---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:3375201
- loss:MSELoss
widget:
- source_sentence: What is Weboob. Weboob is a collection of applications able to
interact with websites, without requiring the user to open them in a browser.
It also provides well-defined APIs to talk to websites lacking one.
sentences:
- Moreno and colleagues (Mossio et al. 2009; Moreno & Mossio 2015) have also claimed
that their organizational approach unifies across backwardlooking and forward-looking
accounts by describing activities that atemporally account for the continuing
persistence of traits.
- average cost of a dj for a wedding 2015
- CIALIS tablets should not be split, crushed or separated in any way. Do not split
CIALIS tablets; the entire dose should be taken. Splitting or crushing may result
in the patient receiving more or less than the desired dose. References. CIALIS
[package insert].
- source_sentence: Cement Impregnated Particle Board is a revolutionary, waterproof,
cement impregnated acoustic floor panel designed to improve impact and airborne
noise transfer through separating floors. Cement Impregnated Particle Board installed
on top of R10 resilient insulation provides a very efficient and stable floating
floor.
sentences:
- '1. to estimate officially the value of (property) for tax purposes. 2. to determine
the amount of (damages, a fine, etc.). 3. to impose a tax or other charge on:to
assess members for painting the clubhouse. 4. to estimate or judge the value,
character, etc., of; evaluate: to assess one''s efforts.'
- '# Lageia
Lageia (Greek: Λάγεια [ˈlaʝa]; Turkish: Laya) is a small village in the Larnaca
District of Cyprus, 7 km west of Pano Lefkara. Its population in 2011 was 28.
'
- The carbohydrates in pineapples are mostly simple sugars, such as sucrose, fructose
and glucose. They also contain some fiber. A cup (165 grams) of pineapples contains
21.7 grams of carbs, and 2.3 grams of fiber, so there are 19.4 grams of digestible
(net) carbs in each cup. The glycemic index value of pineapples can range from
45-66, which is in the medium range (4).
- source_sentence: Representations (concepts) can be portrayed as partitions in multi-dimensional
vector spaces. One example is a neuron activation vector space, where a point
in this space represents one possible pattern of activity in all neurons in the
network.
sentences:
- "# Listeners Bounce Back After Being Laid Off \n\n Last week we asked listeners\
\ who have been laid off to share their success stories. Many tolds us tales of\
\ finding work despite the tough economy."
- The neurobiological uniqueness of the pain inhibitory system, contrasted with
the mechanisms of other sensory modalities, renders pain processing atypical,
which leads to the conclusion that pain experiences are atypical conscious events.
- "# Robert Hampton Gray\nRobert Hampton \"Hammy\" Gray, VC, DSC (November 2, 1917\
\ – August 9, 1945) was a Canadian naval officer, pilot, and recipient of the\
\ Victoria Cross during World War II. He and Eugene Esmonde are the only personnel\
\ of the Royal Navy's Fleet Air Arm to be decorated the VC in the war. Gray is\
\ the last Canadian to be awarded the Victoria Cross.\n\n## Early life\nGray was\
\ born in Trail, British Columbia, Canada, but resided from an early age in Nelson,\
\ where his father was a jeweller.\nHe completed one year at the University of\
\ Alberta before transferring to the Bachelor of Arts program at The University\
\ of British Columbia where he was a member of the Phi Delta Theta fraternity.\n\
Before completing university, he enlisted in the Royal Canadian Naval Volunteer\
\ Reserve (RCNVR) at HMCS Tecumseh in Calgary, Alberta on July 18, 1940. Originally\
\ sent to England for training in September Gray decided to join the Fleet Air\
\ Arm. Gray began his training at HMS St Vincent in January 1941 then 24th Elementary\
\ Flying Training School in Luton by March. Gray was sent back to Canada to train\
\ at RCAF Station Kingston in June. Once completing his training in September,\
\ Gray was given the rank of sub-lieutenant and by November was sent back to England\
\ to train on the Hawker Hurricane at HMS Heron. While at HMS Heron Gray had the\
\ chance to meet his brother Jack, who played the role of an RCAF air gunner in\
\ the film Target for Tonight, before being killed in a air accident not long\
\ after. \nGray initially joined 757 Naval Air Squadron at Winchester, England\
\ at the end of February 1942 where he conducted further training.\n\n## War service\n\
\n### Africa and Norway\nGray was assigned to the African theatre in May 1942,\
\ flying Hawker Hurricanes for shore-based squadrons, nos. 795, 803, and 877,\
\ where he spent two years at Nairobi. In December Gray served for a brief time\
\ aboard the aircraft carrier HMS Illustrious and on December 31 was promoted\
\ to lieutenant.\nIn February 1944 Gray was transferred back to England where\
\ trained to fly the Vought F4U Corsair fighter with 748 Naval Air Squadron at\
\ HMS Heron and on August 14 he joined 1841 NAS, based on HMS Formidable. From\
\ August 24–29, Gray took part in the unsuccessful Operation Goodwood raids against\
\ the German battleship Tirpitz, in Norway. On August 29, Gray was Mentioned in\
\ Dispatches for his participation in an attack on three German destroyers, during\
\ which his plane's rudder was shot off. On January 16, 1945, he received a further\
\ Mention, \"For undaunted courage, skill and determination in carrying out daring\
\ attacks on the German battleship Tirpitz.\"\n\n### Japan\nOn April 4, 1945,\
\ Formidable joined the British Pacific Fleet which was involved in the invasion\
\ of Okinawa. On April 16, Gray led a flight of Corsairs during the attacks against\
\ Ishigaki and Miyako airfields on Okinawa. Gray only conducted combat air patrols\
\ for the remainder of April and into May. In the aftermath of the kamikaze strikes\
\ on Formidable, the ship returned to Sydney, Australia, on May 22 where Gray\
\ helped train replacements from May to July before returning to combat on July\
\ 17. On July 18, Gray led a strafing mission against airfields in the Tokyo area\
\ and another flight to the inland sea on July 24, which damaged one merchant\
\ ship, and damaged two seaplane bases and one airbase. Gray earned a Distinguished\
\ Service Cross for aiding in sinking a Japanese destroyer in the area of Tokyo\
\ on July 28. The award was not announced until August 21, 1945, when the notice\
\ appeared in the London Gazette with the citation, \"For determination and address\
\ in air attacks on targets in Japan\".\n\n#### VC action\nOn August 9, 1945,\
\ Gray original mission was to attack Matsushima airfield, however when it was\
\ realized the airfield was out of commission Gray was ordered to attack targets\
\ of opportunity. Having spotted Japanese shipping at Onagawa Bay, Miyagi Prefecture,\
\ Japan, early in the flight, Gray led the strike force towards the bay. A few\
\ hours after the atomic bombing of Nagasaki, Lieutenant Gray (flying Vought F4U\
\ Corsair KD658, with 151 as his insignia and an X on the aircraft's tail) led\
\ an attack on a group of Japanese naval vessels. Gray scored a direct hit upon\
\ the Etorofu-class escort ship Amakusa with a 500-lb bomb which passed through\
