Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:6661966
loss:MultipleNegativesRankingLoss
loss:CachedMultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:AnglELoss
loss:CoSENTLoss
loss:CosineSimilarityLoss
Inference Endpoints
File size: 178,237 Bytes
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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6131012
- loss:MultipleNegativesRankingLoss
- loss:CachedMultipleNegativesRankingLoss
- loss:SoftmaxLoss
- loss:CosineSimilarityLoss
base_model:
- tasksource/ModernBERT-base-nli
- answerdotai/ModernBERT-base
widget:
- source_sentence: >-
Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to
the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra
dropped the apple. Sandra went to the garden. Sandra went back to the
bedroom. Sandra went back to the office. Mary went back to the office.
Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to
the garden. Sandra put down the apple there. Mary went back to the bathroom.
Daniel travelled to the garden. Mary took the milk. Sandra grabbed the
apple. Mary left the milk there. Sandra journeyed to the bedroom. John
travelled to the office. John went back to the garden. Sandra journeyed to
the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the
milk. Mary went to the hallway. John moved to the hallway. Mary picked up
the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary
discarded the milk. John journeyed to the garden. Mary dropped the football.
Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary
travelled to the bathroom. Daniel went to the bedroom. Mary went to the
hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved
to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk.
Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went
to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden.
Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk.
Sandra put down the milk. John journeyed to the garden. Sandra went back to
the hallway. Sandra picked up the apple. Sandra got the football. Sandra
moved to the garden. Daniel moved to the bathroom. Daniel travelled to the
garden. Sandra went back to the bathroom. Sandra discarded the football.
sentences:
- In the adulthood stage, it can jump, walk, run
- The chocolate is bigger than the container.
- The football before the bathroom was in the garden.
- source_sentence: >-
Context: I am devasted.
Speaker 1: I am very devastated these days.
Speaker 2: That seems bad and I am sorry to hear that. What happened?
Speaker 1: My father day 3 weeks ago.I still can't believe.
Speaker 2: I am truly sorry to hear that. Please accept my apologies for
your loss. May he rest in peace
sentences:
- 'The main emotion of this example dialogue is: content'
- 'This text is about: genealogy'
- The intent of this example is to be offensive/disrespectful.
- source_sentence: in three distinguish’d parts, with three distinguish’d guides
sentences:
- This example is paraphrase.
- This example is neutral.
- This example is negative.
- source_sentence: A boy is playing a piano.
sentences:
- Nine killed in Syrian-linked clashes in Lebanon
- A man is singing and playing a guitar.
- My opinion is to wait until the child itself expresses a desire for this.
- source_sentence: Francis I of France was a king.
sentences:
- >-
The Apple QuickTake -LRB- codenamed Venus , Mars , Neptune -RRB- is one of
the first consumer digital camera lines .. digital camera. digital camera.
It was launched in 1994 by Apple Computer and was marketed for three years
before being discontinued in 1997 .. Apple Computer. Apple Computer. Three
models of the product were built including the 100 and 150 , both built by
Kodak ; and the 200 , built by Fujifilm .. Kodak. Kodak. Fujifilm. Fujifilm.
The QuickTake cameras had a resolution of 640 x 480 pixels maximum -LRB- 0.3
Mpx -RRB- .. resolution. Display resolution. The 200 model is only
officially compatible with the Apple Macintosh for direct connections ,
while the 100 and 150 model are compatible with both the Apple Macintosh and
Microsoft Windows .. Apple Macintosh. Apple Macintosh. Microsoft Windows.
Microsoft Windows. Because the QuickTake 200 is almost identical to the Fuji
DS-7 or to Samsung 's Kenox SSC-350N , Fuji 's software for that camera can
be used to gain Windows compatibility for the QuickTake 200 .. Some other
software replacements also exist as well as using an external reader for the
removable media of the QuickTake 200 .. Time Magazine profiled QuickTake as
`` the first consumer digital camera '' and ranked it among its `` 100
greatest and most influential gadgets from 1923 to the present '' list ..
digital camera. digital camera. Time Magazine. Time Magazine. While the
QuickTake was probably the first digicam to have wide success , technically
this is not true as the greyscale Dycam Model 1 -LRB- also marketed as the
Logitech FotoMan -RRB- was the first consumer digital camera to be sold in
the US in November 1990 .. digital camera. digital camera. greyscale.
greyscale. At least one other camera , the Fuji DS-X , was sold in Japan
even earlier , in late 1989 .
- >-
The ganglion cell layer -LRB- ganglionic layer -RRB- is a layer of the
retina that consists of retinal ganglion cells and displaced amacrine cells
.. retina. retina. In the macula lutea , the layer forms several strata ..
macula lutea. macula lutea. The cells are somewhat flask-shaped ; the
rounded internal surface of each resting on the stratum opticum , and
sending off an axon which is prolonged into it .. flask. Laboratory flask.
stratum opticum. stratum opticum. axon. axon. From the opposite end numerous
dendrites extend into the inner plexiform layer , where they branch and form
flattened arborizations at different levels .. inner plexiform layer. inner
plexiform layer. arborizations. arborizations. dendrites. dendrites. The
ganglion cells vary much in size , and the dendrites of the smaller ones as
a rule arborize in the inner plexiform layer as soon as they enter it ;
while those of the larger cells ramify close to the inner nuclear layer ..
inner plexiform layer. inner plexiform layer. dendrites. dendrites. inner
nuclear layer. inner nuclear layer
- >-
Coyote was a brand of racing chassis designed and built for the use of A. J.
Foyt 's race team in USAC Championship car racing including the Indianapolis
500 .. A. J. Foyt. A. J. Foyt. USAC. United States Auto Club. Championship
car. American Championship car racing. Indianapolis 500. Indianapolis 500.
It was used from 1966 to 1983 with Foyt himself making 141 starts in the car
, winning 25 times .. George Snider had the second most starts with 24 ..
George Snider. George Snider. Jim McElreath has the only other win with a
Coyote chassis .. Jim McElreath. Jim McElreath. Foyt drove a Coyote to
victory in the Indy 500 in 1967 and 1977 .. With Foyt 's permission , fellow
Indy 500 champion Eddie Cheever 's Cheever Racing began using the Coyote
name for his new Daytona Prototype chassis , derived from the Fabcar chassis
design that he had purchased the rights to in 2007 .. Eddie Cheever. Eddie
Cheever. Cheever Racing. Cheever Racing. Daytona Prototype. Daytona
Prototype
datasets:
- tomaarsen/natural-questions-hard-negatives
- tomaarsen/gooaq-hard-negatives
- bclavie/msmarco-500k-triplets
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
- sentence-transformers/gooaq
- sentence-transformers/natural-questions
- tasksource/merged-2l-nli
- tasksource/merged-3l-nli
- tasksource/zero-shot-label-nli
- MoritzLaurer/dataset_train_nli
- google-research-datasets/paws
- nyu-mll/glue
- mwong/fever-evidence-related
- tasksource/sts-companion
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on tasksource/ModernBERT-base-nli
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli) on the [tomaarsen/natural-questions-hard-negatives](https://huggingface.co/datasets/tomaarsen/natural-questions-hard-negatives), [tomaarsen/gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives), [bclavie/msmarco-500k-triplets](https://huggingface.co/datasets/bclavie/msmarco-500k-triplets), [sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [sentence-transformers/natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions), [merged-2l-nli](https://huggingface.co/datasets/tasksource/merged-2l-nli), [merged-3l-nli](https://huggingface.co/datasets/tasksource/merged-3l-nli), [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), [dataset_train_nli](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli), [paws/labeled_final](https://huggingface.co/datasets/paws), [glue/mrpc](https://huggingface.co/datasets/glue), [glue/qqp](https://huggingface.co/datasets/glue), [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related), [glue/stsb](https://huggingface.co/datasets/glue), sick/relatedness and [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion) 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:** [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli) <!-- at revision f07aa4a387ebb3f3b07d60086edff7646ae8b762 -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [tomaarsen/natural-questions-hard-negatives](https://huggingface.co/datasets/tomaarsen/natural-questions-hard-negatives)
