diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1047 @@ +--- +language: +- en +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:4330781 +- loss:MultipleNegativesRankingLoss +- loss:SoftmaxLoss +- loss:CosineSimilarityLoss +base_model: tasksource/ModernBERT-base-nli +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/all-nli +- 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/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli), [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) +- **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/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) + - [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 + + +### 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-nli-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] +``` + + + + + + + + + + + +## 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: query, answer, negative_1, negative_2, negative_3, negative_4, and negative_5 +* 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 | | | | | | | | +* Samples: + | query | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | + |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | when did richmond last play in a preliminary final | 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... | 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... | 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. | 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. | 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... | 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... | + | who sang what in the world's come over you | 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. | 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] | 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. | 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. | 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. | 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. | + | who produces the most wool in the world | 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. | 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] | 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] | 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::: | 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: FL). | 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. | +* Loss: [MultipleNegativesRankingLoss](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: 100,000 training samples +* Columns: question, answer, negative_1, negative_2, negative_3, negative_4, and negative_5 +* 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 | | | | | | | | +* Samples: + | question | answer | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | + |:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | is toprol xl the same as metoprolol? | 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. | Secondly, metoprolol and metoprolol ER have different brand-name equivalents: Brand version of metoprolol: Lopressor. Brand version of metoprolol ER: Toprol XL. | Pill with imprint 1 is White, Round and has been identified as Metoprolol Tartrate 25 mg. | 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. | 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. | 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. | + | are you experienced cd steve hoffman? | 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. | 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. | (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. | 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. | 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. | 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. | + | how are babushka dolls made? | 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. | 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. | Japanese dolls are called as ningyō in Japanese and literally translates to 'human form'. | Matyoo: All Fresno Girl dolls come just as real children are born. | 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. | 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. | +* Loss: [MultipleNegativesRankingLoss](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: 100,000 training samples +* Columns: query, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | query | positive | negative | + |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | query | positive | negative | + |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | the most important factor that influences k+ secretion is __________. | 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). | 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. | + | how much did the mackinac bridge cost to build | 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. | 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. | + | when do concussion symptoms appear | 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. | 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. | +* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim" + } + ``` + +#### sentence-transformers/all-nli + +* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) +* Size: 100,000 training samples +* Columns: anchor, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | anchor | positive | negative | + |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| + | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | + | Children smiling and waving at camera | There are children present | The kids are frowning | + | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | +* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------|:---------------| + | Grayson heard that learn an instrument find some friends who will too write some songs and go for it | Grayson did not hear a pun | 1 | + | What is not explicitly stated as true is considered false.
Fiona is big. Fiona is furry. Fiona is green. Fiona is quiet. Fiona is rough. Fiona is smart. Fiona is young. If Fiona is furry and Fiona is not quiet then Fiona is rough. If someone is quiet and not young then they are big. If someone is not quiet and not smart then they are rough. If someone is smart and not young then they are not green. Furry people are big. Big, smart people are rough. | Fiona is quiet. | 1 | + | You may want to see if you can get in touch with their EPM group and get this guy in a study . | The getting did not happen | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | [M]It[/M] previously stood on Capitol Square in Rome, but [M]has now been replaced with a copy.[/M] | Marcus Aurelius Antoninus Augustus (in Latin: Marcus Aurelius Antoninus Augustus; in the epigraphs: IMP CAES M AVREL ANTONINVS AVG; Rome, April 26, 121 - Sirmium or Vindobona, March 17, 180), better known simply as Marcus Aurelius, was a Roman emperor, philosopher and writer.
On the recommendation of the emperor Hadrian, it was adopted in 138 by the future father-in-law and acquired uncle Antonino Pio who appointed him heir to the imperial throne.
Born as Marco Annio Catilius Severus (Marcus Annius Catilius Severus), he became Marco Annio Vero (Marcus Annius Verus), which was the name of his father, at the time of his marriage with his cousin Faustina, daughter of Antoninus, and therefore assumed the name of Marcus Aurelius Caesar, son of the Augustus (Marcus Aurelius Caesar Augusti filius) during the empire of Antoninus himself.