\ the engine room and detonated a magazine below the after gun turret. The resultant\
\ explosion blew out the ship's side and caused it to sink rapidly with the loss\
\ of 71 crewmen. Gray's plane was damaged by anti-aircraft fire and crashed into\
\ the bay.\nThe citation for his VC, gazetted on November 13, 1945, described\
\ as being:\nfor great valour in leading an attack on a Japanese destroyer in\
\ Onagawa Wan, on 9 August 1945. In the face of fire from shore batteries and\
\ a heavy concentration of fire from some five warships Lieutenant Gray pressed\
\ home his attack, flying very low in order to ensure success, and, although he\
\ was hit and his aircraft was in flames, he obtained at least one direct hit,\
\ sinking the destroyer. Lieutenant Gray has consistently shown a brilliant fighting\
\ spirit and most inspiring leadership.\nGray was one of the last Canadians to\
\ die during World War II, and was the last Canadian to be awarded the Victoria\
\ Cross. His VC is owned by the Gray family.\n\n## Awards and decorations\nGray's\
\ personal awards and decorations include the following:\n| Ribbon | Description\
\ | Notes \
\ |\n| | Victoria Cross |\
\ - Citation for Victoria Cross (VC) |\n| | Distinguished\
\ Service Cross (DSC) | - Citation for Distinguished Service Cross\
\ (DSC) |\n| | 1939–1945 Star | - WWII\
\ 1939–1945 |\n| | Atlantic Star \
\ | - WWII 1939–1945 \
\ |\n| | Africa Star | - WWII\
\ 1939–1945 |\n| | Pacific Star \
\ | - WWII 1939–1945 \
\ |\n| | Defence Medal (United Kingdom) | - WWII\
\ 1939–1945 |\n| | Canadian Volunteer Service\
\ Medal | - WWII 1939–1945 with Overseas Service bar |\n\
| | War Medal 1939–1945 with Mentioned in dispatches | - WWII 1939-1945\
\ |\n\n\n## Legacy\nAs Gray's remains were never\
\ found, he was listed as missing in action and presumed dead. He is commemorated,\
\ with other Canadians who died or were buried at sea during the First and Second\
\ World Wars, at the Halifax Memorial in Point Pleasant Park, Halifax, Nova Scotia.\
\ \nThe War Memorial Gym at University of British Columbia, Royal Canadian Legion\
\ hall in Nelson, numerous other sites in Nelson, and the wardroom of HMCS Tecumseh\
\ (his RCNVR home unit) also bear plaques in his honour.\nGray is one of fourteen\
\ figures commemorated at the Valiants Memorial in Ottawa.\nA memorial for Gray\
\ was erected at Onagawa Bay in 1989 in Sakiyama Park. This is the only memorial\
\ dedicated to a foreign soldier on Japanese soil. Following the devastation of\
\ the March 11, 2011 earthquake (during which the granite monument itself was\
\ knocked over), the monument (with new plaque) was moved from its original location\
\ in Sakiyama Park to one beside the hospital (Onagawacho Community Medicine Center)\
\ in Onagawa Town. A rededication ceremony was held August 24, 2012.\nTo celebrate\
\ the Centennial of the Canadian Navy, during the 2010 air show season, Vintage\
\ Wings of Canada flew at events across Canada in a Corsair bearing the markings\
\ of the plane Gray was likely flying that fateful day.\nHis life is recorded\
\ in A Formidable Hero: Lt. R.H. Gray, VC, DSC, RCNVR by Stuart E. Soward, published\
\ by Trafford Neptune.\n\n### Grays Peak, British Columbia\nOn March 12, 1946,\
\ the Geographic Board of Canada named a mountain in Kokanee Glacier Provincial\
\ Park, British Columbia, after Gray and his brother, Flt Sgt John Balfour Gray,\
\ RCAF, who was also killed in World War II. Rising to a height of 2,753 m (9,032 ft),\
\ Grays Peak is well known in Canada as the mountain pictured on the label of\
\ Kokanee Beer.\n\n### Hampton Gray Memorial Elementary\nThe elementary school\
\ at CFB Shearwater is named after Gray.\n\n### Kingston Norman Rogers Airport\n\
Gray completed his training at No. 31 Service Flying Training School in Kingston,\
\ Ontario. There is a Harvard aircraft, same type of trainer he flew at Kingston,\
\ mounted on a pedestal with a memorial dedicated to him. Additionally, the road\
\ leading to the airport terminal has been named Hampton Gray Gate.\n\n### Royal\
\ Canadian Sea Cadets\nThe Royal Canadian Sea Cadet Corps in Nelson, BC is named\
\ 81 Hampton Gray, VC Royal Canadian Sea Cadet Corps.\n\n### Royal Canadian Air\
\ Cadets\nIn 2012, the Royal Canadian Air Cadets created a new squadron in his\
\ honour called 789 Lt. R. Hampton Gray VC Squadron which is located in Mississauga,\
\ Ontario.\n\n### Harry DeWolf-class offshore patrol vessel\nThe sixth Harry DeWolf-class\
\ offshore patrol vessel for the Royal Canadian Navy will be named for Gray.\n\
\n### Brechin, Angus, Scotland\nThe Gray family headstone in Brechin Cemetery\
\ was completely restored in 2021 after it had fallen into a state of disrepair.\
\ (The main headstone had been removed from its plinth and positioned on the adjacent\
\ grass). The work was carried out and funded by locals. On the 76th anniversary\
\ of his death and VC action a short service was conducted at the family grave.\
\ The headstone carries the inscriptions for Robert and his brother Flight Sergeant\
\ John (Jack) Balfour Gray, RCAF. He was killed on February 27, 1942 serving with\
\ 144 Squadron RAF. He is buried in Doncaster (Rosehill) Cemetery.\nA new housing\
\ development in Brechin will feature a street named after Robert Hampton Gray,\
\ Hampton Gray Way.\n"
- source_sentence: '# The Wishing-Table
The Wishing-Table (German: Tischlein, deck dich) is a 1956 West German family
film directed by Fritz Genschow and starring Werner Stock, Wolfgang Draeger and
Harald Dietl. It is based on the story of the same name by the Brothers Grimm.
## Cast
- Werner Stock as Tailor
- Wolfgang Draeger as Peter
- Harald Dietl as Paul
- Horst Keitel as Hans
- Rita-Maria Nowotny as Kathy
- Wulf Rittscher as Innkeeper
- Fritz Genschow as Woodworker
- Sigrid Hackenberg as Marie
- Renée Stobrawa as Kathy''s aunt
- Karola Ebeling as Liesel
- York Bertram as Charburner
- Otto Czarski as Robber
- Joachim Rödel as Robber
- Alexander Welbat as Robber
- Lutz Götz as Mayor
- Theodor Vogeler as Carpenter
- Nora Brand as Neighbor
- Otto Lengwinat as Miller
- Egon Stief as Servant
## Bibliography
- Jill Nelmes & Jule Selbo. Women Screenwriters: An International Guide. Palgrave
Macmillan, 2015.
'
sentences:
- Low-Cost Feline Spay/Neuter The Michigan Humane Society offers low-cost cat and
kitten spay/neuter services for the pets of residents of southeast Michigan. At
an everyday price of just $50 per male cat or kitten, and $65 per female cat or
kitten, a savings of more than $100 from the regular price, the price includes
the procedure, hospitalization, and anesthesia.
- Annual ryegrass is primarily used for pastures and quick cover in erosion control
plantings. In the South, it is used as a winter annual for overseeding warm season
grasses. Annual ryegrass is quite similar to perennial ryegrass except it is an
annual or biennial, depending on climate and/or length or growing season.
- An AA meeting may take one of several forms, but at any meeting you will find
alcoholics talking about what drinking did to their lives and personalities, what
actions they took to help themselves, and how they are living their lives today.
Click here to learn more about AA meetings.