- [tomaarsen/gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives)
- [bclavie/msmarco-500k-triplets](https://huggingface.co/datasets/bclavie/msmarco-500k-triplets)
- [sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- [sentence-transformers/natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- [merged-2l-nli](https://huggingface.co/datasets/tasksource/merged-2l-nli)
- [merged-3l-nli](https://huggingface.co/datasets/tasksource/merged-3l-nli)
- [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli)
- [dataset_train_nli](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli)
- [paws/labeled_final](https://huggingface.co/datasets/paws)
- [glue/mrpc](https://huggingface.co/datasets/glue)
- [glue/qqp](https://huggingface.co/datasets/glue)
- [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related)
- [glue/stsb](https://huggingface.co/datasets/glue)
- sick/relatedness
- [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion)
- **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': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("tasksource/ModernBERT-base-embed")
# Run inference
sentences = [
'Francis I of France was a king.',
"Coyote was a brand of racing chassis designed and built for the use of A. J. Foyt 's race team in USAC Championship car racing including the Indianapolis 500 .. A. J. Foyt. A. J. Foyt. USAC. United States Auto Club. Championship car. American Championship car racing. Indianapolis 500. Indianapolis 500. It was used from 1966 to 1983 with Foyt himself making 141 starts in the car , winning 25 times .. George Snider had the second most starts with 24 .. George Snider. George Snider. Jim McElreath has the only other win with a Coyote chassis .. Jim McElreath. Jim McElreath. Foyt drove a Coyote to victory in the Indy 500 in 1967 and 1977 .. With Foyt 's permission , fellow Indy 500 champion Eddie Cheever 's Cheever Racing began using the Coyote name for his new Daytona Prototype chassis , derived from the Fabcar chassis design that he had purchased the rights to in 2007 .. Eddie Cheever. Eddie Cheever. Cheever Racing. Cheever Racing. Daytona Prototype. Daytona Prototype",
"The Apple QuickTake -LRB- codenamed Venus , Mars , Neptune -RRB- is one of the first consumer digital camera lines .. digital camera. digital camera. It was launched in 1994 by Apple Computer and was marketed for three years before being discontinued in 1997 .. Apple Computer. Apple Computer. Three models of the product were built including the 100 and 150 , both built by Kodak ; and the 200 , built by Fujifilm .. Kodak. Kodak. Fujifilm. Fujifilm. The QuickTake cameras had a resolution of 640 x 480 pixels maximum -LRB- 0.3 Mpx -RRB- .. resolution. Display resolution. The 200 model is only officially compatible with the Apple Macintosh for direct connections , while the 100 and 150 model are compatible with both the Apple Macintosh and Microsoft Windows .. Apple Macintosh. Apple Macintosh. Microsoft Windows. Microsoft Windows. Because the QuickTake 200 is almost identical to the Fuji DS-7 or to Samsung 's Kenox SSC-350N , Fuji 's software for that camera can be used to gain Windows compatibility for the QuickTake 200 .. Some other software replacements also exist as well as using an external reader for the removable media of the QuickTake 200 .. Time Magazine profiled QuickTake as `` the first consumer digital camera '' and ranked it among its `` 100 greatest and most influential gadgets from 1923 to the present '' list .. digital camera. digital camera. Time Magazine. Time Magazine. While the QuickTake was probably the first digicam to have wide success , technically this is not true as the greyscale Dycam Model 1 -LRB- also marketed as the Logitech FotoMan -RRB- was the first consumer digital camera to be sold in the US in November 1990 .. digital camera. digital camera. greyscale. greyscale. At least one other camera , the Fuji DS-X , was sold in Japan even earlier , in late 1989 .",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, 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.*
-->
<!--
## 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
#### tomaarsen/natural-questions-hard-negatives
* Dataset: [tomaarsen/natural-questions-hard-negatives](https://huggingface.co/datasets/tomaarsen/natural-questions-hard-negatives) at [52dfa09](https://huggingface.co/datasets/tomaarsen/natural-questions-hard-negatives/tree/52dfa09a3d5d3f90e7e115c407ccebe30fe79764)
* Size: 96,658 training samples
* Columns: <code>query</code>, <code>answer</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string | string | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 12.52 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 137.85 tokens</li><li>max: 556 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 144.11 tokens</li><li>max: 1035 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 142.73 tokens</li><li>max: 832 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 146.37 tokens</li><li>max: 649 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 145.79 tokens</li><li>max: 549 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 142.01 tokens</li><li>max: 574 tokens</li></ul> |
* Samples:
| query | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> | <code>Brisbane Bears However, the club was still struggling off-field. One of the Bears' biggest problems was its lack of support (both on and off the field) in Melbourne, the location of most of its away matches. In mid-1996, the struggling Fitzroy Football Club collapsed due to financial pressures and was seeking to merge its assets with another club. When a merger with North Melbourne in forming the North Fitzroy Kangaroos failed to win the support of the other AFL clubs, a deal for a merger was done between Fitzroy and the Bears. The new team was known as the Brisbane Lions, based at the Gabba, with Northey as the coach of the merged club. As such, the history of the Brisbane Bears as an individual entity ended after the 1996 season, with ten seasons of competition and the third-place finish in 1996 as its best performance. The Bears last match as a separate entity was a preliminary final on Saturday 21 September 1996 at the Melbourne Cricket Ground (where the Bears played their first VF...</code> | <code>Virginia Tech–West Virginia football rivalry Virginia Tech held the trophy in six of the nine years in which it was contested, but West Virginia leads the all-time series 28–23–1. The last game was played on September 3, 2017 at FedEx Field in Landover, MD; Virginia Tech won 31–24.</code> | <code>Martin Truex Jr. To start off the Round of 12, Truex scored his 6th win of the season at Charlotte after leading 91 out of 334 laps to secure a spot for the Round of 8. Just two weeks later, he scored another win at Kansas despite having a restart violation early in the race.</code> | <code>Adelaide Football Club Star midfielder for many years Patrick Dangerfield left the club at the end of the 2015 season (a season in which he won the club's best and fairest) and Don Pyke, a former premiership player and assistant coach with West Coast who had also been an assistant coach at Adelaide from 2005 to 2006, was appointed Adelaide's senior coach for at least three years.[9] Adelaide was widely tipped to slide out of the finals in 2016[27][28][29] but the Crows proved to be one of the successes of the season, comfortably qualifying for a home elimination final and defeating North Melbourne by 62 points, before being eliminated the next week by eventual beaten grand finalists, Sydney in the semi-finals. The club had a dominant 2017 season, winning their opening six games and never falling below second place for the entire season. Adelaide claimed their second McClelland Trophy as minor premiers.[30] The Adelaide Crows entered the 2017 finals series as favourites for the premiers...</code> | <code>Battle of Appomattox Court House The Battle of Appomattox Court House (Virginia, U.S.), fought on the morning of April 9, 1865, was one of the last battles of the American Civil War (1861–1865). It was the final engagement of Confederate States Army General-in-Chief, Robert E. Lee, and his Army of Northern Virginia before it surrendered to the Union Army of the Potomac under the Commanding General of the United States, Ulysses S. Grant. Lee, having abandoned the Confederate capital of Richmond, Virginia, after the nine and one-half month Siege of Petersburg and Richmond, retreated west, hoping to join his army with the remaining Confederate forces in North Carolina, the Army of Tennessee under Gen. Joseph E. Johnston. Union infantry and cavalry forces under Gen. Philip Sheridan pursued and cut off the Confederates' retreat at the central Virginia village of Appomattox Court House. Lee launched a last-ditch attack to break through the Union forces to his front, assuming the Union forc...</code> |