Marcus Aurelius was emperor from 161 until his death, which occurred due to illness in 180 in Sirmium according to the contemporary Tertullian or near Vindobo... | 1 | + | (This small difference would be further reduced if retail activities of rural carriers were not counted.) | The gap would become wider if we did not consider retail activities of rural carriers. | 2 | + | None of the toothbrushes are yellow in colour. | All of the toothbrushes are yellow in colour. | 2 | +* Loss: [SoftmaxLoss](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: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | int | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------| + | 2 | The crocodile is rough. The crocodile is tired. The crocodile is dull. The crocodile chases the mouse. The lion visits the cat. The lion is strong. The lion is big. The mouse is nice. The mouse is smart. The mouse is kind. The cat is cute. The cat is small. The cat is beautiful. Nice animals are cute. If something is tired then it sees the mouse. If something sees the mouse then it is lazy. If something is rough and tired then it is dull. If something is cute and small then it is furry. If something is strong and big then it is heavy. If something is dull then it is slow. All slow animals are sleepy. If something is cute then it is small. All small animals are beautiful. If something is heavy then it is obese. All obese animals are awful. If something is furry then it is adorable. All adorable animals are lovely. All lazy animals are fierce.
The crocodile is not sleepy. | This example is True. | + | 0 | Still , Preetam vows to marry Nandini if she meets him again . How long had they known each other? only a few centuries | This example is no. | + | 2 | i wouldnt blame you youre more likely to be killed by blacks the numbers dont lie blacks are dangerous but dont tell the libs that | This example is not gender-bias. | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|:---------------| + | 2 corrections, most recently by Hughesdarren - Show corrections
DERBY NOTES.
(FROM OUR OWN CORRESPONDENT).
The rains have come only just in time to save thousands of head of dying cattle. A few weeks longer delay and Kimberley would have lost stock to such a serious extent that years must have elapsed before its regaining a flourishing condition. As it is many thousands of cattle and sheep
have been lost. On all stations there has been loss. Some, however, have not suffered severely, whilst others have re- ceived a blow that will take a year or two to recover. Obogamma station is one of the favoured few. The losses there do not exceed 200 bead. The Yeeda station loss
is estimated at 500 to 600. The Gogo and Fossil Downs stations lost very heavily,
but no accurate figures are to hand. Kim- berley Downs station, or Balmaningarra as it was called until recently, had no loss over the average. The same can be said of the Barker River station. The losses
will be felt chiefly at shearing time... | This text is about: vegetation | 1 | + | What is Dreamwidth Studios?
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- Get help with using Dreamwidth. | This text is about: blogging platform | 1 | + | Annan #39;s remarks on Iraqi war draw different reactions among Iraqis UN Secretary-general Kofi Annan #39;s declaration saying the Iraq war was quot;illegal quot; sparked different reactions from the Iraqis. | This example news text is about world news | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland . | 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 . | 0 | + | The NBA season of 1975 -- 76 was the 30th season of the National Basketball Association . | The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA . | 1 | + | There are also specific discussions , public profile debates and project discussions . | There are also public discussions , profile specific discussions , and project discussions . | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-----------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence . | Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence . | 1 | + | Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion . | Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 . | 0 | + | They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added . | On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale . | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------| + | How can one learn to play barre chords on a guitar? | What is the easiest way to play barre chords on guitar? | 1 | + | We are inviting my parents in uk. Is there going to be a problem with their application because we live in 1 bedroom flat? | I have been living in a hostel for 2 weeks and I still miss my parents. I am unable to adjust. What should I do? | 0 | + | Can I post videos in Quora? | Can I post video on Quora? | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:---------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Minos fathered Deucalion. | -RSB-. A paten , or diskos , is a small plate , usually made of silver or gold , used to hold Eucharistic bread which is