- source_sentence: '# Breda Holmes
Breda Holmes is a former camogie player, winner of the B+I Star of the Year award
in 1987 and seven All Ireland medals in succession between 1984 and 1991, celebrating
the seventh by scoring the match-turning goal from Ann Downey’s sideline ball
against Cork in the 1991 final.
## Career
She captained Carysfort Training College in their 1984 Purcell Cup campaign and
won six All Ireland club medals with St Paul’s camogie club, based in Kilkenny
city.
'
sentences:
- What is Intellectual Property? Intellectual property (IP) refers to creations
of the mind, such as inventions; literary and artistic works; designs; and symbols,
names and images used in commerce. IP is protected in law by, for example, patents,
copyright and trademarks, which enable people to earn recognition or financial
benefit from what they invent or create.
- '# Kieran Djilali
Kieran Stephen Larbi Allen-Djilali (born 1 January 1991), more commonly known
as Kieran Djilali, is an English former footballer who played as a midfielder.
He played in the Football League with Crystal Palace, Chesterfield, AFC Wimbledon
and Portsmouth.
## Early life
Djilali attended Dunraven School in Streatham.
## Club career
### Crystal Palace and loans
Born in Lambeth, London, Djilali came through the academy at Crystal Palace, going
on trial with Manchester United in mid-2007.
Djilali made his Palace debut aged 17, as a substitute in a 2–1 Football League
Cup victory over Hereford United. This was followed quickly by a string of first-team
appearances in which he impressed.
Djilali joined Conference Premier side Crawley Town on a month-long loan on 1
September 2009. He returned from his loan spell early in late September, having
made 5 league appearances.
On 13 November, he moved on loan to League Two side Chesterfield, where he scored
his first career goal in a game against Darlington on 21 November 2009. On 15
December 2009, his loan was extended by a further month.
He returned to Crystal Palace following his loan spell on 12 January 2010, and
scored his first goal for Palace against Doncaster Rovers on 27 February 2010.
He began the following season in Palace''s first team but dropped out as manager
George Burley sought to bring in more experienced players.
In February, he returned to Chesterfield for a second loan spell. On 23 March,
his loan at Chesterfield was extended to 16 April. Djilali scored once in 10 matches
as Chesterfield were promoted to League One at the end of the season.
When his contract at parent club Crystal Palace expired, he opted to leave Selhurst
Park in the summer of 2011 to seek more game time.
### AFC Wimbledon
In July 2011, Djilali played on trial for Scunthorpe United, but ended up signing
for League Two club AFC Wimbledon on 26 August. On 3 September, he made his debut
for the club, against Port Vale. On 10 March 2012, he scored his first goal for
the club, against Dagenham & Redbridge. In May 2012, Djilali was released from
the club as his contract expired.
### Portsmouth
On 16 August 2012, Djilali signed a one-month contract with League One side Portsmouth.
He made his debut in a 1–1 draw with Bournemouth on the opening day of the League
One season, but was released after just two weeks due to Portsmouth''s tight wage
budget, with manager Michael Appleton putting Djilali''s release down to his lack
of fitness.
### Return to AFC Wimbledon
On 16 November 2012, Djilali re-signed for AFC Wimbledon on a short-term deal.
Manager Neal Ardley said of the move: "Kieran has been with us for a month now.
He has trained well and showed a very good attitude. He has the potential to play
at a higher level but first he needs to prove himself with us. With the busy winter
period coming on, we thought we should augment the squad and take the chance to
have a good look at him in competitive action." Djilali was released by AFC Wimbledon
on 31 January 2013.
### Sligo Rovers
In March 2013, Djilali signed a contract with League of Ireland champions Sligo
Rovers. He made his debut on 8 March, against Derry City. On 18 March, he scored
his first goal for Sligo, against Bray Wanderers.
Djilali... North... ELDING
### Limerick
In July 2014, Djilali signed with League of Ireland side Limerick.
### Cork City
On 21 November 2014, Cork City announced the signing of Kieran Djilali from Munster
rivals, Limerick ahead of the 2015 season. The winger made his debut as a substitute
against former club, Sligo Rovers in a 1–1 draw at The Showgrounds. He scored
his first goal for the Rebel Army after coming on late against Bray Wanderers,
scoring the vital winning goal in a dramatic 1–0 victory.
Whilst at Cork, Djilali suffered a knee injury which he never fully recovered
from and led to him leaving full-time football following his departure from the
club.
### Dulwich Hamlet
After leaving Cork City, and following a period out of the game whilst he recovered
from injury, Djilali joined Dulwich Hamlet of the Isthmian League Premier Division
in September 2016, going on to make his debut as a substitute against Grays Athletic
in the Isthmian League Cup on 13 September 2016.
### Three Bridges
After making three substitute appearances in all competitions for Dulwich Hamlet,
Djilali joined Three Bridges of the Isthmian League South Division on 17 October
2016.
## After football
After Djilali left the League of Ireland and full-time football, he took up youth
football coaching. He attained a UEFA B License and worked as a coach at Fulham''s
academy, and also operated his own coaching business.
## Honours
Chesterfield
- Football League Two (1): 2010–11
Sligo Rovers
- FAI Cup (1): 2013
- Setanta Sports Cup (1): 2014
## Statistics
As of 20 July 2013
| Club | Season | League | League | Cup | Cup | League
Cup | League Cup | Other[A] | Other[A] | Total | Total |
| Club | Season | Apps | Goals | Apps | Goals | Apps |
Goals | Apps | Goals | Apps | Goals |
| ------------------- | ------------ | ------ | ------ | ---- | ----- | ----------
| ---------- | -------- | -------- | ----- | ----- |
| Crystal Palace | 2008–09 | 6 | 0 | 0 | 0 | 2 |
0 | – | – | 8 | 0 |
| Crystal Palace | 2009–10 | 8 | 1 | 2 | 0 | 1 |
0 | – | – | 11 | 1 |
| Crystal Palace | 2010–11 | 14 | 0 | 0 | 0 | 2 |
0 | – | – | 16 | 0 |
| Crystal Palace | Total | 28 | 1 | 2 | 0 | 5 |
0 | – | – | 35 | 1 |
| Crawley (loan) | 2009–10 | 5 | 0 | 0 | 0 | 0 |
0 | – | – | 5 | 0 |
| Chesterfield (loan) | 2009–10 | 8 | 1 | 0 | 0 | 0 |
0 | – | – | 8 | 1 |
| Chesterfield (loan) | 2010–11 | 10 | 1 | 0 | 0 | 0 |
0 | – | – | 10 | 1 |
| AFC Wimbledon | 2011–12 | 12 | 1 | 1 | 0 | 0 |
0 | 1 | 0 | 14 | 1 |
| Portsmouth | 2012–13 | 1 | 0 | 0 | 0 | 0 |
0 | – | – | 1 | 0 |
| AFC Wimbledon | 2012–13 | 5 | 0 | 0 | 0 | 0 |
0 | – | – | 5 | 0 |
| Sligo Rovers | 2013 | 17 | 3 | 1 | 0 | 2 |
0 | 3 | 0 | 23 | 3 |
| Career total | Career total | 86 | 7 | 4 | 0 | 7 |
0 | 4 | 0 | 101 | 7 |
A. ^ The "Other" column constitutes appearances (including substitutions) and
goals in either the Football League Trophy, the Setanta Cup and the UEFA Champions
League.
