|
--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:183 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: '14 William O. Douglas, quoted in Charles Hurd, Film Booking Issue |
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Ordered Reopened,” New York Times, May 4, 1948, 1. 15 Movie Crisis Laid to Video |
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Inroads And Dwindling of Foreign Market, New York Times, February 27, 1949, F1. |
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For details on the lawsuit and its effects, see Arthur De Vany and Henry McMillan, |
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Was the Antitrust Action that Broke Up the Movie Studios Good for the Movies? |
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Evidence from the Stock Market. American Law and Economics Review 6, no. 1 (2004): |
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135-53; and J.C. Strick, The Economics of the Motion Picture Industry: A Survey, |
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Philosophy of the Social Sciences 8, no. 4 (December 1978): 406-17. 16 The Hollywood |
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feature films for which Eisler provided music are Hangmen Also Die (1942), None |
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But the Lonely Heart (1944), Jealousy (1945), The Spanish Main (1945); A Scandal |
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in Paris (1946), Deadline at Dawn (1946), Woman on the Beach (1947), and So Well |
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Remembered (1947). Most of these are middle-of-the-road genre pieces, but the |
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first NOTES 267' |
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sentences: |
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- What is the opinion of Ernest Irving, a pioneer of British film music, on the |
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overall quality of American film music? |
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- What is the title of the 2007 film directed by David Fincher, produced by Michael |
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Medavoy, and featuring a storyline based on a real-life serial killer, as mentioned |
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in the provided context information? |
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- What was the primary reason behind the lawsuit that led to the breakup of the |
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movie studios, as suggested by the article in the New York Times on February 27, |
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1949? |
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- source_sentence: 'But Gorbman (who like Flinn and Kalinak approached film music |
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from a formal background not in musicology but in literary criticism) was certainly |
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not the first scholar engaged in so-called film studies44 to address the role |
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that extra-diegetic music played in classical-style films. Two years before Gorbman''s |
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book was published, the trio of Bordwell, Staiger, and Thompson brought out their |
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monumental The Classical Hollywood Cinema: Film Style and Production to 1960. |
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As noted above, and apropos of its title, the book focuses on filmic narrative |
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style and the technical devices that made this style possible. In its early pages, |
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however, it also contains insightful comments on classical cinema''s use of music. |
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The book''s first music-related passage lays a foundation for Gorbman''s point |
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about how a score might lend unity to a film by recycling distinctive themes that |
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within the THE GOLDEN AGE OF FILM MUSIC, 1933-49 143' |
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sentences: |
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- What is the possible reason, as suggested by David Thomson, for why David Lean's |
|
filmmaking style may have declined after the movie "Summer Madness" (US, 1955)? |
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- What shift in the portrayal of hard body male characters in film, as exemplified |
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by the actors who played these roles in the 1980s and 1990s, suggests that societal |
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expectations and norms may be changing? |
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- What is the significance of the authors' formal background in literary criticism |
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rather than musicology, as mentioned in the context of Gorbman's approach to film |
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music? |
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- source_sentence: (1931); Georg Wilhelm Pabst's Kameradschaft (1931); Fritz Lang's |
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M (1931) and Das Testament der Dr. Mabuse (1932); and Carl Theodor Dreyer's Vampyr |
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(1932). These films’ subtle mix of actual silence with accompanying music and |
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more or less realistic sound effects has drawn and doubtless will continue to |
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draw serious analytical attention from film scholars.45 And even in their own |
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time they drew due attention aplenty from critics of avant-garde persuasion.46 |
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The mere fact that these films differed from the sonic norm attracted the notice, |
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if not always the praise, of movie reviewers for the popular press. Writing from |
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London, a special correspondent for the New York Times observed that Hitchcock's |
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Blackmail goes some way to showing how the cinematograph and the microphone can |
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be mated without their union being forced upon the attention of a punctilious |
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world as VITAPHONE AND MOVIETONE, 1926-8 101 |
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sentences: |
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- What was the primary limitation that led to the failure of Edison's first Kinetophone, |
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which was an early attempt at sound film featuring musical accompaniment? |
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- What was the specific sonic approach employed by the mentioned films of Georg |
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Wilhelm Pabst, Fritz Lang, and Carl Theodor Dreyer that drew serious analytical |
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attention from film scholars? |
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- What limitation in Martin Scorsese's background, as mentioned in the text, restricted |
