diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -56,94 +56,80 @@ tags: - dataset_size:32500 - loss:GISTEmbedLoss widget: -- source_sentence: Vermont South is bordered by Mitcham to the north , Nunawading - and Forest Hill to the west , Vermont to the south and Wantirna and Ringwood to - the east . +- source_sentence: What was the name of Jed's nephew in The Beverly Hillbillies? sentences: - - Vermont South is bordered to the north of Mitcham , to the west by Nunawading - and Forest Hill , to the south by Vermont and to the east by Wantirna and Ringwood - . - - Ocean waves are among the most impressive waves in the world. They clearly show - that waves transfer energy. In the case of ocean waves, energy is transferred - through matter. But some waves, called electromagnetic waves, can transfer energy - without traveling through matter. These waves can travel through space. You can - read more about electromagnetic waves in the chapter "Electromagnetic Radiation. - " Waves that transfer energy through matter are the focus of the present chapter. - These waves are called mechanical waves. - - Mosque–Cathedral of Córdoba The building is most notable for its arcaded hypostyle - hall, with 856 columns of jasper, onyx, marble, and granite. These were made from - pieces of the Roman temple which had occupied the site previously, as well as - other destroyed Roman buildings, such as the Mérida amphitheatre. The double arches - were a new introduction to architecture, permitting higher ceilings than would - otherwise be possible with relatively low columns. The double arches consist of - a lower horseshoe arch and an upper semi-circular arch. The famous alternating - red and white voussoirs of the arches were inspired by those in the Dome of the - Rock.[31] and also resemble those of the Aachen Cathedral, which were built almost - at the same time. Horseshoe arches were known in the Iberian Peninsula since late - Antiquity, as can be seen on the 3rd-century "Estela de los Flavios", now in the - arqueological museum of León. A centrally located honey-combed dome has blue tiles - decorated with stars. -- source_sentence: What are single-celled organisms that lack a nucleus? + - Jed Clampett - The Beverly Hillbillies Characters - ShareTV Buddy Ebsen began + his career as a dancer in the late 1920s in a Broadway chorus. He later formed + a vaudeville ... Character Bio Although he had received little formal education, + Jed Clampett had a good deal of common sense. A good-natured man, he is the apparent + head of the family. Jed's wife (Elly May's mother) died, but is referred to in + the episode "Duke Steals A Wife" as Rose Ellen. Jed was shown to be an expert + marksman and was extremely loyal to his family and kinfolk. The huge oil pool + in the swamp he owned was the beginning of his rags-to-riches journey to Beverly + Hills. Although he longed for the old ways back in the hills, he made the best + of being in Beverly Hills. Whenever he had anything on his mind, he would sit + on the curbstone of his mansion and whittle until he came up with the answer. + Jedediah, the version of Jed's name used in the 1993 Beverly Hillbillies theatrical + movie, was never mentioned in the original television series (though coincidentally, + on Ebsen's subsequent series, Barnaby Jones, Barnaby's nephew J.R. was also named + Jedediah). In one episode Jed and Granny reminisce about seeing Buddy Ebsen and + Vilma Ebsen—a joking reference to the Ebsens' song and dance act. Jed appears + in all 274 episodes. Episode Screenshots + - a stove generates heat for cooking usually + - Miss Marple series by Agatha Christie Miss Marple series 43 works, 13 primary + works Mystery series in order of publication. Miss Marple is introduced in The + Murder at the Vicarage but the books can be read in any order. Mixed short story + collections are included if some are Marple, often have horror, supernatural, + maybe detective Poirot, Pyne, or Quin. Note that "Nemesis" should be read AFTER + "A Caribbean Holiday" +- source_sentence: A recording of folk songs done for the Columbia society in 1942 + was largely arranged by Pjetër Dungu . sentences: - - a dog standing near a street sign on a dirt road - - "Prokaryotes are single-celled organisms that lack a nucleus.. Prokaryotes All\ - \ bacteria are prokaryotes. \n bacteria are single-celled organisms that lack\ - \ a nucleus." - - 1 cup rice uncooked = 7 oz / 200 g = 600 g ( 5 cups / 21 oz in weight) cooked - [2] (Will serve 5 people). 