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> | <code>Lover, You Should've Come Over "Lover, You Should've Come Over" is the seventh track on Jeff Buckley's album Grace. Inspired by the ending of the relationship between Buckley and Rebecca Moore,[1] it concerns the despondency of a young man growing older, finding that his actions represent a perspective he feels that he should have outgrown. Biographer and critic David Browne describes the lyrics as "confused and confusing" and the music as "a languid beauty."[1]</code> | <code>It's Christmas (All Over The World) "It's Christmas (All Over The World)" is a song recorded by Scottish singer Sheena Easton. It was released in November 1985 as the theme song from the soundtrack of Santa Claus: The Movie. The song was written by Bill House and John Hobbs.</code> | <code>The End of the World (Skeeter Davis song) "The End of the World" is a country pop song written by Arthur Kent and Sylvia Dee, for American singer Skeeter Davis. It had success in the 1960s and spawned many covers.</code> | <code>Israel Kamakawiwoʻole His voice became famous outside Hawaii when his album Facing Future was released in 1993. His medley of "Somewhere Over the Rainbow/What a Wonderful World" was released on his albums Ka ʻAnoʻi and Facing Future. It was subsequently featured in several films, television programs, and television commercials.</code> | <code>Make the World Go Away "Make the World Go Away'" is a country-popular music song composed by Hank Cochran. It has become a Top 40 popular success three times: for Timi Yuro (during 1963), for Eddy Arnold (1965), and for the brother-sister duo Donny and Marie Osmond (1975). The original version of the song was recorded by Ray Price during 1963. It has remained a country crooner standard ever since.</code> |
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> | <code>Baa, Baa, Black Sheep As with many nursery rhymes, attempts have been made to find origins and meanings for the rhyme, most which have no corroborating evidence.[1] Katherine Elwes Thomas in The Real Personages of Mother Goose (1930) suggested that the rhyme referred to resentment at the heavy taxation on wool.[5] This has particularly been taken to refer to the medieval English "Great" or "Old Custom" wool tax of 1275, which survived until the fifteenth century.[1] More recently the rhyme has been connected to the slave trade, particularly in the southern United States.[6] This explanation was advanced during debates over political correctness and the use and reform of nursery rhymes in the 1980s, but has no supporting historical evidence.[7] Rather than being negative, the wool of black sheep may have been prized as it could be made into dark cloth without dyeing.[6]</code> | <code>Raymond Group Raymond Group is an Indian branded fabric and fashion retailer, incorporated in 1925. It produces suiting fabric, with a capacity of producing 31 million meters of wool and wool-blended fabrics. Gautam Singhania is the chairman and managing director of the Raymond group.[3]</code> | <code>Silk in the Indian subcontinent Silk in the Indian subcontinent is a luxury good. In India, about 97% of the raw mulberry silk is produced in the five Indian states of Karnataka, Andhra Pradesh, Tamil Nadu, West Bengal and Jammu and Kashmir.[1] Mysore and North Bangalore, the upcoming site of a US$20 million "Silk City", contribute to a majority of silk production.[2] Another emerging silk producer is Tamil Nadu where mulberry cultivation is concentrated in Salem, Erode and Dharmapuri districts. Hyderabad, Andhra Pradesh and Gobichettipalayam, Tamil Nadu were the first locations to have automated silk reeling units.[3] yoyo quantity:::</code> | <code>F. W. Woolworth Company The two Woolworth brothers pioneered and developed merchandising, direct purchasing, sales, and customer service practices commonly used today. Despite its growing to be one of the largest retail chains in the world through most of the 20th century, increased competition led to its decline beginning in the 1980s, while its sporting goods division grew. The chain went out of business in July 1997, when the company decided to focus primarily on sporting goods and renamed itself Venator Group. By 2001, the company focused exclusively on the sporting goods market, changing its name to the present Foot Locker, Inc., changing its ticker symbol from its familiar Z in 2003 to its present ticker (NYSE:Â FL).</code> | <code>Silk Silk's absorbency makes it comfortable to wear in warm weather and while active. Its low conductivity keeps warm air close to the skin during cold weather. It is often used for clothing such as shirts, ties, blouses, formal dresses, high fashion clothes, lining, lingerie, pajamas, robes, dress suits, sun dresses and Eastern folk costumes. For practical use, silk is excellent as clothing that protects from many biting insects that would ordinarily pierce clothing, such as mosquitoes and horseflies.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### tomaarsen/gooaq-hard-negatives
* Dataset: [tomaarsen/gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 500,000 training samples
* Columns: <code>question</code>, <code>answer</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string | string | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.99 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 57.82 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.42 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 56.84 tokens</li><li>max: 120 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.08 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 57.54 tokens</li><li>max: 129 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 58.23 tokens</li><li>max: 195 tokens</li></ul> |
* Samples:
| question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> | <code>Secondly, metoprolol and metoprolol ER have different brand-name equivalents: Brand version of metoprolol: Lopressor. Brand version of metoprolol ER: Toprol XL.</code> | <code>Pill with imprint 1 is White, Round and has been identified as Metoprolol Tartrate 25 mg.</code> | <code>Interactions between your drugs No interactions were found between Allergy Relief and metoprolol. This does not necessarily mean no interactions exist. Always consult your healthcare provider.</code> | <code>Metoprolol is a type of medication called a beta blocker. It works by relaxing blood vessels and slowing heart rate, which improves blood flow and lowers blood pressure. Metoprolol can also improve the likelihood of survival after a heart attack.</code> | <code>Metoprolol starts to work after about 2 hours, but it can take up to 1 week to fully take effect. You may not feel any different when you take metoprolol, but this doesn't mean it's not working. It's important to keep taking your medicine.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> | <code>I Saw the Light. Showcasing the unique talent and musical influence of country-western artist Hank Williams, this candid biography also sheds light on the legacy of drug abuse and tormented relationships that contributes to the singer's legend.</code> | <code>(Read our ranking of his top 10.) And while Howard dresses the part of director, any notion of him as a tortured auteur or dictatorial taskmasker — the clichés of the Hollywood director — are tossed aside. He's very nice.</code> | <code>He was a music star too. Where're you people born and brought up? We 're born and brought up here in Anambra State at Nkpor town, near Onitsha.</code> | <code>At the age of 87 he has now retired from his live shows and all the traveling involved. And although he still picks up his Martin Guitar and does a show now and then, his life is now devoted to writing his memoirs.</code> | <code>The owner of the mysterious voice behind all these videos is a man who's seen a lot, visiting a total of 56 intimate celebrity spaces over the course of five years. His name is Joe Sabia — that's him in the photo — and he's currently the VP of creative development at Condé Nast Entertainment.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> | <code>A quick scan of the auction and buy-it-now listings on eBay finds porcelain doll values ranging from around $5 and $10 to several thousand dollars or more but no dolls listed above $10,000.</code> | <code>Japanese dolls are called as ningyō in Japanese and literally translates to 'human form'.</code> | <code>Matyoo: All Fresno Girl dolls come just as real children are born.</code> | <code>As of September 2016, there are over 100 characters. The main toy line includes 13-inch Dolls, the mini-series, and a variety of mini play-sets and plush dolls as well as Lalaloopsy Littles, smaller siblings of the 13-inch dolls. A spin-off known as "Lala-Oopsies" came out in late 2012.</code> | <code>LOL dolls are little baby dolls that come wrapped inside a surprise toy ball. Each ball has layers that contain stickers, secret messages, mix and match accessories–and finally–a doll. ... The doll on the ball is almost never the doll inside. Dolls are released in series, so not every doll is available all the time.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### bclavie/msmarco-500k-triplets