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his choice of subjects at this stage in his career? |
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- source_sentence: "39\tdivided into small, three-dimensional cubes known as volumetric\ |
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\ pixels, or voxels. When viewers are watching certain images, the voxel demonstrates\ |
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\ how these images in the movie are mapped into brain activity. Clips of the movie\ |
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\ are reconstructed through brain imaging and computer stimulation by associating\ |
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\ visual patterns in the movie with the corresponding brain activity. However,\ |
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\ these reconstructions are blurry and are hard to make because researchers say,\ |
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\ blood flow signals measured using fMRI change much more slowly than the neural\ |
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\ signals that encode dynamic information in movies. Psychology and neuroscience\ |
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\ professor, Jack Gallant explains in an interview that primary visual cortex\ |
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\ responds to the local features of the movie such as edges, colors, motion, and\ |
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\ texture but this part of the brain cannot understand the objects in the movie.\ |
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\ In addition, movies that show people are reconstructed with better accuracy\ |
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\ than abstract images. Using Neuroimaging For Entertainment Success Can brain\ |
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\ scans predict movie success in the box office? Two marketing researchers from\ |
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\ the Rotterdam School of Management devised an experiment by using EEG on participants.\ |
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\ EEG demonstrated that individual choice and box office success correlate with\ |
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\ different types of brain activity. From article, How Neuroimaging Can Save The\ |
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\ Entertainment Industry Millions of Dollars, it states, individual choice is\ |
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\ predicted best by high frontocentral beta activity, the choice of the general\ |
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\ population is predicted by frontal gamma activity. Perhaps, with quickly advanced\ |
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\ technology, predicting movie genre and plots that can hit the box office could\ |
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\ be successful. Neurocinema in Hollywood One strategy that helps filmmakers,\ |
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\ producers, and distributors to achieve global market success is by using fMRI\ |
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\ and EEG to make a better storyline, characters, sound effects, and other" |
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sentences: |
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- What significant change in the portrayal of Rocky's character is evident in the |
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2015 movie Creed, as compared to the original 1976 film Rocky? |
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- What factors led to the selection of the films "Spider-man" (2002), "Cars" (2006), |
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and "Avatar" (2009) for the research project examining the relationship between |
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film and society in the early 2000s? |
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- What is the main reason why researchers find it challenging to reconstruct abstract |
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images from movie clips using brain imaging and computer stimulation? |
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- source_sentence: "11\tdocumentary film so unpleasant when most had sat through horror\ |
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\ pictures that were appreciably more violent and bloody. The answer that McCauley\ |
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\ came up with was that the fictional nature of horror films affords viewers a\ |
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\ sense of control by placing psychological distance between them and the violent\ |
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\ acts they have witnessed. Most people who view horror movies understand that\ |
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\ the filmed events are unreal, which furnishes them with psychological distance\ |
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\ from the horror portrayed in the film. In fact, there is evidence that young\ |
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\ viewers who perceive greater realism in horror films are more negatively affected\ |
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\ by their exposure to horror films than viewers who perceive the film as unreal\ |
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\ (Hoekstra, Harris, & Helmick, 1999). Four Viewing Motivations for Graphic Horror\ |
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\ According to Dr. Deirdre Johnston (1995) study Adolescents’ Motivations for\ |
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\ Viewing Graphic Horror of Human Communication Research there are four different\ |
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\ main reasons for viewing graphic horror. From the study of a small sample of\ |
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\ 220 American adolescents who like watching horror movies, Dr. Johnston reported\ |
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\ that: The four viewing motivations are found to be related to viewers’ cognitive\ |
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\ and affective responses to horror films, as well as viewers’ tendency to identify\ |
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\ with either the killers or victims in these films.\" Dr. Johnson notes that:\ |
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\ 1) gore watchers typically had low empathy, high sensation seeking, and (among\ |
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\ males only) a strong identification with the killer, 2) thrill watchers typically\ |
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\ had both high empathy and sensation seeking, identified themselves more with\ |
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\ the victims, and liked the suspense of the film, 3) independent watchers typically\ |
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\ had a high empathy for the victim along with a high positive effect for overcoming\ |
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\ fear, and 4) problem watchers typically had high empathy for the victim but\ |
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\ were" |
|
sentences: |
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- What was the name of the series published by Oliver Ditson from 1918-25 that contained |