1 pound of rice = 2 1/4 to 2 1/2 cups uncooked = 11 - cups cooked (Will serve 11 people). -- source_sentence: In the UK television series ‘On The Buses’, what is the first name - of Inspector Blake? + - Someone cooking drugs in a spoon over a candle + - A recording of folk songs made for the Columbia society in 1942 was largely arranged + by Pjetër Dungu . + - A Murder of Crows, A Parliament of Owls What do You Call a Group of Birds? Do + you know what a group of Ravens is called? What about a group of peacocks, snipe + or hummingbirds? Here is a list of Bird Collectives, terms that you can use to + describe a group of birds. Birds in general +- source_sentence: A person in a kitchen looking at the oven. sentences: - - How Long Does Vicodin Stay in Your System? While the effects of Vicodin last around - 4 hours, traces of the drug can remain in your system for up to 72 hours. In some - adults you can still find traces of the drug in your system after 5 days. A person's - weight, diet, age and level of body fat will contribute to how quickly your body - is able to process and rid itself of the drug. In general, the higher your metabolism - rate, the sooner traces of the drug will be eliminated. - - 'Stephen Lewis - IMDb IMDb Actor | Writer Stephen Lewis, will be chiefly remembered - for the comedy catchphrase: "I ''ate you Butler!" He delivered it week after week - in the hit sitcom On The Buses, a saucy slice of life that ran on ITV from 1969 - to 1973. Lewis was Cyril "Blakey" Blake, a bus inspector with a Hitler moustache - and delusions of grandeur. His nemesis was Stan Butler, a driver ... See full - bio » Born:' - - 'Definition of Dysuria Our Symptoms article on Burning Urination provides a comprehensive - look at the possible causes and treatments of Burning Urination. Definition of - Dysuria Dysuria: Pain during urination, or difficulty urinating. Dysuria is usually - caused by inflammation of the urethra, frequently as a result of infection. Last - Editorial Review: 5/13/2016' -- source_sentence: 'Two cups of black coffee sitting next to a coffee pot. ' + - "staying warm has a positive impact on an animal 's survival. Furry animals grow\ + \ thicker coats to keep warm in the winter. \n Furry animals grow thicker coats\ + \ which has a positive impact on their survival. " + - A woman In the kitchen opening her oven. + - EE has apologised after a fault left some of its customers unable to use the internet + on their mobile devices. +- source_sentence: Air can be separated into several elements. sentences: - - As of 19 March , more than 225,000 cases of COVID-19 have been reported in over - 150 countries and territories , resulting in more than 9,200 deaths and 85,000 - recoveries . - - Other than gametes, normal human cells have a total of 46 chromosomes per cell. - - two small cups filled with coffee next to a silver coffee pot -- source_sentence: Larry Lurex was the original stage name of which late singer? + - Which of the following substances can be separated into several elements? + - 'Funny Interesting Facts Humor Strange: Carl and the Passions changed band name + to what Carl and the Passions changed band name to what Beach Boys Carl and the + Passions - "So Tough" is the fifteenth studio album released by The Beach Boys + in 1972. In its initial release, it was the second disc of a two-album set with + Pet Sounds (which The Beach Boys were able to license from Capitol