* Dataset: [bclavie/msmarco-500k-triplets](https://huggingface.co/datasets/bclavie/msmarco-500k-triplets) at [cb1a85c](https://huggingface.co/datasets/bclavie/msmarco-500k-triplets/tree/cb1a85c1261fa7c65f4ea43f94e50f8b467c372f)
* Size: 500,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 82.19 tokens</li><li>max: 216 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 78.99 tokens</li><li>max: 209 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>the most important factor that influences k+ secretion is __________.</code> | <code>The regulation of K+ distribution between the intracellular and extracellular space is referred to as internal K+ balance. The most important factors regulating this movement under normal conditions are insulin and catecholamines (1).</code> | <code>They are both also important for secretion and flow of bile: 1 Cholecystokinin: The name of this hormone describes its effect on the biliary system-cholecysto = gallbladder and kinin = movement. 2 Secretin: This hormone is secreted in response to acid in the duodenum.</code> |
| <code>how much did the mackinac bridge cost to build</code> | <code>The cost to design the project was $3,500,000 (Steinman Company). The cost to construct the bridge was $70, 268,500. Two primary contractors were hired to build the bridge: American Bridge for superstructure - $44,532,900; and Merritt-Chapman and Scott of New York for the foundations - $25,735,600.</code> | <code>When your child needs a dental tooth bridge, you need to know the average cost so you can factor the price into your budget. Several factors affect the price of a bridge, which can run between $700 to $1,500 per tooth. If you have insurance or your child is covered by Medicaid, part of the cost may be covered.</code> |
| <code>when do concussion symptoms appear</code> | <code>Then you can get advice on what to do next. For milder symptoms, the doctor may recommend rest and ask you to watch your child closely for changes, such as a headache that gets worse. Symptoms of a concussion don't always show up right away, and can develop within 24 to 72 hours after an injury.</code> | <code>Concussion: A traumatic injury to soft tissue, usually the brain, as a result of a violent blow, shaking, or spinning. A brain concussion can cause immediate but temporary impairment of brain functions, such as thinking, vision, equilibrium, and consciousness. After a person has had a concussion, he or she is at increased risk for recurrence. Moreover, after a person has several concussions, less of a blow can cause injury, and the person can require more time to recover.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: [sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 500,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 9.87 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 44 tokens</li><li>mean: 85.25 tokens</li><li>max: 211 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.18 tokens</li><li>max: 227 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:----------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Rather than preparing students for a specific career, liberal arts programs focus on cultural literacy and hone communication and analytical skills. They often cover various disciplines, ranging from the humanities to social sciences. 1 Program Levels in Liberal Arts: Associate degree, Bachelor's degree, Master's degree.</code> |
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Artes Liberales: The historical basis for the modern liberal arts, consisting of the trivium (grammar, logic, and rhetoric) and the quadrivium (arithmetic, geometry, astronomy, and music). General Education: That part of a liberal education curriculum that is shared by all students.</code> |
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> | <code>Liberal Arts. Upon completion of the Liberal Arts degree, students will be able to express ideas in coherent, creative, and appropriate forms, orally and in writing. Students will be able to apply their reading abilities in order to interconnect an understanding of resources to academic, professional, and personal interests.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### sentence-transformers/gooaq
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 500,000 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: 8 tokens</li><li>mean: 12.19 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 58.34 tokens</li><li>max: 124 tokens</li></ul> |
* Samples:
| question | answer |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### sentence-transformers/natural-questions
* Dataset: [sentence-transformers/natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 12.47 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 138.32 tokens</li><li>max: 556 tokens</li></ul> |
* Samples:
| query | answer |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> |
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> |
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### merged-2l-nli
* Dataset: [merged-2l-nli](https://huggingface.co/datasets/tasksource/merged-2l-nli) at [af845c6](https://huggingface.co/datasets/tasksource/merged-2l-nli/tree/af845c6b78a8ac3ea294666c2e5132cf6d5f4af0)
* Size: 425,243 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 72.83 tokens</li><li>max: 1219 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.78 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>0: ~55.50%</li><li>1: ~44.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>What type of food was cheese considered to be in Rome?</code> | <code>The staple foods were generally consumed around 11 o'clock, and consisted of bread, lettuce, cheese, fruits, nuts, and cold meat left over from the dinner the night before.[citation needed]</code> | <code>1</code> |
| <code>No Weapons of Mass Destruction Found in Iraq Yet.</code> | <code>Weapons of Mass Destruction Found in Iraq.</code> | <code>0</code> |
| <code>I stuck a pin through a carrot. When I pulled the pin out, it had a hole.</code> | <code>The carrot had a hole.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### merged-3l-nli
* Dataset: [merged-3l-nli](https://huggingface.co/datasets/tasksource/merged-3l-nli) at [e311b1f](https://huggingface.co/datasets/tasksource/merged-3l-nli/tree/e311b1f45a8f8cc8d4b2c5b92dbc797a05bc069d)
* Size: 564,204 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 114.98 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 28.37 tokens</li><li>max: 570 tokens</li></ul> | <ul><li>0: ~36.00%</li><li>1: ~31.50%</li><li>2: ~32.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:---------------|
| <code>Over the nave, the two hollow pyramids appear to be designed in the style of chimneys for a castle kitchen.</code> | <code>There are seven pyramids there.</code> | <code>2</code> |
| <code>The Catch of the Season is an Edwardian musical comedy by Seymour Hicks and Cosmo Hamilton, with music by Herbert Haines and Evelyn Baker and lyrics by Charles H. Taylor, based on the fairy tale Cinderella. A debutante is engaged to a young aristocrat but loves a page.</code> | <code>Seymour Hicks was alive in 1975.</code> | <code>1</code> |
| <code>A 3600 g infant is heavy. A 2400 g infant is light.</code> | <code>A 2220 g bicycle is light.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### zero-shot-label-nli
* Dataset: [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli) at [b363c89](https://huggingface.co/datasets/tasksource/zero-shot-label-nli/tree/b363c895cd4b15b814b9dbd7e4466cd301c96b2a)
* Size: 1,090,333 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~49.30%</li><li>2: ~50.70%</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 77.36 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 8.08 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|
| <code>2</code> | <code>okay</code> | <code>This example is reply_y.</code> |
| <code>2</code> | <code>We retrospectively compared 2 methods that have been proposed to screen for IA [1, 2].</code> | <code>This example is background.</code> |
| <code>2</code> | <code>PersonX puts it under PersonX's pillow PersonX then checks it again<br>Person X suffers from obsessive compulsive disorder.</code> | <code>This example is weakener.</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### dataset_train_nli
* Dataset: [dataset_train_nli](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli) at [1e00964](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli/tree/1e009645b2943106614107b06107b1ee85ac1161)
* Size: 1,018,733 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 95.56 tokens</li><li>max: 1152 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 14.05 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:---------------|