to be consecrated .. bread. Host ( Holy Communion ). consecrated. consecrated. It is generally used during the service itself , while the reserved sacrament are stored in the tabernacle in a ciborium .. reserved sacrament. reserved sacrament. tabernacle. Church tabernacle. ciborium. Ciborium ( container ) | 1 | + | Hawaii is the tenth-most densely populated state. | Central Frontenac is a township in eastern Ontario , Canada in the County of Frontenac .. Frontenac. Frontenac County. township. township ( Canada ). Ontario. Ontario. Canada. Canada. County of Frontenac. Frontenac County, Ontario. Central Frontenac was created in 1998 through an amalgamation of the Townships of Hinchinbrooke , Kennebec , Olden and Oso .. Frontenac. Frontenac County | 1 | + | Brunei is a former country. | John Harley -LRB- 29 September 1728 -- 7 January 1788 -RRB- was a British bishop .. Harley was the second son of Edward Harley , 3rd Earl of Oxford and Earl Mortimer .. Edward. Edward Harley, 4th Earl of Oxford and Earl Mortimer. He was Archdeacon of Shropshire from 1760 to 1769 and then Archdeacon of Hereford from 1869 to 1787 .. Archdeacon of Shropshire. Archdeacon of Ludlow. Archdeacon of Hereford. Archdeacon of Hereford. He was Dean of Windsor , Registrar of the Order of the Garter and briefly , at the end of his life , the Bishop of Hereford .. Dean of Windsor. Dean of Windsor. Bishop of Hereford. Bishop of Hereford. His son Edward -LRB- by his wife Roach Vaughan , daughter of Gwynne Vaughan of Trebarry , Radnorshire -RRB- succeeded Harley 's elder brother -LRB- Edward -RRB- as 5th Earl of Oxford .. Edward. Edward Harley, 4th Earl of Oxford and Earl Mortimer | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| + | type | string | string | float | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:---------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:-------------------------------| + | russia may pull out of the treaty if the us and other nato allies refuse to ratify the amended version. | warned that russia would withdraw from the treaty if western nations refuse to ratify its amended version. | 4.400000095367432 | + | Israeli forces detain 5 Palestinians in West Bank | Israeli Forces Arrest Five Palestinians across West Bank | 4.800000190734863 | + | Chinese premier meets Indian vice president | Hezbollah urges to elect new Lebanese president | 0.0 | +* Loss: [CosineSimilarityLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| + | type | string | string | float | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:---------------------------------------------------------------------|:-------------------------------------------------------------------------|:--------------------------------| + | A person is singing and playing a guitar | A boy is playing a piano | 3.0999999046325684 | + | A man and a woman are driving down the street in a jeep | A man and a woman are not driving down the street in a jeep | 4.400000095367432 | + | There is no man eating a bowl of cereal | A man is eating a bowl of cereal | 4.0 | +* Loss: [CosineSimilarityLoss](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: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | float | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:------------------|:-----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------| + | 0.8 | the act of combining, blending, integrating | the act of chipping something. | + | 2.75 | a device to control the rate of some activity, e.g., chemical or mechanical | any of various controls or devices for regulating or controlling fluid flow, pressure, temperature, etc.. | + | 2.5 | a physical entity | a separate and self-contained entity. | +* Loss: [CosineSimilarityLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | What happens to the norm when a number is multiplied by p? | 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. | 0 | + | The abode of the Greek gods was on the summit of Mount Olympus, in Thessaly. | Mount Olympus is in Thessaly. | 1 | + | The drain is clogged with hair. It has to be cleaned. | The hair has to be cleaned. | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------| + | 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. | Everybody considers the interest on the national debt an outrage. | 1 | + | 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. | Roda JC kept the Cup trophy at their headquarters. | 1 | + | 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. | Discover Card is a way to build credit for less than 50 million cardholders | 0 | +* Loss: [SoftmaxLoss](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: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | int | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------| + | 2 | Police suspected that Shaichat , 20 , had been abducted either by Palestinians or by Israeli Arabs .
Nobody claimed responsibility for Schaichat 's death , but police suspect that the 20-year-old soldier was abducted either by Palestinians or Israeli Arabs . | This example is equivalent. | + | 2 | Can immorality be achieved by blocking death genes?