Records). Unfortunately, + due to the fact that Carl and the Passions - "So Tough" was a transitional album + that saw the departure of one member and the introduction of two new ones, making + it wildly inconsistent in terms of type of material present, it paled next to + their 1966 classic and was seen as something of a disappointment in its time of + release. The title of the album itself was a reference to an early band Carl Wilson + had been in as a teenager (some say a possible early name for the Beach Boys). + It was also the first album released under a new deal with Warner Bros. that allowed + the company to distribute all future Beach Boys product in foreign as well as + domestic markets.' + - Which statement correctly describes a relationship between two human body systems? +- source_sentence: What do outdoor plants require to survive? sentences: - - Larry Lurex Story - Freddie Mercury Net Worth Larry Lurex Story Read more... Freddie - Mercury Freddie Mercury Net Worth is $100 Million. Freddie Mercury was born in - Zanzibar and has an estimated net worth of $100 million dollars. As the lead singer - and songwriter with the hugely successful British band, Queen, Freddie Mercury - wrote many hit. Freddie Mercury (born Farrok... Freddie Mercury Net Worth is $100 - Million. Freddie Mercury Net Worth is $100 Million. Freddie Mercury was born in - Zanzibar and has an estimated net worth of $100 million dollars. As the lead singer - and songwriter with the hugely successful British band, Queen, Freddie Mercury - wrote many hit Freddie Mercury , 5 September 1946 - 24 November 1991) was a British - musician, singer and songwriter, best known as the lead vocalist and lyricist - of the rock band Queen. As a performer, he was known for his flamboyant stage - persona and powerful vocals over a four-octave range. As a songwriter, Mercury - composed many hits for Queen, including "Bohemian Rhapsody", "Killer Queen", "Somebody - to Love", "Don't Stop Me Now", "Crazy Little Thing Called Love" and "We Are the - Champions". In addition to his work with Queen, he led a solo career, and also - occasionally served as a producer and guest musician for other artists. He died - of bronchopneumonia brought on by AIDS on 24 November 1991, only one day after - publicly acknowledging he had the disease. Mercury was a Parsi born in Zanzibar - and grew up there and in India until his mid-teens. He has been referred to as - "Britain's first Asian rock star". In 2002, Mercury was placed ... - - The everyday beverage coffee is a commodity second only to oil in worldwide trade. - - more than 680,000 cases of COVID-19 have been reported in over 190 countries and - territories , resulting in approximately 31,900 deaths . + - "a plants require water for survival. If no rain or watering, the plant dies.\ + \ \n Outdoor plants require rain to survive." + - (Vegan) soups are nutritious. In addition to them being easy to digest, most the + time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables, + and beans. Because the soup is full of those nutrients AND that it's easy to digest, + your body is able to absorb more of those nutrients into your system. + - If you do the math, there are 11,238,513 possible combinations of five white balls + (without order mattering). Multiply that by the 26 possible red balls, and you + get 292,201,338 possible Powerball number combinations. At $2 per ticket, you'd + need $584,402,676 to buy every single combination and guarantee a win. model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: @@ -155,34 +141,34 @@ model-index: type: sts-test metrics: - type: pearson_cosine - value: 0.6538441701104785 + value: 0.6714313838072434 name: Pearson Cosine - type: spearman_cosine - value: 0.6608537808023268 + value: 0.6764470893265916 name: Spearman Cosine - type: pearson_manhattan - value: 0.6693534702440489 + value: 0.6861398225745147 name: Pearson Manhattan - type: spearman_manhattan - value: 0.663396547474586 + value: 0.6796621094474724 name: Spearman Manhattan - type: pearson_euclidean - value: 0.6659159057834463 + value: 0.6827244055069464 name: Pearson Euclidean - type: spearman_euclidean - value: 0.6608898503190082 + value: 0.6765170355481462 name: Spearman Euclidean - type: pearson_dot - value: 0.6529234578170307 + value: 0.6696832549446547 name: Pearson Dot - type: spearman_dot - value: 0.6600371150331951 + value: 0.6729786875637124 name: Spearman Dot - type: pearson_max - value: 0.6693534702440489 + value: 0.6861398225745147 name: Pearson Max - type: spearman_max - value: 0.663396547474586 + value: 0.6796621094474724 name: Spearman Max - task: type: binary-classification @@ -192,109 +178,109 @@ model-index: type: allNLI-dev metrics: - type: cosine_accuracy - value: 0.6953125 + value: 0.693359375 name: Cosine Accuracy - type: cosine_accuracy_threshold - value: 0.9144611358642578 + value: 0.91576087474823 name: Cosine Accuracy Threshold - type: cosine_f1 - value: 0.560364464692483 + value: 0.5541125541125541 name: Cosine F1 - type: cosine_f1_threshold - value: 0.8176530599594116 + value: 0.8233019709587097 name: Cosine F1 Threshold - type: cosine_precision - value: 0.462406015037594 + value: 0.4429065743944637 name: Cosine Precision - type: cosine_recall - value: 0.7109826589595376 + value: 0.7398843930635838 name: Cosine Recall - type: cosine_ap - value: 0.5199510827485813 + value: 0.5144632305543018 name: Cosine Ap - type: dot_accuracy value: 0.69140625 name: Dot Accuracy - type: dot_accuracy_threshold - value: 701.11181640625 + value: 702.5236206054688 name: Dot Accuracy Threshold - type: dot_f1 - value: 0.5522041763341067 + value: 0.5553145336225597 name: Dot F1 - type: dot_f1_threshold - value: 626.5067138671875 + value: 628.98291015625 name: Dot F1 Threshold - type: dot_precision - value: 0.46124031007751937 + value: 0.4444444444444444 name: Dot Precision - type: dot_recall - value: 0.6878612716763006 + value: 0.7398843930635838 name: Dot Recall - type: dot_ap - value: 0.518193951147311 + value: 0.5124233096341834 name: Dot Ap - type: manhattan_accuracy - value: 0.69140625 + value: 0.689453125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold - value: 245.27915954589844 + value: 229.2916717529297 name: Manhattan Accuracy Threshold - type: manhattan_f1 - value: 0.5572354211663066 + value: 0.5631929046563192 name: Manhattan F1 - type: manhattan_f1_threshold - value: 361.41156005859375 + value: 344.4880065917969 name: Manhattan F1 Threshold - type: manhattan_precision - value: 0.44482758620689655 + value: 0.4568345323741007 name: Manhattan Precision - type: manhattan_recall - value: 0.7456647398843931 + value: 0.7341040462427746 name: Manhattan Recall - type: manhattan_ap - value: 0.5198398437485072 + value: 0.5121340318215517 name: Manhattan Ap - type: euclidean_accuracy - value: 0.6953125 + value: 0.697265625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold - value: 11.449090003967285 + value: 11.35433578491211 name: Euclidean Accuracy Threshold - type: euclidean_f1 - value: 0.560364464692483 + value: 0.5541125541125541 name: Euclidean F1 - type: euclidean_f1_threshold - value: 16.681232452392578 + value: 16.42122459411621 name: Euclidean F1 Threshold - type: euclidean_precision - value: 0.462406015037594 + value: 0.4429065743944637 name: Euclidean Precision - type: euclidean_recall - value: 0.7109826589595376 + value: 0.7398843930635838 name: Euclidean Recall - type: euclidean_ap - value: 0.5195978197561747 + value: 0.5142904425739593 name: Euclidean Ap - type: max_accuracy - value: 0.6953125 + value: 0.697265625 name: Max Accuracy - type: max_accuracy_threshold - value: 701.11181640625 + value: 702.5236206054688 name: Max Accuracy Threshold - type: max_f1 - value: 0.560364464692483 + value: 0.5631929046563192 name: Max F1 - type: max_f1_threshold - value: 626.5067138671875 + value: 628.98291015625 name: Max F1 Threshold - type: max_precision - value: 0.462406015037594 + value: 0.4568345323741007 