| <code>where is nayagara falls located</code> | <code>The example utterance is a query about music.</code> | <code>1</code> |
| <code>Druyun gets nine-month prison sentence A former top Air Force acquisition executive today was sentenced to nine months in prison for conspiring to help Boeing Co. win a multibillion-dollar Pentagon contract.</code> | <code>This example news text is about world news</code> | <code>1</code> |
| <code>Writing on the #39;wall #39; n Last edition of the Far Eastern Economic Review is shown on the streets of Hong Kong. The weekly news magazine is to fold in its current form with the loss of 80 jobs, the magazine #39;s publisher Dow Jones said yesterday.</code> | <code>This example news text is about science and technology</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### paws/labeled_final
* Dataset: [paws/labeled_final](https://huggingface.co/datasets/paws) at [161ece9](https://huggingface.co/datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 49,401 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 27.44 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.44 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>0: ~55.60%</li><li>1: ~44.40%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .</code> | <code>In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .</code> | <code>0</code> |
| <code>The NBA season of 1975 -- 76 was the 30th season of the National Basketball Association .</code> | <code>The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .</code> | <code>1</code> |
| <code>There are also specific discussions , public profile debates and project discussions .</code> | <code>There are also public discussions , profile specific discussions , and project discussions .</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/mrpc
* Dataset: [glue/mrpc](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 3,668 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 27.55 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 27.25 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~33.70%</li><li>1: ~66.30%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .</code> | <code>Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .</code> | <code>1</code> |
| <code>Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .</code> | <code>Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .</code> | <code>0</code> |
| <code>They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .</code> | <code>On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/qqp
* Dataset: [glue/qqp](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 363,846 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.9 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.73 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~61.90%</li><li>1: ~38.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------|:---------------------------------------------------------|:---------------|
| <code>What are reviews of Big Data University?</code> | <code>What is your review of Big Data University?</code> | <code>1</code> |
| <code>What are glass bottles made of?</code> | <code>How is a glass bottle made?</code> | <code>0</code> |
| <code>What do you really know about Algeria?</code> | <code>What do you know about Algeria?</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### fever-evidence-related
* Dataset: [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related) at [14aba00](https://huggingface.co/datasets/mwong/fever-evidence-related/tree/14aba009b5fcd97b1a9ee6f3e3b0da0e308cf7cb)
* Size: 403,218 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.63 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 350.02 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>0: ~31.80%</li><li>1: ~68.20%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>The Bridges of Madison County is a TV series.</code> | <code>Saulsbury is a town in Hardeman County , Tennessee .. Hardeman County. Hardeman County, Tennessee. Tennessee. Tennessee. County. List of counties in Tennessee. Hardeman. Hardeman County, Tennessee. The population was 99 at the 2000 census and 81 at the 2010 census showing a decrease of 18 .. United States Census, 2010. It is located along State Highway 57 in southwest Hardeman County .. Hardeman County. Hardeman County, Tennessee. State. Political divisions of the United States. County. List of counties in Tennessee. Hardeman. Hardeman County, Tennessee. State Highway 57. State Highway 57</code> | <code>1</code> |
| <code>Jessica Lange's first film role was in Godzilla.</code> | <code>Haji Ahmadov -LRB- Hacı Əhmədov , born on 23 November 1993 in Baku , Soviet Union -RRB- is an Azerbaijani football defender who plays for AZAL .. Baku. Baku. AZAL. AZAL PFK. Soviet Union. Soviet Union. Azerbaijani. Azerbaijani people. football. football ( soccer ). defender. Defender ( football )</code> | <code>1</code> |
| <code>Brad Pitt directed 12 Years a Slave.</code> | <code>The Bronze Bauhinia Star -LRB- , BBS -RRB- is the lowest rank in Order of the Bauhinia Star in Hong Kong , created in 1997 to replace the British honours system of the Order of the British Empire after the transfer of sovereignty to People 's Republic of China and the establishment of the Hong Kong Special Administrative Region -LRB- HKSAR -RRB- .. Order of the Bauhinia Star. Order of the Bauhinia Star. British honours system. British honours system. Order of the British Empire. Order of the British Empire. Special Administrative Region. Special Administrative Region of the People's Republic of China. It is awarded to persons who have given outstanding service over a long period of time , but in a more limited field or way than that required for the Silver Bauhinia Star .. Silver Bauhinia Star. Silver Bauhinia Star</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/stsb
* Dataset: [glue/stsb](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.22 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.04 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.74</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>Snowden Hits Hurdles in Search for Asylum</code> | <code>Snowden's hits hurdles in search for asylum</code> | <code>5.0</code> |
| <code>Ukrainian protesters back in streets for anti-government rally</code> | <code>Ukraine protesters topple Lenin statue in Kiev</code> | <code>2.5999999046325684</code> |
| <code>"Biotech products, if anything, may be safer than conventional products because of all the testing," Fraley said, adding that 18 countries have adopted biotechnology.</code> | <code>"Biotech products, if anything, may be safer than conventional products because of all the testing," said Robert Fraley, Monsanto's executive vice president.</code> | <code>3.200000047683716</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
#### sick/relatedness
* Dataset: sick/relatedness
* Size: 4,439 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.17 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.06 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 3.53</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------------|
| <code>The dark skinned male is standing on one hand in front of a yellow building</code> | <code>The dark skinned male is not standing on one hand in front of a yellow building</code> | <code>4.0</code> |
| <code>A man is singing and playing a guitar</code> | <code>A boy is skillfully playing a piano</code> | <code>2.299999952316284</code> |
| <code>A picture is being drawn by a man</code> | <code>The person is drawing</code> | <code>4.099999904632568</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
#### sts-companion
* Dataset: [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion) at [fd8beff](https://huggingface.co/datasets/tasksource/sts-companion/tree/fd8beffb788df5f6673bc688e6dcbe3690a3acc6)
* Size: 5,289 training samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | float | string | string |
| details | <ul><li>min: 0.0</li><li>mean: 3.15</li><li>max: 5.0</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.78 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.68 tokens</li><li>max: 71 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>4.6</code> | <code>As a matter of urgency, therefore, the staff complement of the Interdepartmental Group attached to the Commission Secretariat should be strengthened at the earliest possible opportunity in order to ensure that all proposals for acts which are general in scope are accompanied, when considered by the College of Commissioners and on the basis of Article 299(2), by a simplified sheet outlining their potential impact.</code> | <code>Thus, it is urgent that the inter-service group staff should be strengthened very quickly at the heart of the General Secretariat of the Commission, so that all proposals to act of general scope can be accompanied, during their examination by the college on the basis of Article 299(2), a detailed impact statement.</code> |