'
- 10 Most Famous Soccer Stadiums in the World. The Camp Nou with its capacity of
99,354 is the largest stadium in Europe and also the fourth largest soccer stadium
in the world. It is situated in Barcelona, Catalonia, Spain, and is the home of
Spanish club Barcelona since 1957.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: mse dev
type: mse-dev
metrics:
- type: negative_mse
value: -77.74003601074219
name: Negative Mse
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5250944624924359
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4523412698412697
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4623987053582962
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.52
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.76
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.78
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333326
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.22
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.122
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.55
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.61
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5456863439791646
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6494444444444444
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.47358422601023775
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.42000000000000004
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.64
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.69
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42000000000000004
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2533333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.29000000000000004
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.51
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.575
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.685
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5353904032358002
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5508928571428571
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.46799146568426697
name: Cosine Map@100
---
# SentenceTransformer
**ModernBERT-small-v2** represents an efficient approach to creating highly efficient and accurate dense vector encoders. It leverages a small ModernBERT architecture, simple MLM training, and distillation from a larger performant model to achieve superior performance at a lower computational cost compared to standard large models.
### Key Features & Training Methodology
This model was created using a specialized four-stage pipeline:
1. **Deep & Narrow Architecture:** Unlike typical small models (e.g., 6 layers), this student model features **12 Transformer layers** but operates within a narrow **384-dimensional embedding space**. This depth allows for complex multi-hop reasoning crucial for high-accuracy retrieval tasks, while the narrow dimension ensures extremely fast encoding and small index sizes.
2. **Guided Initialization (GUIDE):** The model did not start from random weights. It inherited structural and semantic knowledge from a larger teacher model (`answerdotai/ModernBERT-base`) via **Principal Component Analysis (PCA) Projection**. This technique surgically compressed the teacher's 768-dimensional knowledge into the student's 384-dimensional space, providing a massive "head start."
3. **Extensive MLM Pre-training:** Following initialization, the model underwent comprehensive **Masked Language Modeling (MLM)** pre-training on a highly diverse corpus combining:
* Search Data (MS MARCO)
* Academic Texts (Stanford Philosophy)
* General Knowledge (NPR, FineWiki)
4. **Knowledge Distillation (STS Tuning):** The final, critical stage optimized the model for semantic similarity. It was trained to mimic the output embeddings of a powerful Retrieval Teacher (`Alibaba-NLP/gte-modernbert-base`) using **Mean Squared Error (MSE) loss**. This specialized tuning ensures its 384-dimensional vectors excel at similarity and retrieval tasks.
### Training
The final model, **ModernBERT-small-v2**, was trained using a curated combination of four distinct datasets during the **MLM Pre-training** phase to ensure broad general knowledge acquisition before the final distillation tuning.
GitHub: [semantic-search-models/ModernBERT-small-v2](https://github.com/Johnnyboycurtis/semantic-search-models/tree/main/ModernBERT-small-v2)
The following datasets were integrated and processed:
1. **MS MARCO Triplets** (`sentence-transformers/msmarco-msmarco-MiniLM-L6-v3`, "triplet" split)
* *Source Focus:* Query/Document ranking (Search Relevance).
2. **Stanford Encyclopedia of Philosophy Triplets** (`johnnyboycurtis/Philosophical-Triplets-Retrieval`)
* *Source Focus:* Deep, technical, and abstract academic reasoning.
3. **NPR Articles** (`sentence-transformers/npr`)
* *Source Focus:* Modern news, journalistic style, and general current events.
4. **FineWiki (English)** (`HuggingFaceFW/finewiki`, "en" split)
* *Source Focus:* Encyclopedic, factual knowledge spanning a wide range of topics.
* Only used in distillation training; not used in MLM.
***
*(Note: During the final Knowledge Distillation phase, the targets were generated using embeddings from the teacher model (`Alibaba-NLP/gte-modernbert-base`) based on the combined text content of this merged corpus.)*
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- parquet
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
import torch
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("johnnyboycurtis/ModernBERT-small-v2", model_kwargs={"attn_implementation": "flash_attention_2", "dtype": torch.bfloat16}) # or use "sdpa"
# Run inference
sentences = [
'# Breda Holmes\nBreda Holmes is a former camogie player, winner of the B+I Star of the Year award in 1987 and seven All Ireland medals in succession between 1984 and 1991, celebrating the seventh by scoring the match-turning goal from Ann Downey’s sideline ball against Cork in the 1991 final.\n\n## Career\nShe captained Carysfort Training College in their 1984 Purcell Cup campaign and won six All Ireland club medals with St Paul’s camogie club, based in Kilkenny city.\n',
'What is Intellectual Property? Intellectual property (IP) refers to creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce. IP is protected in law by, for example, patents, copyright and trademarks, which enable people to earn recognition or financial benefit from what they invent or create.',
'10 Most Famous Soccer Stadiums in the World. The Camp Nou with its capacity of 99,354 is the largest stadium in Europe and also the fourth largest soccer stadium in the world. It is situated in Barcelona, Catalonia, Spain, and is the home of Spanish club Barcelona since 1957.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.2616, 0.5490],
# [0.2616, 1.0000, 0.3196],
# [0.5490, 0.3196, 1.0000]])
```
## Evaluation
### Metrics
#### Knowledge Distillation
* Dataset: `mse-dev`
* Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-----------|
| **negative_mse** | **-77.74** |
#### Information Retrieval
* Datasets: `NanoMSMARCO` and `NanoHotpotQA`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoHotpotQA |
|:--------------------|:------------|:-------------|
| cosine_accuracy@1 | 0.32 | 0.52 |
| cosine_accuracy@3 | 0.52 | 0.76 |
| cosine_accuracy@5 | 0.6 | 0.78 |
| cosine_accuracy@10 | 0.76 | 0.84 |
| cosine_precision@1 | 0.32 | 0.52 |
| cosine_precision@3 | 0.1733 | 0.3333 |
| cosine_precision@5 | 0.12 | 0.22 |
| cosine_precision@10 | 0.076 | 0.122 |
| cosine_recall@1 | 0.32 | 0.26 |
| cosine_recall@3 | 0.52 | 0.5 |
| cosine_recall@5 | 0.6 | 0.55 |
| cosine_recall@10 | 0.76 | 0.61 |
| **cosine_ndcg@10** | **0.5251** | **0.5457** |
| cosine_mrr@10 | 0.4523 | 0.6494 |
| cosine_map@100 | 0.4624 | 0.4736 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"MSMARCO",
"HotpotQA"
],
"dataset_id": "sentence-transformers/NanoBEIR-en"
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.42 |
| cosine_accuracy@3 | 0.64 |
| cosine_accuracy@5 | 0.69 |
| cosine_accuracy@10 | 0.8 |
| cosine_precision@1 | 0.42 |
| cosine_precision@3 | 0.2533 |
| cosine_precision@5 | 0.17 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.29 |
| cosine_recall@3 | 0.51 |
| cosine_recall@5 | 0.575 |
| cosine_recall@10 | 0.685 |
| **cosine_ndcg@10** | **0.5354** |
| cosine_mrr@10 | 0.5509 |
| cosine_map@100 | 0.468 |
## Training Details
### Training Dataset
#### parquet
* Dataset: parquet
* Size: 3,375,201 training samples
* Columns: text and label
* Approximate statistics based on the first 1000 samples:
| | text | label |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------|
| type | string | list |
| details | - min: 5 tokens
- mean: 280.41 tokens
- max: 1024 tokens
| |
* Samples:
| text | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| # Scientists Link Diamonds To Earth's Quick Cooling
Scientists say they have evidence the Earth was bombarded by meteors about 13,000 years ago, triggering a 1,000-year cold spell. Researchers write in the journal Science that they have found a layer of microscopic diamonds scattered across North America. An abrupt cooling may have caused many large mammals to become extinct. | [4.6171875, 2.515625, 2.439453125, -1.4853515625, -6.328125, ...] |
| # Brad Giffen
Brad Giffen is a retired Canadian news anchor who has worked on television in both Canada and the United States.
