diff --git "a/checkpoint-2745/README.md" "b/checkpoint-2745/README.md" --- "a/checkpoint-2745/README.md" +++ "b/checkpoint-2745/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: 0.6591049782717581 + value: 0.6581325546921425 name: Pearson Cosine - type: spearman_cosine - value: 0.6655085832559531 + value: 0.663227790467879 name: Spearman Cosine - type: pearson_manhattan - value: 0.674239366272805 + value: 0.6737449201833172 name: Pearson Manhattan - type: spearman_manhattan - value: 0.6690548074623918 + value: 0.6677674926772068 name: Spearman Manhattan - type: pearson_euclidean - value: 0.6710226989754035 + value: 0.6699927428669137 name: Pearson Euclidean - type: spearman_euclidean - value: 0.6657110379624879 + value: 0.6639387537576042 name: Spearman Euclidean - type: pearson_dot - value: 0.6585371125233661 + value: 0.655652438684661 name: Pearson Dot - type: spearman_dot - value: 0.6647672338597431 + value: 0.6598153725330006 name: Spearman Dot - type: pearson_max - value: 0.674239366272805 + value: 0.6737449201833172 name: Pearson Max - type: spearman_max - value: 0.6690548074623918 + value: 0.6677674926772068 name: Spearman Max - task: type: binary-classification @@ -192,109 +178,109 @@ model-index: type: allNLI-dev metrics: - type: cosine_accuracy - value: 0.697265625 + value: 0.689453125 name: Cosine Accuracy - type: cosine_accuracy_threshold - value: 0.912941038608551 + value: 0.9239224195480347 name: Cosine Accuracy Threshold - type: cosine_f1 - value: 0.558891454965358 + value: 0.5515695067264574 name: Cosine F1 - type: cosine_f1_threshold - value: 0.826492190361023 + value: 0.8346847295761108 name: Cosine F1 Threshold - type: cosine_precision - value: 0.4653846153846154 + value: 0.45054945054945056 name: Cosine Precision - type: cosine_recall - value: 0.6994219653179191 + value: 0.7109826589595376 name: Cosine Recall - type: cosine_ap - value: 0.5170912462717843 + value: 0.5100510670205529 name: Cosine Ap - type: dot_accuracy - value: 0.6953125 + value: 0.69140625 name: Dot Accuracy - type: dot_accuracy_threshold - value: 699.905517578125 + value: 704.034912109375 name: Dot Accuracy Threshold - type: dot_f1 - value: 0.5568445475638051 + value: 0.5517241379310345 name: Dot F1 - type: dot_f1_threshold - value: 630.53271484375 + value: 620.714599609375 name: Dot F1 Threshold - type: dot_precision - value: 0.46511627906976744 + value: 0.425 name: Dot Precision - type: dot_recall - value: 0.6936416184971098 + value: 0.7861271676300579 name: Dot Recall - type: dot_ap - value: 0.5173897942840021 + value: 0.5088577654112999 name: Dot Ap - type: manhattan_accuracy - value: 0.689453125 + value: 0.6875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold - value: 248.03265380859375 + value: 227.51451110839844 name: Manhattan Accuracy Threshold - type: manhattan_f1 - value: 0.5596330275229358 + value: 0.5520169851380042 name: Manhattan F1 - type: manhattan_f1_threshold - value: 344.65313720703125 + value: 344.68267822265625 name: Manhattan F1 Threshold - type: manhattan_precision - value: 0.46387832699619774 + value: 0.436241610738255 name: Manhattan Precision - type: manhattan_recall - value: 0.7052023121387283 + value: 0.7514450867052023 name: Manhattan Recall - type: manhattan_ap - value: 0.5172164569460806 + value: 0.5073092702655073 name: Manhattan Ap - type: euclidean_accuracy - value: 0.697265625 + value: 0.69140625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold - value: 11.538026809692383 + value: 11.195505142211914 name: Euclidean Accuracy Threshold - type: euclidean_f1 - value: 0.5576036866359447 + value: 0.5491071428571428 name: Euclidean F1 - type: euclidean_f1_threshold - value: 16.292882919311523 + value: 15.894262313842773 name: Euclidean F1 Threshold - type: euclidean_precision - value: 0.46360153256704983 + value: 0.44727272727272727 name: Euclidean Precision - type: euclidean_recall - value: 0.6994219653179191 + value: 0.7109826589595376 name: Euclidean Recall - type: euclidean_ap - value: 0.5171319156824372 + value: 0.5098181318339068 name: Euclidean Ap - type: max_accuracy - value: 0.697265625 + value: 0.69140625 name: Max Accuracy - type: max_accuracy_threshold - value: 699.905517578125 + value: 704.034912109375 name: Max Accuracy Threshold - type: max_f1 - value: 0.5596330275229358 + value: 0.5520169851380042 name: Max F1 - type: max_f1_threshold - value: 630.53271484375 + value: 620.714599609375 name: Max F1 Threshold - type: max_precision - value: 0.4653846153846154 + value: 0.45054945054945056 name: Max Precision - type: max_recall - value: 0.7052023121387283 + value: 0.7861271676300579 name: Max Recall - type: max_ap - value: 0.5173897942840021 + value: 0.5100510670205529 name: Max Ap - task: type: binary-classification @@ -304,109 +290,109 @@ model-index: type: Qnli-dev metrics: - type: cosine_accuracy - value: 0.6953125 + value: 0.67578125 name: Cosine Accuracy - type: cosine_accuracy_threshold - value: 0.8067620396614075 + value: 0.7981740236282349 name: Cosine Accuracy Threshold - type: cosine_f1 - value: 0.6953748006379585 + value: 0.6933797909407665 name: Cosine F1 - type: cosine_f1_threshold - value: 0.7095954418182373 + value: 0.744655430316925 name: Cosine F1 Threshold - type: cosine_precision - value: 0.5575447570332481 + value: 0.5887573964497042 name: Cosine Precision - type: cosine_recall - value: 0.923728813559322 + value: 0.8432203389830508 name: Cosine Recall - type: cosine_ap - value: 0.7043241142333805 + value: 0.7024537673431017 name: Cosine Ap - type: dot_accuracy - value: 0.6875 + value: 0.669921875 name: Dot Accuracy - type: dot_accuracy_threshold - value: 612.6456298828125 + value: 610.97509765625 name: Dot Accuracy Threshold - type: dot_f1 - value: 0.6943999999999998 + value: 0.6900175131348512 name: Dot F1 - type: dot_f1_threshold - value: 541.830322265625 + value: 568.671875 name: Dot F1 Threshold - type: dot_precision - value: 0.5578406169665809 + value: 0.5880597014925373 name: Dot Precision - type: dot_recall - value: 0.9194915254237288 + value: 0.8347457627118644 name: Dot Recall - type: dot_ap - value: 0.7015226184100491 + value: 0.7000264168874522 name: Dot Ap - type: manhattan_accuracy - value: 0.6953125 + value: 0.69140625 name: Manhattan Accuracy - type: manhattan_accuracy_threshold - value: 364.14117431640625 + value: 383.7911682128906 name: Manhattan Accuracy Threshold - type: manhattan_f1 - value: 0.694136291600634 + value: 0.6901408450704225 name: Manhattan F1 - type: manhattan_f1_threshold - value: 444.42218017578125 + value: 413.4296875 name: Manhattan F1 Threshold - type: manhattan_precision - value: 0.5544303797468354 + value: 0.5903614457831325 name: Manhattan Precision - type: manhattan_recall - value: 0.9279661016949152 + value: 0.8305084745762712 name: Manhattan Recall - type: manhattan_ap - value: 0.7137495271732022 + value: 0.710276735549099 name: Manhattan Ap - type: euclidean_accuracy - value: 0.701171875 + value: 0.673828125 name: Euclidean Accuracy - type: euclidean_accuracy_threshold - value: 17.201496124267578 + value: 17.699954986572266 name: Euclidean Accuracy Threshold - type: euclidean_f1 - value: 0.6953748006379585 + value: 0.6933797909407665 name: Euclidean F1 - type: euclidean_f1_threshold - value: 20.963668823242188 + value: 19.75226402282715 name: Euclidean F1 Threshold - type: euclidean_precision - value: 0.5575447570332481 + value: 0.5887573964497042 name: Euclidean Precision - type: euclidean_recall - value: 0.923728813559322 + value: 0.8432203389830508 name: Euclidean Recall - type: euclidean_ap - value: 0.7050333505348279 + value: 0.7027015569550685 name: Euclidean Ap - type: max_accuracy - value: 0.701171875 + value: 0.69140625 name: Max Accuracy - type: max_accuracy_threshold - value: 612.6456298828125 + value: 610.97509765625 name: Max Accuracy Threshold - type: max_f1 - value: 0.6953748006379585 + value: 0.6933797909407665 name: Max F1 - type: max_f1_threshold - value: 541.830322265625 + value: 568.671875 name: Max F1 Threshold - type: max_precision - value: 0.5578406169665809 + value: 0.5903614457831325 name: Max Precision - type: max_recall - value: 0.9279661016949152 + value: 0.8432203389830508 name: Max Recall - type: max_ap - value: 0.7137495271732022 + value: 0.710276735549099 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-checkpoints-tmp") # 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) @@ -520,16 +508,16 @@ You can finetune this model on your own dataset. | Metric | Value | |:--------------------|:-----------| -| pearson_cosine | 0.6591 | -| **spearman_cosine** | **0.6655** | -| pearson_manhattan | 0.6742 | -| spearman_manhattan | 0.6691 | -| pearson_euclidean | 0.671 | -| spearman_euclidean | 0.6657 | -| pearson_dot | 0.6585 | -| spearman_dot | 0.6648 | -| pearson_max | 0.6742 | -| spearman_max | 0.6691 | +| pearson_cosine | 0.6581 | +| **spearman_cosine** | **0.6632** | +| pearson_manhattan | 0.6737 | +| spearman_manhattan | 0.6678 | +| pearson_euclidean | 0.67 | +| spearman_euclidean | 0.6639 | +| pearson_dot | 0.6557 | +| spearman_dot | 0.6598 | +| pearson_max | 0.6737 | +| spearman_max | 0.6678 | #### Binary Classification * Dataset: `allNLI-dev` @@ -537,41 +525,41 @@ You can finetune this model on your own dataset. | Metric | Value | |:-----------------------------|:-----------| -| cosine_accuracy | 0.6973 | -| cosine_accuracy_threshold | 0.9129 | -| cosine_f1 | 0.5589 | -| cosine_f1_threshold | 0.8265 | -| cosine_precision | 0.4654 | -| cosine_recall | 0.6994 | -| cosine_ap | 0.5171 | -| dot_accuracy | 0.6953 | -| dot_accuracy_threshold | 699.9055 | -| dot_f1 | 0.5568 | -| dot_f1_threshold | 630.5327 | -| dot_precision | 0.4651 | -| dot_recall | 0.6936 | -| dot_ap | 0.5174 | -| manhattan_accuracy | 0.6895 | -| manhattan_accuracy_threshold | 248.0327 | -| manhattan_f1 | 0.5596 | -| manhattan_f1_threshold | 344.6531 | -| manhattan_precision | 0.4639 | -| manhattan_recall | 0.7052 | -| manhattan_ap | 0.5172 | -| euclidean_accuracy | 0.6973 | -| euclidean_accuracy_threshold | 11.538 | -| euclidean_f1 | 0.5576 | -| euclidean_f1_threshold | 16.2929 | -| euclidean_precision | 0.4636 | -| euclidean_recall | 0.6994 | -| euclidean_ap | 0.5171 | -| max_accuracy | 0.6973 | -| max_accuracy_threshold | 699.9055 | -| max_f1 | 0.5596 | -| max_f1_threshold | 630.5327 | -| max_precision | 0.4654 | -| max_recall | 0.7052 | -| **max_ap** | **0.5174** | +| cosine_accuracy | 0.6895 | +| cosine_accuracy_threshold | 0.9239 | +| cosine_f1 | 0.5516 | +| cosine_f1_threshold | 0.8347 | +| cosine_precision | 0.4505 | +| cosine_recall | 0.711 | +| cosine_ap | 0.5101 | +| dot_accuracy | 0.6914 | +| dot_accuracy_threshold | 704.0349 | +| dot_f1 | 0.5517 | +| dot_f1_threshold | 620.7146 | +| dot_precision | 0.425 | +| dot_recall | 0.7861 | +| dot_ap | 0.5089 | +| manhattan_accuracy | 0.6875 | +| manhattan_accuracy_threshold | 227.5145 | +| manhattan_f1 | 0.552 | +| manhattan_f1_threshold | 344.6827 | +| manhattan_precision | 0.4362 | +| manhattan_recall | 0.7514 | +| manhattan_ap | 0.5073 | +| euclidean_accuracy | 0.6914 | +| euclidean_accuracy_threshold | 11.1955 | +| euclidean_f1 | 0.5491 | +| euclidean_f1_threshold | 15.8943 | +| euclidean_precision | 0.4473 | +| euclidean_recall | 0.711 | +| euclidean_ap | 0.5098 | +| max_accuracy | 0.6914 | +| max_accuracy_threshold | 704.0349 | +| max_f1 | 0.552 | +| max_f1_threshold | 620.7146 | +| max_precision | 0.4505 | +| max_recall | 0.7861 | +| **max_ap** | **0.5101** | #### Binary Classification * Dataset: `Qnli-dev` @@ -579,41 +567,41 @@ You can finetune this model on your own dataset. | Metric | Value | |:-----------------------------|:-----------| -| cosine_accuracy | 0.6953 | -| cosine_accuracy_threshold | 0.8068 | -| cosine_f1 | 0.6954 | -| cosine_f1_threshold | 0.7096 | -| cosine_precision | 0.5575 | -| cosine_recall | 0.9237 | -| cosine_ap | 0.7043 | -| dot_accuracy | 0.6875 | -| dot_accuracy_threshold | 612.6456 | -| dot_f1 | 0.6944 | -| dot_f1_threshold | 541.8303 | -| dot_precision | 0.5578 | -| dot_recall | 0.9195 | -| dot_ap | 0.7015 | -| manhattan_accuracy | 0.6953 | -| manhattan_accuracy_threshold | 364.1412 | -| manhattan_f1 | 0.6941 | -| manhattan_f1_threshold | 444.4222 | -| manhattan_precision | 0.5544 | -| manhattan_recall | 0.928 | -| manhattan_ap | 0.7137 | -| euclidean_accuracy | 0.7012 | -| euclidean_accuracy_threshold | 17.2015 | -| euclidean_f1 | 0.6954 | -| euclidean_f1_threshold | 20.9637 | -| euclidean_precision | 0.5575 | -| euclidean_recall | 0.9237 | -| euclidean_ap | 0.705 | -| max_accuracy | 0.7012 | -| max_accuracy_threshold | 612.6456 | -| max_f1 | 0.6954 | -| max_f1_threshold | 541.8303 | -| max_precision | 0.5578 | -| max_recall | 0.928 | -| **max_ap** | **0.7137** | +| cosine_accuracy | 0.6758 | +| cosine_accuracy_threshold | 0.7982 | +| cosine_f1 | 0.6934 | +| cosine_f1_threshold | 0.7447 | +| cosine_precision | 0.5888 | +| cosine_recall | 0.8432 | +| cosine_ap | 0.7025 | +| dot_accuracy | 0.6699 | +| dot_accuracy_threshold | 610.9751 | +| dot_f1 | 0.69 | +| dot_f1_threshold | 568.6719 | +| dot_precision | 0.5881 | +| dot_recall | 0.8347 | +| dot_ap | 0.7 | +| manhattan_accuracy | 0.6914 | +| manhattan_accuracy_threshold | 383.7912 | +| manhattan_f1 | 0.6901 | +| manhattan_f1_threshold | 413.4297 | +| manhattan_precision | 0.5904 | +| manhattan_recall | 0.8305 | +| manhattan_ap | 0.7103 | +| euclidean_accuracy | 0.6738 | +| euclidean_accuracy_threshold | 17.7 | +| euclidean_f1 | 0.6934 | +| euclidean_f1_threshold | 19.7523 | +| euclidean_precision | 0.5888 | +| euclidean_recall | 0.8432 | +| euclidean_ap | 0.7027 | +| max_accuracy | 0.6914 | +| max_accuracy_threshold | 610.9751 | +| max_f1 | 0.6934 | +| max_f1_threshold | 568.6719 | +| max_precision | 0.5904 | +| max_recall | 0.8432 | +| **max_ap** | **0.7103** |