| <code>4.0</code> | <code>Reiterating the calls made by the European Parliament in its resolution of 16 March 2000, what initiatives does the Presidency of the European Council propose to take with a view to playing a more active role so as to guarantee the full and complete application of the UN peace plan?</code> | <code>As requested by the European Parliament in its resolution of 16 March 2000, that these initiatives the presidency of the European Council is going to take to play a more active role in order to ensure the full implementation of the UN peace plan?</code> |
| <code>3.2</code> | <code>Let us, as a Europe of 15 Member States, organise ourselves in order to be able to welcome those countries who are knocking at the door into the fold under respectable conditions.</code> | <code>Let us organise itself to 15 in order to be able to welcome the right conditions for countries which are knocking on our door.</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Datasets
#### merged-2l-nli
* Dataset: [merged-2l-nli](https://huggingface.co/datasets/tasksource/merged-2l-nli) at [af845c6](https://huggingface.co/datasets/tasksource/merged-2l-nli/tree/af845c6b78a8ac3ea294666c2e5132cf6d5f4af0)
* Size: 4,053 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 75.82 tokens</li><li>max: 1219 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.5 tokens</li><li>max: 158 tokens</li></ul> | <ul><li>0: ~51.00%</li><li>1: ~49.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>What happens to the norm when a number is multiplied by p?</code> | <code>While completing Q (roughly, filling the gaps) with respect to the absolute value yields the field of real numbers, completing with respect to the p-adic norm |−|p yields the field of p-adic numbers.</code> | <code>0</code> |
| <code>The abode of the Greek gods was on the summit of Mount Olympus, in Thessaly.</code> | <code>Mount Olympus is in Thessaly.</code> | <code>1</code> |
| <code>The drain is clogged with hair. It has to be cleaned.</code> | <code>The hair has to be cleaned.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### merged-3l-nli
* Dataset: [merged-3l-nli](https://huggingface.co/datasets/tasksource/merged-3l-nli) at [e311b1f](https://huggingface.co/datasets/tasksource/merged-3l-nli/tree/e311b1f45a8f8cc8d4b2c5b92dbc797a05bc069d)
* Size: 2,872 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 167.67 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 23.6 tokens</li><li>max: 430 tokens</li></ul> | <ul><li>0: ~38.20%</li><li>1: ~31.30%</li><li>2: ~30.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------|
| <code>But if Congress opts for debt over taxation, you can count on thoughtless commentators to denounce the interest payments on that debt as a second, and separate, outrage.</code> | <code>Everybody considers the interest on the national debt an outrage.</code> | <code>1</code> |
| <code>The 1997 KNVB Cup Final was a football match between Roda JC and Heerenveen on 8 May 1997 at De Kuip, Rotterdam. It was the final match of the 1996–97 KNVB Cup competition and the 79th KNVB Cup final. Roda won 4–2 after goals from Gerald Sibon, Ger Senden, Eric van der Luer and Maarten Schops. It was the side's first KNVB Cup trophy.</code> | <code>Roda JC kept the Cup trophy at their headquarters.</code> | <code>1</code> |
| <code>Discover Financial Services, Inc. is an American financial services company, which issues the Discover Card and operates the Discover and Pulse networks, and owns Diners Club International. Discover Card is the third largest credit card brand in the United States, when measured by cards in force, with nearly 50 million cardholders.</code> | <code>Discover Card is a way to build credit for less than 50 million cardholders</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### zero-shot-label-nli
* Dataset: [zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli) at [b363c89](https://huggingface.co/datasets/tasksource/zero-shot-label-nli/tree/b363c895cd4b15b814b9dbd7e4466cd301c96b2a)
* Size: 14,419 evaluation samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~51.40%</li><li>2: ~48.60%</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 72.92 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 8.01 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------|
| <code>2</code> | <code>Police suspected that Shaichat , 20 , had been abducted either by Palestinians or by Israeli Arabs .<br>Nobody claimed responsibility for Schaichat 's death , but police suspect that the 20-year-old soldier was abducted either by Palestinians or Israeli Arabs .</code> | <code>This example is equivalent.</code> |
| <code>2</code> | <code>Can immorality be achieved by blocking death genes?<br>Can immortality be achieved by blocking death genes?</code> | <code>This example is not_duplicate.</code> |
| <code>2</code> | <code>can a minor sit at a bar in nj</code> | <code>This example is False.</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### dataset_train_nli
* Dataset: [dataset_train_nli](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli) at [1e00964](https://huggingface.co/datasets/MoritzLaurer/dataset_train_nli/tree/1e009645b2943106614107b06107b1ee85ac1161)
* Size: 1,018,733 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 96.94 tokens</li><li>max: 1020 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.86 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~52.50%</li><li>1: ~47.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------|
| <code>Ecoplug MAX®<br>ECOPLUG MAX® is an efficient method to prevent regroth from leaf trees.<br>- Provides 100 percent effective on all brushwood<br>- Can be used all year round<br>- Kills all unwanted leaf tree<br>- Minimizes chemical diffusion<br>- Kills the entire root system of the treated tree/stump<br>- Fully selective method<br>reduce chemical use up to 90% compared to previously used methods.<br>- Can be used all year around.<br>- Will exterminate: Alder, elm, aspen, birch, beech, lime, maple, mountain ash,sallow, poplar, ash, cherry, bird cherry, oak and more broad leafed trees<br>- Minimize the use of chemicals during treatment of trees and stumps.<br>- The product will kill off the entire root system, but only the root system. Neither people, animals or the enviromnent will be exposed to our product..</code> | <code>This text is about: root extermination</code> | <code>0</code> |
| <code>can you start f. m. eight hundred and ninety radio channel</code> | <code>The intent of this example utterance is a datetime query.</code> | <code>1</code> |
| <code>never again swings between false sentiment and unfunny madcap comedy and , along the way , expects the audience to invest in the central relationship as some kind of marriage of true minds .</code> | <code>The sentiment in this example rotten tomatoes movie review is negative</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### paws/labeled_final
* Dataset: [paws/labeled_final](https://huggingface.co/datasets/paws) at [161ece9](https://huggingface.co/datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09)
* Size: 8,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 9 tokens</li><li>mean: 27.86 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.83 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~54.90%</li><li>1: ~45.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Bradd Crellin represented BARLA Cumbria on a tour of Australia with 6 other players representing Britain , also on a tour of Australia .</code> | <code>Bradd Crellin also represented BARLA Great Britain on a tour through Australia on a tour through Australia with 6 other players representing Cumbria .</code> | <code>0</code> |
| <code>They were there to enjoy us and they were there to pray for us .</code> | <code>They were there for us to enjoy and they were there for us to pray .</code> | <code>1</code> |
| <code>After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month .</code> | <code>In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/mrpc
* Dataset: [glue/mrpc](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 408 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 408 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 14 tokens</li><li>mean: 27.92 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.24 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~31.62%</li><li>1: ~68.38%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> |
| <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> |
| <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/qqp
* Dataset: [glue/qqp](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 40,430 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 15.77 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.05 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>What happens to a question on Quora if it is marked as needing further improvement?</code> | <code>If Quora doesn't understand my question and marks it as needing improvement, can others still see it?</code> | <code>1</code> |