Over his broadcasting career he has also worked as a radio personality, disc jockey, VJ, television reporter, television producer and voice-over artist.
## Broadcasting career
Giffen studied at the Poynter Institute for Advanced Journalism Study. In the late 1980s he was a broadcaster on CHUM-FM radio station in Toronto, Ontario, Canada. He previously was John Majhor's successor veejay on CITY-TV's music video program Toronto Rocks. and he hosted the CBC Television battle of the bands competition Rock Wars.
In 1990, Giffen pivoted to news journalism and became a reporter for CFTO's nightly news program World Beat News (later rebranded as CFTO News in early 1998, and CTV News in 2005).
In 1993, Giffen moved to the United States and became co-anchor of the nightly news on the Fox affiliate KSTU, in Salt Lake City, Utah. Giffen left that post in 1995 to accept ... | [-1.693359375, 13.3828125, 4.50390625, 0.41064453125, -2.884765625, ...] |
| # How Trump Won, According To The Exit Polls
Donald Trump will be the next president of the United States. That's remarkable for all sorts of reasons: He has no governmental experience, for example. And many times during his campaign, Trump's words inflamed large swaths of Americans, whether it was his comments from years ago talking about grabbing women's genitals or calling Mexican immigrants in the U.S. illegally "rapists" and playing up crimes committed by immigrants, including drug crimes and murders. But right now, it's also remarkable because almost no one saw it coming. All major forecasters predicted a Hillary Clinton win, whether moderately or by a landslide. So what happened? We don't know just yet why pollsters and forecasters got it wrong, but here's what made this electorate so different from the one that elected Barack Obama by 4 points in 2012. To be clear, it's impossible to break any election results out into fully discrete demographic groups or trends — race, gend... | [3.4296875, 12.828125, 2.8203125, -5.47265625, -5.390625, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0001
- `num_train_epochs`: 2
- `warmup_steps`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
Click to expand
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0.1
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss | mse-dev_negative_mse | NanoMSMARCO_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:-----:|:-------------:|:--------------------:|:--------------------------:|:---------------------------:|:----------------------------:|
| 0.0019 | 100 | 4.2698 | - | - | - | - |
| 0.0038 | 200 | 4.2304 | - | - | - | - |
| 0.0057 | 300 | 4.1280 | - | - | - | - |
| 0.0076 | 400 | 3.8576 | - | - | - | - |
| 0.0095 | 500 | 3.1561 | - | - | - | - |
| 0.0114 | 600 | 2.5527 | - | - | - | - |
| 0.0133 | 700 | 2.3275 | - | - | - | - |
| 0.0152 | 800 | 2.2656 | - | - | - | - |
| 0.0171 | 900 | 2.2401 | - | - | - | - |
| 0.0190 | 1000 | 2.2256 | -221.2144 | 0.0514 | 0.0577 | 0.0545 |
| 0.0209 | 1100 | 2.2140 | - | - | - | - |
| 0.0228 | 1200 | 2.1920 | - | - | - | - |
| 0.0247 | 1300 | 2.1840 | - | - | - | - |
| 0.0265 | 1400 | 2.1662 | - | - | - | - |
| 0.0284 | 1500 | 2.1598 | - | - | - | - |
| 0.0303 | 1600 | 2.1452 | - | - | - | - |
| 0.0322 | 1700 | 2.1226 | - | - | - | - |
| 0.0341 | 1800 | 2.1068 | - | - | - | - |
| 0.0360 | 1900 | 2.0941 | - | - | - | - |
| 0.0379 | 2000 | 2.0796 | -206.8865 | 0.1481 | 0.0672 | 0.1077 |
| 0.0398 | 2100 | 2.0621 | - | - | - | - |
| 0.0417 | 2200 | 2.0545 | - | - | - | - |
| 0.0436 | 2300 | 2.0382 | - | - | - | - |
| 0.0455 | 2400 | 2.0267 | - | - | - | - |
| 0.0474 | 2500 | 2.0167 | - | - | - | - |
| 0.0493 | 2600 | 2.0041 | - | - | - | - |
| 0.0512 | 2700 | 1.9902 | - | - | - | - |
| 0.0531 | 2800 | 1.9746 | - | - | - | - |
| 0.0550 | 2900 | 1.9650 | - | - | - | - |
| 0.0569 | 3000 | 1.9539 | -194.5440 | 0.1243 | 0.1242 | 0.1243 |
| 0.0588 | 3100 | 1.9401 | - | - | - | - |
| 0.0607 | 3200 | 1.9317 | - | - | - | - |
| 0.0626 | 3300 | 1.9181 | - | - | - | - |
| 0.0645 | 3400 | 1.9098 | - | - | - | - |
| 0.0664 | 3500 | 1.8983 | - | - | - | - |
| 0.0683 | 3600 | 1.8924 | - | - | - | - |
| 0.0702 | 3700 | 1.8806 | - | - | - | - |
| 0.0721 | 3800 | 1.8717 | - | - | - | - |
| 0.0740 | 3900 | 1.8591 | - | - | - | - |
| 0.0758 | 4000 | 1.8525 | -184.2026 | 0.1647 | 0.1745 | 0.1696 |
| 0.0777 | 4100 | 1.8416 | - | - | - | - |
| 0.0796 | 4200 | 1.8359 | - | - | - | - |
| 0.0815 | 4300 | 1.8256 | - | - | - | - |
| 0.0834 | 4400 | 1.8131 | - | - | - | - |
| 0.0853 | 4500 | 1.8063 | - | - | - | - |
| 0.0872 | 4600 | 1.7950 | - | - | - | - |
| 0.0891 | 4700 | 1.7846 | - | - | - | - |
| 0.0910 | 4800 | 1.7762 | - | - | - | - |
| 0.0929 | 4900 | 1.7620 | - | - | - | - |
| 0.0948 | 5000 | 1.7605 | -175.1685 | 0.1960 | 0.2024 | 0.1992 |