name: Max Precision - type: max_recall - value: 0.7456647398843931 + value: 0.7398843930635838 name: Max Recall - type: max_ap - value: 0.5199510827485813 + value: 0.5144632305543018 name: Max Ap - task: type: binary-classification @@ -307,106 +293,106 @@ model-index: value: 0.6796875 name: Cosine Accuracy - type: cosine_accuracy_threshold - value: 0.7946834564208984 + value: 0.7822062969207764 name: Cosine Accuracy Threshold - type: cosine_f1 - value: 0.6925566343042071 + value: 0.6888111888111887 name: Cosine F1 - type: cosine_f1_threshold - value: 0.710798978805542 + value: 0.7400832176208496 name: Cosine F1 Threshold - type: cosine_precision - value: 0.5602094240837696 + value: 0.5863095238095238 name: Cosine Precision - type: cosine_recall - value: 0.9067796610169492 + value: 0.8347457627118644 name: Cosine Recall - type: cosine_ap - value: 0.7029887964034148 + value: 0.7033794247567502 name: Cosine Ap - type: dot_accuracy - value: 0.677734375 + value: 0.671875 name: Dot Accuracy - type: dot_accuracy_threshold - value: 605.239990234375 + value: 591.1290283203125 name: Dot Accuracy Threshold - type: dot_f1 - value: 0.6929392446633826 + value: 0.6865671641791046 name: Dot F1 - type: dot_f1_threshold - value: 548.1533203125 + value: 550.0813598632812 name: Dot F1 Threshold - type: dot_precision - value: 0.5656836461126006 + value: 0.5640326975476839 name: Dot Precision - type: dot_recall - value: 0.8940677966101694 + value: 0.8771186440677966 name: Dot Recall - type: dot_ap - value: 0.6994501763457299 + value: 0.7006971037825662 name: Dot Ap - type: manhattan_accuracy - value: 0.69921875 + value: 0.689453125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold - value: 378.17779541015625 + value: 382.2354431152344 name: Manhattan Accuracy Threshold - type: manhattan_f1 - value: 0.6949429037520392 + value: 0.689655172413793 name: Manhattan F1 - type: manhattan_f1_threshold - value: 440.03692626953125 + value: 423.2154235839844 name: Manhattan F1 Threshold - type: manhattan_precision - value: 0.5649867374005305 + value: 0.5813953488372093 name: Manhattan Precision - type: manhattan_recall - value: 0.902542372881356 + value: 0.847457627118644 name: Manhattan Recall - type: manhattan_ap - value: 0.7121888278992424 + value: 0.7099747296529381 name: Manhattan Ap - type: euclidean_accuracy - value: 0.6796875 + value: 0.681640625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold - value: 17.590106964111328 + value: 18.154266357421875 name: Euclidean Accuracy Threshold - type: euclidean_f1 - value: 0.6957928802588996 + value: 0.6888111888111887 name: Euclidean F1 - type: euclidean_f1_threshold - value: 20.986061096191406 + value: 19.91478729248047 name: Euclidean F1 Threshold - type: euclidean_precision - value: 0.56282722513089 + value: 0.5863095238095238 name: Euclidean Precision - type: euclidean_recall - value: 0.9110169491525424 + value: 0.8347457627118644 name: Euclidean Recall - type: euclidean_ap - value: 0.7036846072487284 + value: 0.7040751495314653 name: Euclidean Ap - type: max_accuracy - value: 0.69921875 + value: 0.689453125 name: Max Accuracy - type: max_accuracy_threshold - value: 605.239990234375 + value: 591.1290283203125 name: Max Accuracy Threshold - type: max_f1 - value: 0.6957928802588996 + value: 0.689655172413793 name: Max F1 - type: max_f1_threshold - value: 548.1533203125 + value: 550.0813598632812 name: Max F1 Threshold - type: max_precision - value: 0.5656836461126006 + value: 0.5863095238095238 name: Max Precision - type: max_recall - value: 0.9110169491525424 + value: 0.8771186440677966 name: Max Recall - type: max_ap - value: 0.7121888278992424 + value: 0.7099747296529381 name: Max Ap --- @@ -438,6 +424,8 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [m SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( + (alpha_dropout_layer): Dropout(p=0.01, inplace=False) + (gate_dropout_layer): Dropout(p=0.05, inplace=False) (linear_cls_pj): Linear(in_features=768, out_features=768, bias=True) (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True) (linear_mean_pj): Linear(in_features=768, out_features=768, bias=True) @@ -472,9 +460,9 @@ from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest2-step1") # Run inference sentences = [ - 'Larry Lurex was the original stage name of which late singer?', - 'Larry Lurex Story - Freddie Mercury Net Worth Larry Lurex Story Read more... Freddie Mercury Freddie Mercury Net Worth is $100 Million. Freddie Mercury was born in Zanzibar and has an estimated net worth of $100 million dollars. As the lead singer and songwriter with the hugely successful British band, Queen, Freddie Mercury wrote many hit. Freddie Mercury (born Farrok... Freddie Mercury Net Worth is $100 Million. Freddie Mercury Net Worth is $100 Million. Freddie Mercury was born in Zanzibar and has an estimated net worth of $100 million dollars. As the lead singer and songwriter with the hugely successful British band, Queen, Freddie Mercury wrote many hit Freddie Mercury , 5 September 1946 - 24 November 1991) was a British musician, singer and songwriter, best known as the lead vocalist and lyricist of the rock band Queen. As a performer, he was known for his flamboyant stage persona and powerful vocals over a four-octave range. As a songwriter, Mercury composed many hits for Queen, including "Bohemian Rhapsody", "Killer Queen", "Somebody to Love", "Don\'t Stop Me Now", "Crazy Little Thing Called Love" and "We Are the Champions". In addition to his work with Queen, he led a solo career, and also occasionally served as a producer and guest musician for other artists. He died of bronchopneumonia brought on by AIDS on 24 November 1991, only one day after publicly acknowledging he had the disease. Mercury was a Parsi born in Zanzibar and grew up there and in India until his mid-teens. He has been referred to as "Britain\'s first Asian rock star". In 2002, Mercury was placed ...', - 'The everyday beverage coffee is a commodity second only to oil in worldwide trade.', + 'What do outdoor plants require to survive?', + 'a plants require water for survival. If no rain or watering, the plant dies. \n Outdoor plants require rain to survive.', + "(Vegan) soups are nutritious. In addition to them being easy to digest, most the time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables, and beans. Because the soup is full of those nutrients AND that it's easy to digest, your body is able to absorb more of those nutrients into your system.", ] embeddings = model.encode(sentences) print(embeddings.shape) @@ -520,100 +508,100 @@ You can finetune this model on your own dataset. | Metric | Value | |:--------------------|:-----------| -| pearson_cosine | 0.6538 | -| **spearman_cosine** | **0.6609** | -| pearson_manhattan | 0.6694 | -| spearman_manhattan | 0.6634 | -| pearson_euclidean | 0.6659 | -| spearman_euclidean | 0.6609 | -| pearson_dot | 0.6529 | -| spearman_dot | 0.66 | -| pearson_max | 0.6694 | -| spearman_max | 0.6634 | +| pearson_cosine | 0.6714 | +| **spearman_cosine** | **0.6764** | +| pearson_manhattan | 0.6861 | +| spearman_manhattan | 0.6797 | +| pearson_euclidean | 0.6827 | +| spearman_euclidean | 0.6765 | +| pearson_dot | 0.6697 | +| spearman_dot | 0.673 | +| pearson_max | 0.6861 | +| spearman_max | 0.6797 | #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) -| Metric | Value | -|:-----------------------------|:---------| -| cosine_accuracy | 0.6953 | -| cosine_accuracy_threshold | 0.9145 | -| cosine_f1 | 0.5604 | -| cosine_f1_threshold | 0.8177 | -| cosine_precision | 0.4624 | -| cosine_recall | 0.711 | -| cosine_ap | 0.52 | -| dot_accuracy | 0.6914 | -| dot_accuracy_threshold | 701.1118 | -| dot_f1 | 0.5522 | -| dot_f1_threshold | 626.5067 | -| dot_precision | 0.4612 | -| dot_recall | 0.6879 | -| dot_ap | 0.5182 | -| manhattan_accuracy | 0.6914 | -| manhattan_accuracy_threshold | 245.2792 | -| manhattan_f1 | 0.5572 | -| manhattan_f1_threshold | 361.4116 | -| manhattan_precision | 0.4448 | -| manhattan_recall | 0.7457 | -| manhattan_ap | 0.5198 | -| euclidean_accuracy | 0.6953 | -| euclidean_accuracy_threshold | 11.4491 | -| euclidean_f1 | 0.5604 | -| euclidean_f1_threshold | 16.6812 | -| euclidean_precision | 0.4624 | -| euclidean_recall | 0.711 | -| euclidean_ap | 0.5196 | -| max_accuracy | 0.6953 | -| max_accuracy_threshold | 701.1118 | -| max_f1 | 0.5604 | -| max_f1_threshold | 626.5067 | -| max_precision | 0.4624 | -| max_recall | 0.7457 | -| **max_ap** | **0.52** | +| Metric | Value | +|:-----------------------------|:-----------| +| cosine_accuracy | 0.6934 | +| cosine_accuracy_threshold | 0.9158 | +| cosine_f1 | 0.5541 | +| cosine_f1_threshold | 0.8233 | +| cosine_precision | 0.4429 | +| cosine_recall | 0.7399 | +| cosine_ap | 0.5145 | +| dot_accuracy | 0.6914 | +| dot_accuracy_threshold | 702.5236 | +| dot_f1 | 0.5553 | +| dot_f1_threshold | 628.9829 | +| dot_precision | 0.4444 | +| dot_recall | 0.7399 | +| dot_ap | 0.5124 | +| manhattan_accuracy | 0.6895 | +| manhattan_accuracy_threshold | 229.2917 | +| manhattan_f1 | 0.5632 | +| manhattan_f1_threshold | 344.488 | +| manhattan_precision | 0.4568 | +| manhattan_recall | 0.7341 | +| manhattan_ap | 0.5121 | +| euclidean_accuracy | 0.6973 | +| euclidean_accuracy_threshold | 11.3543 | +| euclidean_f1 | 0.5541 | +| euclidean_f1_threshold | 16.4212 | +| euclidean_precision | 0.4429 | +| euclidean_recall | 0.7399 | +| euclidean_ap | 0.5143 | +| max_accuracy | 0.6973 | +| max_accuracy_threshold | 702.5236 | +| max_f1 | 0.5632 | +| max_f1_threshold | 628.9829 | +| max_precision | 0.4568 | +| max_recall | 0.7399 | +| **max_ap** | **0.5145** | #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) -| Metric | Value | -|:-----------------------------|:-----------| -| cosine_accuracy | 0.6797 | -| cosine_accuracy_threshold | 0.7947 | -| cosine_f1 | 0.6926 | -| cosine_f1_threshold | 0.7108 | -| cosine_precision | 0.5602 | -| cosine_recall | 0.9068 | -| cosine_ap | 0.703 | -| dot_accuracy | 0.6777 | -| dot_accuracy_threshold | 605.24 | -| dot_f1 | 0.6929 | -| dot_f1_threshold | 548.1533 | -| dot_precision | 0.5657 | -| dot_recall | 0.8941 | -| dot_ap | 0.6995 | -| manhattan_accuracy | 0.6992 | -| manhattan_accuracy_threshold | 378.1778 | -| manhattan_f1 | 0.6949 | -| manhattan_f1_threshold | 440.0369 | -| manhattan_precision | 0.565 | -| manhattan_recall | 0.9025 | -| manhattan_ap | 0.7122 | -| euclidean_accuracy | 0.6797 | -| euclidean_accuracy_threshold | 17.5901 | -| euclidean_f1 | 0.6958 | -| euclidean_f1_threshold | 20.9861 | -| euclidean_precision | 0.5628 | -| euclidean_recall | 0.911 | -| euclidean_ap | 0.7037 | -| max_accuracy | 0.6992 | -| max_accuracy_threshold | 605.24 | -| max_f1 | 0.6958 | -| max_f1_threshold | 548.1533 | -| max_precision | 0.5657 | -| max_recall | 0.911 | -| **max_ap** | **0.7122** | +| Metric | Value | +|:-----------------------------|:---------| +| cosine_accuracy | 0.6797 | +| cosine_accuracy_threshold | 0.7822 | +| cosine_f1 | 0.6888 | +| cosine_f1_threshold | 0.7401 | +| cosine_precision | 0.5863 | +| cosine_recall | 0.8347 | +| cosine_ap | 0.7034 | +| dot_accuracy | 0.6719 | +| dot_accuracy_threshold | 591.129 | +| dot_f1 | 0.6866 | +| dot_f1_threshold | 550.0814 | +| dot_precision | 0.564 | +| dot_recall | 0.8771 | +| dot_ap | 0.7007 | +| manhattan_accuracy | 0.6895 | +| manhattan_accuracy_threshold | 382.2354 | +| manhattan_f1 | 0.6897 | +| manhattan_f1_threshold | 423.2154 | +| manhattan_precision | 0.5814 | +| manhattan_recall | 0.8475 | +| manhattan_ap | 0.71 | +| euclidean_accuracy | 0.6816 | +| euclidean_accuracy_threshold | 18.1543 | +| euclidean_f1 | 0.6888 | +| euclidean_f1_threshold | 19.9148 | +| euclidean_precision | 0.5863 | +| euclidean_recall | 0.8347 | +| euclidean_ap | 0.7041 | +| max_accuracy | 0.6895 | +| max_accuracy_threshold | 591.129 | +| max_f1 | 0.6897 | +| max_f1_threshold | 550.0814 | +| max_precision | 0.5863 | +| max_recall | 0.8771 | +| **max_ap** | **0.71** |