| <code>What does the open blue circle in Facebook Messenger mean?</code> | <code>"what does ""delivered"" mean on Facebook messenger?"</code> | <code>0</code> |
| <code>How do I cool my mind?</code> | <code>What is the best way to be cool?</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### fever-evidence-related
* Dataset: [fever-evidence-related](https://huggingface.co/datasets/mwong/fever-evidence-related) at [14aba00](https://huggingface.co/datasets/mwong/fever-evidence-related/tree/14aba009b5fcd97b1a9ee6f3e3b0da0e308cf7cb)
* Size: 54,578 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 7 tokens</li><li>mean: 13.66 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 282.39 tokens</li><li>max: 1713 tokens</li></ul> | <ul><li>0: ~28.10%</li><li>1: ~71.90%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League.</code> | <code>RapidAdvance is a technology-powered financial services company that provides working capital to small and mid-sized businesses in the United States .. United States. United States. financial services. financial services. working capital. working capital. small and mid-sized businesses. Small and medium-sized enterprises. It offers small business loan programs for business owners in a variety of industries , including traditional retail establishments , brand name chain restaurants , automotive repair , manufacturing , trucking , and professional service providers .. Founded in 2005 and headquartered in Bethesda , Maryland , the company was acquired by Dan Gilbert 's Rockbridge Growth Equity , LLC in 2013 .. It is part of Rock Ventures `` family '' of companies that include the Cleveland Cavaliers , Fathead , Quicken Loans and Genius .. Rock Ventures. Rock Ventures. Cleveland Cavaliers. Cleveland Cavaliers. Fathead. Fathead ( brand ). Quicken Loans. Quicken Loans. Genius. Genius</code> | <code>1</code> |
| <code>Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League.</code> | <code>Arthur Herbert Copeland -LRB- June 22 , 1898 Rochester , New York -- July 6 , 1970 -RRB- was an American mathematician .. American. United States. He graduated from Harvard University in 1926 and taught at Rice University and the University of Michigan .. Rice University. Rice University. University of Michigan. University of Michigan. Harvard University. Harvard University. His main interest was in the foundations of probability .. probability. probability theory. He worked with Paul Erdos on the Copeland-Erdos constant .. Copeland-Erdos constant. Copeland-Erdos constant. Paul Erdos. Paul Erdos. His son , Arthur Herbert Copeland , Jr. , is also a mathematician .</code> | <code>1</code> |
| <code>Tilda Swinton is a vegan.</code> | <code>Michael Ronald Taylor -LRB- 1 June 1938 , Ealing , West London - 19 January 1969 -RRB- was a British jazz composer , pianist and co-songwriter for the band Cream .. Ealing. Ealing. London. London. British. United Kingdom. Cream. Cream ( band ). Mike Taylor was brought up by his grandparents in London and Kent , and joined the RAF for his national service .. London. London. Having rehearsed and written extensively throughout the early 1960s , he recorded two albums for the Lansdowne series produced by Denis Preston : Pendulum -LRB- 1966 -RRB- with drummer Jon Hiseman , bassist Tony Reeves and saxophonist Dave Tomlin -RRB- and Trio -LRB- 1967 -RRB- with Hiseman and bassists Jack Bruce and Ron Rubin .. Denis Preston. Denis Preston. Jon Hiseman. Jon Hiseman. Dave Tomlin. Dave Tomlin ( musician ). Jack Bruce. Jack Bruce. They were issued on UK Columbia .. Columbia. Columbia Graphophone Company. During his brief recording career , several of Taylor 's pieces were played and recorded by his ...</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### glue/stsb
* Dataset: [glue/stsb](https://huggingface.co/datasets/glue) at [bcdcba7](https://huggingface.co/datasets/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.46 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.47 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.35</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>The room used for defecation is almost always referred to by euphemism.</code> | <code>I'm English, and would probably use 'toilet' most of the time, and always in the context of a private home.</code> | <code>1.600000023841858</code> |
| <code>The two-year note US2YT=RR fell 5/32 in price, taking its yield to 1.23 percent from 1.16 percent late on Monday.</code> | <code>The benchmark 10-year note US10YT=RR lost 11/32 in price, taking its yield to 3.21 percent from 3.17 percent late on Monday.</code> | <code>2.0</code> |
| <code>I use Elinchrom Skyports, but if money is not an issue then go for PocketWizards.</code> | <code>Or just go with the ultra-cheap YongNuo RF-602, which give you a lot of bang for the buck.</code> | <code>1.2000000476837158</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
#### sick/relatedness
* Dataset: sick/relatedness
* Size: 495 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 495 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.69 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.15 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 3.59</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------|
| <code>The young boys are playing outdoors and the man is smiling nearby</code> | <code>There is no boy playing outdoors and there is no man smiling</code> | <code>3.5999999046325684</code> |
| <code>A person in a black jacket is doing tricks on a motorbike</code> | <code>A skilled person is riding a bicycle on one wheel</code> | <code>3.4000000953674316</code> |
| <code>Four children are doing backbends in the gym</code> | <code>Four girls are doing backbends and playing outdoors</code> | <code>3.799999952316284</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
#### sts-companion
* Dataset: [sts-companion](https://huggingface.co/datasets/tasksource/sts-companion) at [fd8beff](https://huggingface.co/datasets/tasksource/sts-companion/tree/fd8beffb788df5f6673bc688e6dcbe3690a3acc6)
* Size: 5,289 evaluation samples
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | float | string | string |
| details | <ul><li>min: 0.0</li><li>mean: 3.24</li><li>max: 5.0</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.56 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.21 tokens</li><li>max: 72 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:-----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>3.8</code> | <code>After all, it is by no means certain that the proposed definition of equitable price is better than any other, because the various definitions that are currently in use in the Member States are all perfectly satisfactory.</code> | <code>In fact, it is not absolutely certain that the definition of price that is proposed is better than another, because the different currently in the Member States all fully. </code> |
| <code>2.0</code> | <code>rslw: no, why would i hate them?</code> | <code>why do you hate america so much?</code> |
| <code>3.0</code> | <code>Families of #Newtown Victims Look for Answers on #Gun Violence #NRA</code> | <code>Families of Newtown School Massacre Victims Organize Against Gun Violence</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 24
- `learning_rate`: 3.5e-05
- `weight_decay`: 1e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3.5e-05
- `weight_decay`: 1e-06
- `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
- `restore_callback_states_from_checkpoint`: 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`: False
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0028 | 500 | 5.7412 |
| 0.0055 | 1000 | 2.2293 |
| 0.0083 | 1500 | 1.1572 |
| 0.0111 | 2000 | 0.9386 |
| 0.0138 | 2500 | 0.8352 |
| 0.0166 | 3000 | 0.7291 |
| 0.0194 | 3500 | 0.6555 |
| 0.0222 | 4000 | 0.6488 |
| 0.0249 | 4500 | 0.6267 |
| 0.0277 | 5000 | 0.5527 |
| 0.0305 | 5500 | 0.5985 |
| 0.0332 | 6000 | 0.5574 |
| 0.0360 | 6500 | 0.5642 |
| 0.0388 | 7000 | 0.5821 |
| 0.0415 | 7500 | 0.5289 |
| 0.0443 | 8000 | 0.5374 |
| 0.0471 | 8500 | 0.5187 |
| 0.0499 | 9000 | 0.5278 |
| 0.0526 | 9500 | 0.4983 |
| 0.0554 | 10000 | 0.4758 |
| 0.0582 | 10500 | 0.4939 |
| 0.0609 | 11000 | 0.4944 |
| 0.0637 | 11500 | 0.4967 |
| 0.0665 | 12000 | 0.4543 |
| 0.0692 | 12500 | 0.4649 |
| 0.0720 | 13000 | 0.4612 |
| 0.0748 | 13500 | 0.4612 |
| 0.0776 | 14000 | 0.4684 |
| 0.0803 | 14500 | 0.4904 |
| 0.0831 | 15000 | 0.4538 |
| 0.0859 | 15500 | 0.4388 |
| 0.0886 | 16000 | 0.4584 |
| 0.0914 | 16500 | 0.4728 |
| 0.0942 | 17000 | 0.4236 |
| 0.0969 | 17500 | 0.4328 |
| 0.0997 | 18000 | 0.4624 |
| 0.1025 | 18500 | 0.4732 |
| 0.1053 | 19000 | 0.4375 |
| 0.1080 | 19500 | 0.4495 |
| 0.1108 | 20000 | 0.4296 |
| 0.1136 | 20500 | 0.4211 |
| 0.1163 | 21000 | 0.4399 |
| 0.1191 | 21500 | 0.4353 |
| 0.1219 | 22000 | 0.4407 |
| 0.1246 | 22500 | 0.3892 |
| 0.1274 | 23000 | 0.4121 |
| 0.1302 | 23500 | 0.4253 |
| 0.1330 | 24000 | 0.4066 |
| 0.1357 | 24500 | 0.4168 |
| 0.1385 | 25000 | 0.3921 |
| 0.1413 | 25500 | 0.4008 |
| 0.1440 | 26000 | 0.4164 |
| 0.1468 | 26500 | 0.4047 |
| 0.1496 | 27000 | 0.4031 |