| 0.0967 | 5100 | 1.7481 | - | - | - | - |
| 0.0986 | 5200 | 1.7419 | - | - | - | - |
| 0.1005 | 5300 | 1.7301 | - | - | - | - |
| 0.1024 | 5400 | 1.7280 | - | - | - | - |
| 0.1043 | 5500 | 1.7131 | - | - | - | - |
| 0.1062 | 5600 | 1.7063 | - | - | - | - |
| 0.1081 | 5700 | 1.6959 | - | - | - | - |
| 0.1100 | 5800 | 1.6884 | - | - | - | - |
| 0.1119 | 5900 | 1.6801 | - | - | - | - |
| 0.1138 | 6000 | 1.6700 | -166.4924 | 0.2493 | 0.2150 | 0.2321 |
| 0.1157 | 6100 | 1.6637 | - | - | - | - |
| 0.1176 | 6200 | 1.6543 | - | - | - | - |
| 0.1195 | 6300 | 1.6451 | - | - | - | - |
| 0.1214 | 6400 | 1.6382 | - | - | - | - |
| 0.1233 | 6500 | 1.6278 | - | - | - | - |
| 0.1251 | 6600 | 1.6235 | - | - | - | - |
| 0.1270 | 6700 | 1.6150 | - | - | - | - |
| 0.1289 | 6800 | 1.6054 | - | - | - | - |
| 0.1308 | 6900 | 1.6007 | - | - | - | - |
| 0.1327 | 7000 | 1.5874 | -158.1013 | 0.2809 | 0.2349 | 0.2579 |
| 0.1346 | 7100 | 1.5824 | - | - | - | - |
| 0.1365 | 7200 | 1.5724 | - | - | - | - |
| 0.1384 | 7300 | 1.5669 | - | - | - | - |
| 0.1403 | 7400 | 1.5535 | - | - | - | - |
| 0.1422 | 7500 | 1.5450 | - | - | - | - |
| 0.1441 | 7600 | 1.5345 | - | - | - | - |
| 0.1460 | 7700 | 1.5340 | - | - | - | - |
| 0.1479 | 7800 | 1.5242 | - | - | - | - |
| 0.1498 | 7900 | 1.5181 | - | - | - | - |
| 0.1517 | 8000 | 1.5086 | -150.1032 | 0.2957 | 0.2454 | 0.2705 |
| 0.1536 | 8100 | 1.5007 | - | - | - | - |
| 0.1555 | 8200 | 1.4950 | - | - | - | - |
| 0.1574 | 8300 | 1.4829 | - | - | - | - |
| 0.1593 | 8400 | 1.4780 | - | - | - | - |
| 0.1612 | 8500 | 1.4737 | - | - | - | - |
| 0.1631 | 8600 | 1.4603 | - | - | - | - |
| 0.1650 | 8700 | 1.4510 | - | - | - | - |
| 0.1669 | 8800 | 1.4500 | - | - | - | - |
| 0.1688 | 8900 | 1.4408 | - | - | - | - |
| 0.1707 | 9000 | 1.4372 | -142.8462 | 0.3033 | 0.2824 | 0.2929 |
| 0.1726 | 9100 | 1.4270 | - | - | - | - |
| 0.1744 | 9200 | 1.4233 | - | - | - | - |
| 0.1763 | 9300 | 1.4135 | - | - | - | - |
| 0.1782 | 9400 | 1.4074 | - | - | - | - |
| 0.1801 | 9500 | 1.3981 | - | - | - | - |
| 0.1820 | 9600 | 1.3919 | - | - | - | - |
| 0.1839 | 9700 | 1.3844 | - | - | - | - |
| 0.1858 | 9800 | 1.3741 | - | - | - | - |
| 0.1877 | 9900 | 1.3685 | - | - | - | - |
| 0.1896 | 10000 | 1.3668 | -135.7081 | 0.3194 | 0.3059 | 0.3127 |
| 0.1915 | 10100 | 1.3568 | - | - | - | - |
| 0.1934 | 10200 | 1.3505 | - | - | - | - |
| 0.1953 | 10300 | 1.3433 | - | - | - | - |
| 0.1972 | 10400 | 1.3338 | - | - | - | - |
| 0.1991 | 10500 | 1.3295 | - | - | - | - |
| 0.2010 | 10600 | 1.3275 | - | - | - | - |
| 0.2029 | 10700 | 1.3149 | - | - | - | - |
| 0.2048 | 10800 | 1.3119 | - | - | - | - |
| 0.2067 | 10900 | 1.3055 | - | - | - | - |
| 0.2086 | 11000 | 1.2952 | -129.2064 | 0.3109 | 0.3434 | 0.3272 |
| 0.2105 | 11100 | 1.2920 | - | - | - | - |
| 0.2124 | 11200 | 1.2851 | - | - | - | - |
| 0.2143 | 11300 | 1.2769 | - | - | - | - |
| 0.2162 | 11400 | 1.2747 | - | - | - | - |
| 0.2181 | 11500 | 1.2686 | - | - | - | - |
| 0.2200 | 11600 | 1.2684 | - | - | - | - |
| 0.2219 | 11700 | 1.2582 | - | - | - | - |
| 0.2237 | 11800 | 1.2582 | - | - | - | - |
| 0.2256 | 11900 | 1.2479 | - | - | - | - |
| 0.2275 | 12000 | 1.2418 | -123.6261 | 0.3439 | 0.3547 | 0.3493 |
| 0.2294 | 12100 | 1.2400 | - | - | - | - |
| 0.2313 | 12200 | 1.2330 | - | - | - | - |
| 0.2332 | 12300 | 1.2288 | - | - | - | - |
| 0.2351 | 12400 | 1.2230 | - | - | - | - |
| 0.2370 | 12500 | 1.2164 | - | - | - | - |
| 0.2389 | 12600 | 1.2157 | - | - | - | - |
| 0.2408 | 12700 | 1.2166 | - | - | - | - |
| 0.2427 | 12800 | 1.2045 | - | - | - | - |
| 0.2446 | 12900 | 1.2035 | - | - | - | - |
| 0.2465 | 13000 | 1.1968 | -118.8691 | 0.3282 | 0.3329 | 0.3306 |
| 0.2484 | 13100 | 1.1942 | - | - | - | - |
| 0.2503 | 13200 | 1.1895 | - | - | - | - |
| 0.2522 | 13300 | 1.1843 | - | - | - | - |
| 0.2541 | 13400 | 1.1755 | - | - | - | - |
| 0.2560 | 13500 | 1.1756 | - | - | - | - |
| 0.2579 | 13600 | 1.1707 | - | - | - | - |
| 0.2598 | 13700 | 1.1637 | - | - | - | - |
| 0.2617 | 13800 | 1.1684 | - | - | - | - |
| 0.2636 | 13900 | 1.1628 | - | - | - | - |
| 0.2655 | 14000 | 1.1585 | -115.4122 | 0.3779 | 0.3579 | 0.3679 |
| 0.2674 | 14100 | 1.1602 | - | - | - | - |
| 0.2693 | 14200 | 1.1504 | - | - | - | - |
| 0.2712 | 14300 | 1.1483 | - | - | - | - |
| 0.2730 | 14400 | 1.1488 | - | - | - | - |
| 0.2749 | 14500 | 1.1392 | - | - | - | - |
| 0.2768 | 14600 | 1.1343 | - | - | - | - |
| 0.2787 | 14700 | 1.1363 | - | - | - | - |
| 0.2806 | 14800 | 1.1342 | - | - | - | - |
| 0.2825 | 14900 | 1.1327 | - | - | - | - |
| 0.2844 | 15000 | 1.1219 | -111.9139 | 0.3794 | 0.3791 | 0.3793 |
| 0.2863 | 15100 | 1.1246 | - | - | - | - |
| 0.2882 | 15200 | 1.1152 | - | - | - | - |
| 0.2901 | 15300 | 1.1196 | - | - | - | - |
| 0.2920 | 15400 | 1.1097 | - | - | - | - |