Can immortality be achieved by blocking death genes? | This example is not_duplicate. | + | 2 | can a minor sit at a bar in nj | This example is False. | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------| + | Ecoplug MAX®
ECOPLUG MAX® is an efficient method to prevent regroth from leaf trees.
- Provides 100 percent effective on all brushwood
- Can be used all year round
- Kills all unwanted leaf tree
- Minimizes chemical diffusion
- Kills the entire root system of the treated tree/stump
- Fully selective method
reduce chemical use up to 90% compared to previously used methods.
- Can be used all year around.
- Will exterminate: Alder, elm, aspen, birch, beech, lime, maple, mountain ash,sallow, poplar, ash, cherry, bird cherry, oak and more broad leafed trees
- Minimize the use of chemicals during treatment of trees and stumps.
- 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.. | This text is about: root extermination | 0 | + | can you start f. m. eight hundred and ninety radio channel | The intent of this example utterance is a datetime query. | 1 | + | 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 . | The sentiment in this example rotten tomatoes movie review is negative | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Bradd Crellin represented BARLA Cumbria on a tour of Australia with 6 other players representing Britain , also on a tour of Australia . | Bradd Crellin also represented BARLA Great Britain on a tour through Australia on a tour through Australia with 6 other players representing Cumbria . | 0 | + | They were there to enjoy us and they were there to pray for us . | They were there for us to enjoy and they were there for us to pray . | 1 | + | After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month . | In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month . | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 408 samples: + | | sentence1 | sentence2 | label | + |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | He said the foodservice pie business doesn 't fit the company 's long-term growth strategy . | " The foodservice pie business does not fit our long-term growth strategy . | 1 | + | Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war . | His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war . | 0 | + | The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat . | 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 . | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------| + | What happens to a question on Quora if it is marked as needing further improvement? | If Quora doesn't understand my question and marks it as needing improvement, can others still see it? | 1 | + | What does the open blue circle in Facebook Messenger mean? | "what does ""delivered"" mean on Facebook messenger?" | 0 | + | How do I cool my mind? | What is the best way to be cool? | 0 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:--------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League. | 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 | 1 | + | Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League. | 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 . | 1 | + | Tilda Swinton is a vegan. | 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 ... | 1 | +* Loss: [SoftmaxLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| + | type | string | string | float | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| + | The room used for defecation is almost always referred to by euphemism. | I'm English, and would probably use 'toilet' most of the time, and always in the context of a private home. | 1.600000023841858 | + | 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. | 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. | 2.0 | + | I use Elinchrom Skyports, but if money is not an issue then go for PocketWizards. | Or just go with the ultra-cheap YongNuo RF-602, which give you a lot of bang for the buck. | 1.2000000476837158 | +* Loss: [CosineSimilarityLoss](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: sentence1, sentence2, and label +* Approximate statistics based on the first 495 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| + | type | string | string | float | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------| + | The young boys are playing outdoors and the man is smiling nearby | There is no boy playing outdoors and there is no man smiling | 3.5999999046325684 | + | A person in a black jacket is doing tricks on a motorbike | A skilled person is riding a bicycle on one wheel | 3.4000000953674316 | + | Four children are doing backbends in the gym | Four girls are doing backbends and playing outdoors | 3.799999952316284 | +* Loss: [CosineSimilarityLoss](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: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | float | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:-----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 3.8 | 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. | 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. | + | 2.0 | rslw: no, why would i hate them? | why do you hate america so much? | + | 3.0 | Families of #Newtown Victims Look for Answers on #Gun Violence #NRA | Families of Newtown School Massacre Victims Organize Against Gun Violence | +* Loss: [CosineSimilarityLoss](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`: 2e-05 +- `weight_decay`: 1e-06 +- `num_train_epochs`: 1 +- `warmup_ratio`: 0.1 +- `fp16`: True + +#### All Hyperparameters +
Click to expand + +- `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`: 2e-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 + +
+ +### Training Logs +| Epoch | Step | Training Loss | +|:------:|:-----:|:-------------:| +| 0.0125 | 500 | 3.9436 | +| 0.0250 | 1000 | 1.6589 | +| 0.0375 | 1500 | 1.1438 | +| 0.0500 | 2000 | 0.9633 | +| 0.0624 | 2500 | 0.8801 | +| 0.0749 | 3000 | 0.8087 | +| 0.0874 | 3500 | 0.7826 | +| 0.0999 | 4000 | 0.7566 | +| 0.1124 | 4500 | 0.7424 | +| 0.1249 | 5000 | 0.7154 | +| 0.1374 | 5500 | 0.6596 | +| 0.1499 | 6000 | 0.6539 | +| 0.1623 | 6500 | 0.6288 | +| 0.1748 | 7000 | 0.6337 | +| 0.1873 | 7500 | 0.6232 | +| 0.1998 | 8000 | 0.6218 | +| 0.2123 | 8500 | 0.5803 | +| 0.2248 | 9000 | 0.5778 | +| 0.2373 | 9500 | 0.5922 | +| 0.2498 | 10000 | 0.5726 | +| 0.2622 | 10500 | 0.5531 | +| 0.2747 | 11000 | 0.564 | +| 0.2872 | 11500 | 0.5693 | +| 0.2997 | 12000 | 0.5462 | +| 0.3122 | 12500 | 0.5577 | +| 0.3247 | 13000 | 0.526 | +| 0.3372 | 13500 | 0.5344 | +| 0.3497 | 14000 | 0.5366 | +| 0.3621 | 14500 | 0.5185 | +| 0.3746 | 15000 | 0.5243 | +| 0.3871 | 15500 | 0.5112 | +| 0.3996 | 16000 | 0.5124 | +| 0.4121 | 16500 | 0.4874 | +| 0.4246 | 17000 | 0.5399 | +| 0.4371 | 17500 | 0.515 | +| 0.4496 | 18000 | 0.5261 | +| 0.4620 | 18500 | 0.4917 | +| 0.4745 | 19000 | 0.4716 | +| 0.4870 | 19500 | 0.4887 | +| 0.4995 | 20000 | 0.4594 | +| 0.5120 | 20500 | 0.4687 | +| 0.5245 | 21000 | 0.4576 | +| 0.5370 | 21500 | 0.4735 | +| 0.5495 | 22000 | 0.464 | +| 0.5620 | 22500 | 0.4678 | +| 0.5744 | 23000 | 0.481 | +| 0.5869 | 23500 | 0.4918 | +| 0.5994 | 24000 | 0.4576 | +| 0.6119 | 24500 | 0.4467 | +| 0.6244 | 25000 | 0.4556 | +| 0.6369 | 25500 | 0.4489 | +| 0.6494 | 26000 | 0.4406 | +| 0.6619 | 26500 | 0.4587 | +| 0.6743 | 27000 | 0.4751 | +| 0.6868 | 27500 | 0.4446 | +| 0.6993 | 28000 | 0.433 | +| 0.7118 | 28500 | 0.4469 | +| 0.7243 | 29000 | 0.4479 | +| 0.7368 | 29500 | 0.4408 | +| 0.7493 | 30000 | 0.4259 | +| 0.7618 | 30500 | 0.464 | +| 0.7742 | 31000 | 0.4592 | +| 0.7867 | 31500 | 0.4442 | +| 0.7992 | 32000 | 0.4305 | +| 0.8117 | 32500 | 0.4439 | +| 0.8242 | 33000 | 0.4335 | +| 0.8367 | 33500 | 0.4255 | +| 0.8492 | 34000 | 0.4119 | +| 0.8617 | 34500 | 0.4298 | +| 0.8741 | 35000 | 0.4443 | +| 0.8866 | 35500 | 0.4369 | +| 0.8991 | 36000 | 0.41 | +| 0.9116 | 36500 | 0.43 | +| 0.9241 | 37000 | 0.398 | +| 0.9366 | 37500 | 0.4471 | +| 0.9491 | 38000 | 0.4409 | +| 0.9616 | 38500 | 0.4392 | +| 0.9741 | 39000 | 0.4197 | +| 0.9865 | 39500 | 0.4154 | +| 0.9990 | 40000 | 0.419 | + + +### 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} +} +``` + + + + + + \ No newline at end of file