| 0.1523 | 27500 | 0.3955 |
| 0.1551 | 28000 | 0.3809 |
| 0.1579 | 28500 | 0.3992 |
| 0.1606 | 29000 | 0.3686 |
| 0.1634 | 29500 | 0.3851 |
| 0.1662 | 30000 | 0.3776 |
| 0.1690 | 30500 | 0.3919 |
| 0.1717 | 31000 | 0.4026 |
| 0.1745 | 31500 | 0.38 |
| 0.1773 | 32000 | 0.41 |
| 0.1800 | 32500 | 0.3731 |
| 0.1828 | 33000 | 0.3831 |
| 0.1856 | 33500 | 0.3727 |
| 0.1883 | 34000 | 0.3664 |
| 0.1911 | 34500 | 0.3882 |
| 0.1939 | 35000 | 0.3873 |
| 0.1967 | 35500 | 0.3529 |
| 0.1994 | 36000 | 0.3923 |
| 0.2022 | 36500 | 0.4051 |
| 0.2050 | 37000 | 0.4134 |
| 0.2077 | 37500 | 0.3478 |
| 0.2105 | 38000 | 0.3602 |
| 0.2133 | 38500 | 0.3547 |
| 0.2160 | 39000 | 0.3748 |
| 0.2188 | 39500 | 0.3537 |
| 0.2216 | 40000 | 0.38 |
| 0.2244 | 40500 | 0.3731 |
| 0.2271 | 41000 | 0.3537 |
| 0.2299 | 41500 | 0.3576 |
| 0.2327 | 42000 | 0.3626 |
| 0.2354 | 42500 | 0.3587 |
| 0.2382 | 43000 | 0.3488 |
| 0.2410 | 43500 | 0.3694 |
| 0.2437 | 44000 | 0.3508 |
| 0.2465 | 44500 | 0.3634 |
| 0.2493 | 45000 | 0.3608 |
| 0.2521 | 45500 | 0.4007 |
| 0.2548 | 46000 | 0.3559 |
| 0.2576 | 46500 | 0.3317 |
| 0.2604 | 47000 | 0.3518 |
| 0.2631 | 47500 | 0.3578 |
| 0.2659 | 48000 | 0.3375 |
| 0.2687 | 48500 | 0.3229 |
| 0.2714 | 49000 | 0.3319 |
| 0.2742 | 49500 | 0.3656 |
| 0.2770 | 50000 | 0.3598 |
| 0.2798 | 50500 | 0.3705 |
| 0.2825 | 51000 | 0.3431 |
| 0.2853 | 51500 | 0.3587 |
| 0.2881 | 52000 | 0.3361 |
| 0.2908 | 52500 | 0.3734 |
| 0.2936 | 53000 | 0.3361 |
| 0.2964 | 53500 | 0.3322 |
| 0.2991 | 54000 | 0.347 |
| 0.3019 | 54500 | 0.3617 |
| 0.3047 | 55000 | 0.3318 |
| 0.3074 | 55500 | 0.3401 |
| 0.3102 | 56000 | 0.328 |
| 0.3130 | 56500 | 0.3553 |
| 0.3158 | 57000 | 0.3669 |
| 0.3185 | 57500 | 0.4088 |
| 0.3213 | 58000 | 0.3636 |
| 0.3241 | 58500 | 0.3372 |
| 0.3268 | 59000 | 0.3494 |
| 0.3296 | 59500 | 0.3504 |
| 0.3324 | 60000 | 0.3389 |
| 0.3351 | 60500 | 0.3219 |
| 0.3379 | 61000 | 0.3283 |
| 0.3407 | 61500 | 0.3202 |
| 0.3435 | 62000 | 0.3185 |
| 0.3462 | 62500 | 0.3449 |
| 0.3490 | 63000 | 0.3527 |
| 0.3518 | 63500 | 0.3349 |
| 0.3545 | 64000 | 0.3225 |
| 0.3573 | 64500 | 0.3269 |
| 0.3601 | 65000 | 0.3074 |
| 0.3628 | 65500 | 0.3513 |
| 0.3656 | 66000 | 0.3166 |
| 0.3684 | 66500 | 0.3472 |
| 0.3712 | 67000 | 0.3395 |
| 0.3739 | 67500 | 0.3437 |
| 0.3767 | 68000 | 0.3491 |
| 0.3795 | 68500 | 0.3181 |
| 0.3822 | 69000 | 0.3324 |
| 0.3850 | 69500 | 0.3335 |
| 0.3878 | 70000 | 0.3401 |
| 0.3905 | 70500 | 0.3433 |
| 0.3933 | 71000 | 0.3229 |
| 0.3961 | 71500 | 0.3264 |
| 0.3989 | 72000 | 0.3123 |
| 0.4016 | 72500 | 0.3207 |
| 0.4044 | 73000 | 0.3008 |
| 0.4072 | 73500 | 0.2998 |
| 0.4099 | 74000 | 0.2992 |
| 0.4127 | 74500 | 0.3134 |
| 0.4155 | 75000 | 0.3262 |
| 0.4182 | 75500 | 0.2988 |
| 0.4210 | 76000 | 0.2936 |
| 0.4238 | 76500 | 0.314 |
| 0.4266 | 77000 | 0.3083 |
| 0.4293 | 77500 | 0.3103 |
| 0.4321 | 78000 | 0.3303 |
| 0.4349 | 78500 | 0.3282 |
| 0.4376 | 79000 | 0.3415 |
| 0.4404 | 79500 | 0.3001 |
| 0.4432 | 80000 | 0.321 |
| 0.4459 | 80500 | 0.3219 |
| 0.4487 | 81000 | 0.3477 |
| 0.4515 | 81500 | 0.2871 |
| 0.4542 | 82000 | 0.2913 |
| 0.4570 | 82500 | 0.3121 |
| 0.4598 | 83000 | 0.3057 |
| 0.4626 | 83500 | 0.32 |
| 0.4653 | 84000 | 0.3086 |
| 0.4681 | 84500 | 0.3091 |
| 0.4709 | 85000 | 0.3243 |
| 0.4736 | 85500 | 0.3104 |
| 0.4764 | 86000 | 0.3124 |
| 0.4792 | 86500 | 0.3134 |
| 0.4819 | 87000 | 0.2967 |
| 0.4847 | 87500 | 0.3036 |
| 0.4875 | 88000 | 0.3079 |
| 0.4903 | 88500 | 0.2959 |
| 0.4930 | 89000 | 0.3332 |
| 0.4958 | 89500 | 0.3151 |
| 0.4986 | 90000 | 0.3233 |
| 0.5013 | 90500 | 0.3083 |
| 0.5041 | 91000 | 0.2913 |
| 0.5069 | 91500 | 0.31 |
| 0.5096 | 92000 | 0.2962 |
| 0.5124 | 92500 | 0.3254 |
| 0.5152 | 93000 | 0.312 |
| 0.5180 | 93500 | 0.3152 |
| 0.5207 | 94000 | 0.3208 |
| 0.5235 | 94500 | 0.3039 |
| 0.5263 | 95000 | 0.3187 |
| 0.5290 | 95500 | 0.3052 |
| 0.5318 | 96000 | 0.3114 |
| 0.5346 | 96500 | 0.315 |
| 0.5373 | 97000 | 0.2862 |
| 0.5401 | 97500 | 0.3104 |
| 0.5429 | 98000 | 0.3 |
| 0.5457 | 98500 | 0.3017 |
| 0.5484 | 99000 | 0.3189 |
| 0.5512 | 99500 | 0.2919 |
| 0.5540 | 100000 | 0.2913 |
| 0.5567 | 100500 | 0.2936 |
| 0.5595 | 101000 | 0.3044 |
| 0.5623 | 101500 | 0.3034 |
| 0.5650 | 102000 | 0.2999 |
| 0.5678 | 102500 | 0.2961 |
| 0.5706 | 103000 | 0.328 |
| 0.5734 | 103500 | 0.3061 |
| 0.5761 | 104000 | 0.295 |
| 0.5789 | 104500 | 0.2997 |
| 0.5817 | 105000 | 0.2981 |
| 0.5844 | 105500 | 0.2966 |
| 0.5872 | 106000 | 0.2798 |
| 0.5900 | 106500 | 0.3001 |
| 0.5927 | 107000 | 0.3018 |
| 0.5955 | 107500 | 0.3076 |
| 0.5983 | 108000 | 0.3093 |
| 0.6010 | 108500 | 0.3096 |
| 0.6038 | 109000 | 0.2914 |
| 0.6066 | 109500 | 0.2874 |
| 0.6094 | 110000 | 0.2777 |
| 0.6121 | 110500 | 0.2854 |
| 0.6149 | 111000 | 0.3279 |
| 0.6177 | 111500 | 0.2843 |
| 0.6204 | 112000 | 0.2956 |
| 0.6232 | 112500 | 0.3076 |
| 0.6260 | 113000 | 0.314 |
| 0.6287 | 113500 | 0.295 |
| 0.6315 | 114000 | 0.2914 |
| 0.6343 | 114500 | 0.3041 |
| 0.6371 | 115000 | 0.2871 |
| 0.6398 | 115500 | 0.3004 |
| 0.6426 | 116000 | 0.2954 |
| 0.6454 | 116500 | 0.2959 |
| 0.6481 | 117000 | 0.3214 |
| 0.6509 | 117500 | 0.2828 |
| 0.6537 | 118000 | 0.3005 |
| 0.6564 | 118500 | 0.2918 |
| 0.6592 | 119000 | 0.2988 |
| 0.6620 | 119500 | 0.2901 |
| 0.6648 | 120000 | 0.2796 |
| 0.6675 | 120500 | 0.2988 |
| 0.6703 | 121000 | 0.2969 |
| 0.6731 | 121500 | 0.2892 |
| 0.6758 | 122000 | 0.2812 |
| 0.6786 | 122500 | 0.2992 |
| 0.6814 | 123000 | 0.2691 |
| 0.6841 | 123500 | 0.2966 |
| 0.6869 | 124000 | 0.2906 |
| 0.6897 | 124500 | 0.2807 |
| 0.6925 | 125000 | 0.2684 |
| 0.6952 | 125500 | 0.2771 |
| 0.6980 | 126000 | 0.2992 |
| 0.7008 | 126500 | 0.274 |
| 0.7035 | 127000 | 0.2846 |
| 0.7063 | 127500 | 0.2898 |
| 0.7091 | 128000 | 0.2795 |
| 0.7118 | 128500 | 0.2758 |
| 0.7146 | 129000 | 0.2883 |
| 0.7174 | 129500 | 0.2968 |
| 0.7201 | 130000 | 0.2756 |
| 0.7229 | 130500 | 0.3116 |
| 0.7257 | 131000 | 0.2923 |
| 0.7285 | 131500 | 0.2758 |
| 0.7312 | 132000 | 0.262 |
| 0.7340 | 132500 | 0.283 |
| 0.7368 | 133000 | 0.2937 |
| 0.7395 | 133500 | 0.2891 |
| 0.7423 | 134000 | 0.2743 |
| 0.7451 | 134500 | 0.3087 |
| 0.7478 | 135000 | 0.2855 |
| 0.7506 | 135500 | 0.2902 |
| 0.7534 | 136000 | 0.278 |
| 0.7562 | 136500 | 0.2607 |
| 0.7589 | 137000 | 0.2634 |
| 0.7617 | 137500 | 0.2807 |
| 0.7645 | 138000 | 0.294 |
| 0.7672 | 138500 | 0.2837 |
| 0.7700 | 139000 | 0.2521 |
| 0.7728 | 139500 | 0.2751 |
| 0.7755 | 140000 | 0.3012 |
| 0.7783 | 140500 | 0.2816 |
| 0.7811 | 141000 | 0.2756 |
| 0.7839 | 141500 | 0.2661 |
| 0.7866 | 142000 | 0.2585 |
| 0.7894 | 142500 | 0.2718 |
| 0.7922 | 143000 | 0.2724 |
| 0.7949 | 143500 | 0.2804 |
| 0.7977 | 144000 | 0.2582 |
| 0.8005 | 144500 | 0.2636 |
| 0.8032 | 145000 | 0.2536 |
| 0.8060 | 145500 | 0.2862 |
| 0.8088 | 146000 | 0.2842 |
| 0.8116 | 146500 | 0.2702 |
| 0.8143 | 147000 | 0.2727 |
| 0.8171 | 147500 | 0.2591 |
| 0.8199 | 148000 | 0.2709 |
| 0.8226 | 148500 | 0.2879 |
| 0.8254 | 149000 | 0.2669 |
| 0.8282 | 149500 | 0.2748 |
| 0.8309 | 150000 | 0.2689 |
| 0.8337 | 150500 | 0.2414 |
| 0.8365 | 151000 | 0.261 |
| 0.8393 | 151500 | 0.2967 |
| 0.8420 | 152000 | 0.2757 |
| 0.8448 | 152500 | 0.2667 |
| 0.8476 | 153000 | 0.252 |
| 0.8503 | 153500 | 0.2659 |
| 0.8531 | 154000 | 0.2799 |
| 0.8559 | 154500 | 0.2653 |
| 0.8586 | 155000 | 0.275 |
| 0.8614 | 155500 | 0.3067 |
| 0.8642 | 156000 | 0.2742 |
| 0.8669 | 156500 | 0.2616 |
| 0.8697 | 157000 | 0.2793 |
| 0.8725 | 157500 | 0.2721 |
| 0.8753 | 158000 | 0.2623 |
| 0.8780 | 158500 | 0.2801 |
| 0.8808 | 159000 | 0.2499 |
| 0.8836 | 159500 | 0.283 |
| 0.8863 | 160000 | 0.2641 |
| 0.8891 | 160500 | 0.2642 |
| 0.8919 | 161000 | 0.271 |
| 0.8946 | 161500 | 0.2624 |
| 0.8974 | 162000 | 0.2721 |
| 0.9002 | 162500 | 0.2698 |
| 0.9030 | 163000 | 0.2519 |
| 0.9057 | 163500 | 0.2771 |
| 0.9085 | 164000 | 0.2719 |
| 0.9113 | 164500 | 0.2747 |
| 0.9140 | 165000 | 0.28 |
| 0.9168 | 165500 | 0.2618 |
| 0.9196 | 166000 | 0.2755 |
| 0.9223 | 166500 | 0.3104 |
| 0.9251 | 167000 | 0.2671 |
| 0.9279 | 167500 | 0.2491 |
| 0.9307 | 168000 | 0.262 |
| 0.9334 | 168500 | 0.2514 |
| 0.9362 | 169000 | 0.2632 |
| 0.9390 | 169500 | 0.2834 |
| 0.9417 | 170000 | 0.2573 |
| 0.9445 | 170500 | 0.2662 |
| 0.9473 | 171000 | 0.2631 |
| 0.9500 | 171500 | 0.2507 |
| 0.9528 | 172000 | 0.2739 |
| 0.9556 | 172500 | 0.2567 |
| 0.9584 | 173000 | 0.2489 |
| 0.9611 | 173500 | 0.2607 |
| 0.9639 | 174000 | 0.2627 |
| 0.9667 | 174500 | 0.2715 |
| 0.9694 | 175000 | 0.2603 |
| 0.9722 | 175500 | 0.2533 |
| 0.9750 | 176000 | 0.261 |
| 0.9777 | 176500 | 0.2485 |
| 0.9805 | 177000 | 0.2719 |
| 0.9833 | 177500 | 0.2693 |
| 0.9861 | 178000 | 0.2825 |
| 0.9888 | 178500 | 0.2697 |
| 0.9916 | 179000 | 0.2601 |
| 0.9944 | 179500 | 0.2459 |
| 0.9971 | 180000 | 0.2674 |
| 0.9999 | 180500 | 0.2725 |
</details>
### Framework Versions
- Python: 3.11.4
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.4.0+cu121
- Accelerate: 1.0.1
- Datasets: 2.20.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```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}
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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