| 0.2939 | 15500 | 1.1067 | - | - | - | - |
| 0.2958 | 15600 | 1.0994 | - | - | - | - |
| 0.2977 | 15700 | 1.1077 | - | - | - | - |
| 0.2996 | 15800 | 1.1057 | - | - | - | - |
| 0.3015 | 15900 | 1.0949 | - | - | - | - |
| 0.3034 | 16000 | 1.0981 | -109.2994 | 0.3867 | 0.3855 | 0.3861 |
| 0.3053 | 16100 | 1.0933 | - | - | - | - |
| 0.3072 | 16200 | 1.0873 | - | - | - | - |
| 0.3091 | 16300 | 1.0851 | - | - | - | - |
| 0.3110 | 16400 | 1.0840 | - | - | - | - |
| 0.3129 | 16500 | 1.0831 | - | - | - | - |
| 0.3148 | 16600 | 1.0755 | - | - | - | - |
| 0.3167 | 16700 | 1.0733 | - | - | - | - |
| 0.3186 | 16800 | 1.0724 | - | - | - | - |
| 0.3205 | 16900 | 1.0698 | - | - | - | - |
| 0.3223 | 17000 | 1.0710 | -106.3769 | 0.4092 | 0.4066 | 0.4079 |
| 0.3242 | 17100 | 1.0699 | - | - | - | - |
| 0.3261 | 17200 | 1.0642 | - | - | - | - |
| 0.3280 | 17300 | 1.0576 | - | - | - | - |
| 0.3299 | 17400 | 1.0597 | - | - | - | - |
| 0.3318 | 17500 | 1.0572 | - | - | - | - |
| 0.3337 | 17600 | 1.0547 | - | - | - | - |
| 0.3356 | 17700 | 1.0502 | - | - | - | - |
| 0.3375 | 17800 | 1.0467 | - | - | - | - |
| 0.3394 | 17900 | 1.0485 | - | - | - | - |
| 0.3413 | 18000 | 1.0455 | -103.7698 | 0.4510 | 0.4237 | 0.4374 |
| 0.3432 | 18100 | 1.0433 | - | - | - | - |
| 0.3451 | 18200 | 1.0404 | - | - | - | - |
| 0.3470 | 18300 | 1.0397 | - | - | - | - |
| 0.3489 | 18400 | 1.0352 | - | - | - | - |
| 0.3508 | 18500 | 1.0318 | - | - | - | - |
| 0.3527 | 18600 | 1.0302 | - | - | - | - |
| 0.3546 | 18700 | 1.0330 | - | - | - | - |
| 0.3565 | 18800 | 1.0220 | - | - | - | - |
| 0.3584 | 18900 | 1.0223 | - | - | - | - |
| 0.3603 | 19000 | 1.0254 | -101.5743 | 0.4439 | 0.4265 | 0.4352 |
| 0.3622 | 19100 | 1.0186 | - | - | - | - |
| 0.3641 | 19200 | 1.0216 | - | - | - | - |
| 0.3660 | 19300 | 1.0152 | - | - | - | - |
| 0.3679 | 19400 | 1.0139 | - | - | - | - |
| 0.3698 | 19500 | 1.0125 | - | - | - | - |
| 0.3716 | 19600 | 1.0087 | - | - | - | - |
| 0.3735 | 19700 | 1.0045 | - | - | - | - |
| 0.3754 | 19800 | 1.0032 | - | - | - | - |
| 0.3773 | 19900 | 1.0013 | - | - | - | - |
| 0.3792 | 20000 | 1.0017 | -99.6613 | 0.4554 | 0.4374 | 0.4464 |
| 0.3811 | 20100 | 1.0007 | - | - | - | - |
| 0.3830 | 20200 | 0.9959 | - | - | - | - |
| 0.3849 | 20300 | 0.9965 | - | - | - | - |
| 0.3868 | 20400 | 0.9909 | - | - | - | - |
| 0.3887 | 20500 | 0.9902 | - | - | - | - |
| 0.3906 | 20600 | 0.9903 | - | - | - | - |
| 0.3925 | 20700 | 0.9927 | - | - | - | - |
| 0.3944 | 20800 | 0.9865 | - | - | - | - |
| 0.3963 | 20900 | 0.9843 | - | - | - | - |
| 0.3982 | 21000 | 0.9809 | -97.4922 | 0.4689 | 0.4462 | 0.4575 |
| 0.4001 | 21100 | 0.9801 | - | - | - | - |
| 0.4020 | 21200 | 0.9785 | - | - | - | - |
| 0.4039 | 21300 | 0.9718 | - | - | - | - |
| 0.4058 | 21400 | 0.9725 | - | - | - | - |
| 0.4077 | 21500 | 0.9705 | - | - | - | - |
| 0.4096 | 21600 | 0.9729 | - | - | - | - |
| 0.4115 | 21700 | 0.9714 | - | - | - | - |
| 0.4134 | 21800 | 0.9647 | - | - | - | - |
| 0.4153 | 21900 | 0.9623 | - | - | - | - |
| 0.4172 | 22000 | 0.9579 | -95.7813 | 0.4642 | 0.4549 | 0.4595 |
| 0.4191 | 22100 | 0.9553 | - | - | - | - |
| 0.4209 | 22200 | 0.9558 | - | - | - | - |
| 0.4228 | 22300 | 0.9584 | - | - | - | - |
| 0.4247 | 22400 | 0.9544 | - | - | - | - |
| 0.4266 | 22500 | 0.9520 | - | - | - | - |
| 0.4285 | 22600 | 0.9516 | - | - | - | - |
| 0.4304 | 22700 | 0.9543 | - | - | - | - |
| 0.4323 | 22800 | 0.9502 | - | - | - | - |
| 0.4342 | 22900 | 0.9477 | - | - | - | - |
| 0.4361 | 23000 | 0.9405 | -93.9238 | 0.4856 | 0.4521 | 0.4688 |
| 0.4380 | 23100 | 0.9448 | - | - | - | - |
| 0.4399 | 23200 | 0.9424 | - | - | - | - |
| 0.4418 | 23300 | 0.9369 | - | - | - | - |
| 0.4437 | 23400 | 0.9318 | - | - | - | - |
| 0.4456 | 23500 | 0.9342 | - | - | - | - |
| 0.4475 | 23600 | 0.9392 | - | - | - | - |
| 0.4494 | 23700 | 0.9358 | - | - | - | - |
| 0.4513 | 23800 | 0.9303 | - | - | - | - |
| 0.4532 | 23900 | 0.9306 | - | - | - | - |
| 0.4551 | 24000 | 0.9277 | -92.2427 | 0.4946 | 0.4798 | 0.4872 |
| 0.4570 | 24100 | 0.9267 | - | - | - | - |
| 0.4589 | 24200 | 0.9228 | - | - | - | - |
| 0.4608 | 24300 | 0.9239 | - | - | - | - |
| 0.4627 | 24400 | 0.9225 | - | - | - | - |
| 0.4646 | 24500 | 0.9169 | - | - | - | - |
| 0.4665 | 24600 | 0.9170 | - | - | - | - |
| 0.4684 | 24700 | 0.9195 | - | - | - | - |
| 0.4702 | 24800 | 0.9153 | - | - | - | - |
| 0.4721 | 24900 | 0.9138 | - | - | - | - |
| 0.4740 | 25000 | 0.9108 | -90.7635 | 0.4622 | 0.4812 | 0.4717 |
| 0.4759 | 25100 | 0.9133 | - | - | - | - |
| 0.4778 | 25200 | 0.9076 | - | - | - | - |
| 0.4797 | 25300 | 0.9081 | - | - | - | - |
| 0.4816 | 25400 | 0.9093 | - | - | - | - |
| 0.4835 | 25500 | 0.9037 | - | - | - | - |
| 0.4854 | 25600 | 0.9025 | - | - | - | - |
| 0.4873 | 25700 | 0.9058 | - | - | - | - |
