |
|:----------------------------------------------------------------------------------|:-------------------------------------------------------------|
| <code>what is the advantage of using the GPU rendering options in Android?</code> | <code>Can anyone explain all these Developer Options?</code> |
| <code>Blank video when converting uncompressed AVI files with ffmpeg</code> | <code>FFmpeg lossy compression problems</code> |
| <code>URL Rewriting of a query string in php</code> | <code>How to create friendly URL in php?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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
- `multi_dataset_batch_sampler`: round_robin
#### 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`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:-----------------------:|:------------------------:|
| 0.0140 | 100 | 3.739 | - | - |
| 0.0279 | 200 | 1.1317 | - | - |
| 0.0419 | 300 | 0.9645 | - | - |
| 0.0558 | 400 | 0.9053 | - | - |
| 0.0698 | 500 | 0.8889 | - | - |
| 0.0838 | 600 | 0.8741 | - | - |
| 0.0977 | 700 | 0.8329 | - | - |
| 0.1117 | 800 | 0.8331 | - | - |
| 0.1256 | 900 | 0.8241 | - | - |
| 0.1396 | 1000 | 0.7829 | 0.8460 | - |
| 0.1535 | 1100 | 0.7871 | - | - |
| 0.1675 | 1200 | 0.7521 | - | - |
| 0.1815 | 1300 | 0.7905 | - | - |
| 0.1954 | 1400 | 0.7531 | - | - |
| 0.2094 | 1500 | 0.7677 | - | - |
| 0.2233 | 1600 | 0.7745 | - | - |
| 0.2373 | 1700 | 0.7651 | - | - |
| 0.2513 | 1800 | 0.7712 | - | - |
| 0.2652 | 1900 | 0.7476 | - | - |
| 0.2792 | 2000 | 0.7814 | 0.8370 | - |
| 0.2931 | 2100 | 0.7536 | - | - |
| 0.3071 | 2200 | 0.7689 | - | - |
| 0.3210 | 2300 | 0.7656 | - | - |
| 0.3350 | 2400 | 0.7672 | - | - |
| 0.3490 | 2500 | 0.6921 | - | - |
| 0.3629 | 2600 | 0.6778 | - | - |
| 0.3769 | 2700 | 0.6844 | - | - |
| 0.3908 | 2800 | 0.6907 | - | - |
| 0.4048 | 2900 | 0.6881 | - | - |
| 0.4188 | 3000 | 0.6815 | 0.8372 | - |
| 0.4327 | 3100 | 0.6869 | - | - |
| 0.4467 | 3200 | 0.698 | - | - |
| 0.4606 | 3300 | 0.6868 | - | - |
| 0.4746 | 3400 | 0.7174 | - | - |
| 0.4886 | 3500 | 0.6714 | - | - |
| 0.5025 | 3600 | 0.6698 | - | - |
| 0.5165 | 3700 | 0.6838 | - | - |
| 0.5304 | 3800 | 0.6927 | - | - |
| 0.5444 | 3900 | 0.6628 | - | - |
| 0.5583 | 4000 | 0.6647 | 0.8367 | - |
| 0.5723 | 4100 | 0.6766 | - | - |
| 0.5863 | 4200 | 0.6987 | - | - |
| 0.6002 | 4300 | 0.6895 | - | - |
| 0.6142 | 4400 | 0.6571 | - | - |
| 0.6281 | 4500 | 0.66 | - | - |
| 0.6421 | 4600 | 0.6747 | - | - |
| 0.6561 | 4700 | 0.6495 | - | - |
| 0.6700 | 4800 | 0.6746 | - | - |
| 0.6840 | 4900 | 0.6575 | - | - |
| 0.6979 | 5000 | 0.6712 | 0.8454 | - |
| 0.7119 | 5100 | 0.6627 | - | - |
| 0.7259 | 5200 | 0.6538 | - | - |
| 0.7398 | 5300 | 0.6659 | - | - |
| 0.7538 | 5400 | 0.6551 | - | - |
| 0.7677 | 5500 | 0.6548 | - | - |
| 0.7817 | 5600 | 0.673 | - | - |
| 0.7956 | 5700 | 0.6805 | - | - |
| 0.8096 | 5800 | 0.6537 | - | - |
| 0.8236 | 5900 | 0.6826 | - | - |
| 0.8375 | 6000 | 0.7182 | 0.8370 | - |
| 0.8515 | 6100 | 0.7391 | - | - |
| 0.8654 | 6200 | 0.7006 | - | - |
| 0.8794 | 6300 | 0.6774 | - | - |
| 0.8934 | 6400 | 0.7076 | - | - |
| 0.9073 | 6500 | 0.6893 | - | - |
| 0.9213 | 6600 | 0.678 | - | - |
| 0.9352 | 6700 | 0.6703 | - | - |
| 0.9492 | 6800 | 0.675 | - | - |
| 0.9631 | 6900 | 0.6842 | - | - |
| 0.9771 | 7000 | 0.6909 | 0.8452 | - |
| 0.9911 | 7100 | 0.681 | - | - |
| 1.0 | 7164 | - | - | 0.8145 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.202 kWh
- **Carbon Emitted**: 0.079 kg of CO2
- **Hours Used**: 0.601 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",
}
```
#### 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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