| 0.4892 | 25800 | 0.9018 | - | - | - | - |
| 0.4911 | 25900 | 0.9014 | - | - | - | - |
| 0.4930 | 26000 | 0.8946 | -89.2562 | 0.4745 | 0.4957 | 0.4851 |
| 0.4949 | 26100 | 0.8982 | - | - | - | - |
| 0.4968 | 26200 | 0.8946 | - | - | - | - |
| 0.4987 | 26300 | 0.8941 | - | - | - | - |
| 0.5006 | 26400 | 0.8925 | - | - | - | - |
| 0.5025 | 26500 | 0.8947 | - | - | - | - |
| 0.5044 | 26600 | 0.8906 | - | - | - | - |
| 0.5063 | 26700 | 0.8895 | - | - | - | - |
| 0.5082 | 26800 | 0.8866 | - | - | - | - |
| 0.5101 | 26900 | 0.8840 | - | - | - | - |
| 0.5120 | 27000 | 0.8764 | -87.8039 | 0.5011 | 0.5173 | 0.5092 |
| 0.5139 | 27100 | 0.8859 | - | - | - | - |
| 0.5158 | 27200 | 0.8839 | - | - | - | - |
| 0.5177 | 27300 | 0.8794 | - | - | - | - |
| 0.5195 | 27400 | 0.8790 | - | - | - | - |
| 0.5214 | 27500 | 0.8788 | - | - | - | - |
| 0.5233 | 27600 | 0.8780 | - | - | - | - |
| 0.5252 | 27700 | 0.8749 | - | - | - | - |
| 0.5271 | 27800 | 0.8742 | - | - | - | - |
| 0.5290 | 27900 | 0.8700 | - | - | - | - |
| 0.5309 | 28000 | 0.8691 | -86.4419 | 0.4936 | 0.4776 | 0.4856 |
| 0.5328 | 28100 | 0.8747 | - | - | - | - |
| 0.5347 | 28200 | 0.8644 | - | - | - | - |
| 0.5366 | 28300 | 0.8673 | - | - | - | - |
| 0.5385 | 28400 | 0.8670 | - | - | - | - |
| 0.5404 | 28500 | 0.8638 | - | - | - | - |
| 0.5423 | 28600 | 0.8649 | - | - | - | - |
| 0.5442 | 28700 | 0.8629 | - | - | - | - |
| 0.5461 | 28800 | 0.8629 | - | - | - | - |
| 0.5480 | 28900 | 0.8591 | - | - | - | - |
| 0.5499 | 29000 | 0.8566 | -85.0408 | 0.4792 | 0.4918 | 0.4855 |
| 0.5518 | 29100 | 0.8588 | - | - | - | - |
| 0.5537 | 29200 | 0.8545 | - | - | - | - |
| 0.5556 | 29300 | 0.8534 | - | - | - | - |
| 0.5575 | 29400 | 0.8543 | - | - | - | - |
| 0.5594 | 29500 | 0.8534 | - | - | - | - |
| 0.5613 | 29600 | 0.8519 | - | - | - | - |
| 0.5632 | 29700 | 0.8486 | - | - | - | - |
| 0.5651 | 29800 | 0.8530 | - | - | - | - |
| 0.5670 | 29900 | 0.8477 | - | - | - | - |
| 0.5688 | 30000 | 0.8465 | -83.9435 | 0.4986 | 0.5097 | 0.5042 |
| 0.5707 | 30100 | 0.8425 | - | - | - | - |
| 0.5726 | 30200 | 0.8437 | - | - | - | - |
| 0.5745 | 30300 | 0.8430 | - | - | - | - |
| 0.5764 | 30400 | 0.8431 | - | - | - | - |
| 0.5783 | 30500 | 0.8424 | - | - | - | - |
| 0.5802 | 30600 | 0.8403 | - | - | - | - |
| 0.5821 | 30700 | 0.8347 | - | - | - | - |
| 0.5840 | 30800 | 0.8344 | - | - | - | - |
| 0.5859 | 30900 | 0.8348 | - | - | - | - |
| 0.5878 | 31000 | 0.8351 | -82.8113 | 0.4999 | 0.5088 | 0.5043 |
| 0.5897 | 31100 | 0.8362 | - | - | - | - |
| 0.5916 | 31200 | 0.8307 | - | - | - | - |
| 0.5935 | 31300 | 0.8315 | - | - | - | - |
| 0.5954 | 31400 | 0.8311 | - | - | - | - |
| 0.5973 | 31500 | 0.8305 | - | - | - | - |
| 0.5992 | 31600 | 0.8304 | - | - | - | - |
| 0.6011 | 31700 | 0.8277 | - | - | - | - |
| 0.6030 | 31800 | 0.8249 | - | - | - | - |
| 0.6049 | 31900 | 0.8262 | - | - | - | - |
| 0.6068 | 32000 | 0.8236 | -81.7389 | 0.4811 | 0.5256 | 0.5034 |
| 0.6087 | 32100 | 0.8209 | - | - | - | - |
| 0.6106 | 32200 | 0.8226 | - | - | - | - |
| 0.6125 | 32300 | 0.8207 | - | - | - | - |
| 0.6144 | 32400 | 0.8224 | - | - | - | - |
| 0.6163 | 32500 | 0.8163 | - | - | - | - |
| 0.6182 | 32600 | 0.8181 | - | - | - | - |
| 0.6200 | 32700 | 0.8147 | - | - | - | - |
| 0.6219 | 32800 | 0.8170 | - | - | - | - |
| 0.6238 | 32900 | 0.8156 | - | - | - | - |
| 0.6257 | 33000 | 0.8141 | -80.4979 | 0.5042 | 0.5085 | 0.5064 |
| 0.6276 | 33100 | 0.8088 | - | - | - | - |
| 0.6295 | 33200 | 0.8098 | - | - | - | - |
| 0.6314 | 33300 | 0.8133 | - | - | - | - |
| 0.6333 | 33400 | 0.8087 | - | - | - | - |
| 0.6352 | 33500 | 0.8086 | - | - | - | - |
| 0.6371 | 33600 | 0.8094 | - | - | - | - |
| 0.6390 | 33700 | 0.8054 | - | - | - | - |
| 0.6409 | 33800 | 0.8043 | - | - | - | - |
| 0.6428 | 33900 | 0.8035 | - | - | - | - |
| 0.6447 | 34000 | 0.7990 | -79.5726 | 0.4990 | 0.5166 | 0.5078 |
| 0.6466 | 34100 | 0.8035 | - | - | - | - |
| 0.6485 | 34200 | 0.7990 | - | - | - | - |
| 0.6504 | 34300 | 0.7996 | - | - | - | - |
| 0.6523 | 34400 | 0.8005 | - | - | - | - |
| 0.6542 | 34500 | 0.8000 | - | - | - | - |
| 0.6561 | 34600 | 0.7975 | - | - | - | - |
| 0.6580 | 34700 | 0.7959 | - | - | - | - |
| 0.6599 | 34800 | 0.7921 | - | - | - | - |
| 0.6618 | 34900 | 0.7916 | - | - | - | - |
| 0.6637 | 35000 | 0.7933 | -78.7884 | 0.5104 | 0.5139 | 0.5122 |
| 0.6656 | 35100 | 0.7908 | - | - | - | - |
| 0.6675 | 35200 | 0.7913 | - | - | - | - |
| 0.6693 | 35300 | 0.7921 | - | - | - | - |
| 0.6712 | 35400 | 0.7929 | - | - | - | - |
| 0.6731 | 35500 | 0.7915 | - | - | - | - |
| 0.6750 | 35600 | 0.7871 | - | - | - | - |
| 0.6769 | 35700 | 0.7836 | - | - | - | - |
| 0.6788 | 35800 | 0.7805 | - | - | - | - |
| 0.6807 | 35900 | 0.7870 | - | - | - | - |
| 0.6826 | 36000 | 0.7797 | -77.7400 | 0.5251 | 0.5457 | 0.5354 |