|
ensemble music for motion picture plays? |
|
- What shift in the cultural, political, and social contexts of the 1980s and 1990s |
|
may have led to the deconstruction of the hard body characters portrayed by actors |
|
such as Stallone and Schwarzenegger in more recent movies? |
|
- What is the primary reason why viewers who perceive greater realism in horror |
|
films are more negatively affected by their exposure to horror films than viewers |
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who perceive the film as unreal? |
|
datasets: |
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- YxBxRyXJx/QAsimple_for_BGE_241019 |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: BGE base Movie Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
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name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8205128205128205 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9743589743589743 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8205128205128205 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.32478632478632485 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8205128205128205 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9743589743589743 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9207838928594967 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8940170940170941 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8940170940170938 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9230769230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.30769230769230776 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9230769230769231 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9233350110390831 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8982905982905982 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8982905982905982 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9230769230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9487179487179487 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.30769230769230776 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18974358974358976 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000002 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8461538461538461 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9230769230769231 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9487179487179487 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9234104189545929 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.898962148962149 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.898962148962149 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7692307692307693 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8974358974358975 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9487179487179487 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9487179487179487 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7692307692307693 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29914529914529925 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18974358974358976 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09487179487179488 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7692307692307693 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8974358974358975 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9487179487179487 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9487179487179487 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8688480033444261 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8418803418803418 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8443986568986569 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5641025641025641 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8717948717948718 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9230769230769231 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9487179487179487 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5641025641025641 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2905982905982907 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18461538461538465 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09487179487179488 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5641025641025641 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8717948717948718 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9230769230769231 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9487179487179487 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.768187565996018 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.708119658119658 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7088711597999523 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Movie Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [q_asimple_for_bge_241019](https://huggingface.co/datasets/YxBxRyXJx/QAsimple_for_BGE_241019) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- [q_asimple_for_bge_241019](https://huggingface.co/datasets/YxBxRyXJx/QAsimple_for_BGE_241019) |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("YxBxRyXJx/bge-base-movie-matryoshka") |
|
# Run inference |
|
sentences = [ |
|
'11\tdocumentary film so unpleasant when most had sat through horror pictures that were appreciably more violent and bloody. The answer that McCauley came up with was that the fictional nature of horror films affords viewers a sense of control by placing psychological distance between them and the violent acts they have witnessed. Most people who view horror movies understand that the filmed events are unreal, which furnishes them with psychological distance from the horror portrayed in the film. In fact, there is evidence that young viewers who perceive greater realism in horror films are more negatively affected by their exposure to horror films than viewers who perceive the film as unreal (Hoekstra, Harris, & Helmick, 1999). Four Viewing Motivations for Graphic Horror According to Dr. Deirdre Johnston (1995) study Adolescents’ Motivations for Viewing Graphic Horror of Human Communication Research there are four different main reasons for viewing graphic horror. From the study of a small sample of 220 American adolescents who like watching horror movies, Dr. Johnston reported that: The four viewing motivations are found to be related to viewers’ cognitive and affective responses to horror films, as well as viewers’ tendency to identify with either the killers or victims in these films." Dr. Johnson notes that: 1) gore watchers typically had low empathy, high sensation seeking, and (among males only) a strong identification with the killer, 2) thrill watchers typically had both high empathy and sensation seeking, identified themselves more with the victims, and liked the suspense of the film, 3) independent watchers typically had a high empathy for the victim along with a high positive effect for overcoming fear, and 4) problem watchers typically had high empathy for the victim but were', |
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'What is the primary reason why viewers who perceive greater realism in horror films are more negatively affected by their exposure to horror films than viewers who perceive the film as unreal?', |
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'What shift in the cultural, political, and social contexts of the 1980s and 1990s may have led to the deconstruction of the hard body characters portrayed by actors such as Stallone and Schwarzenegger in more recent movies?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.8205 | 0.8462 | 0.8462 | 0.7692 | 0.5641 | |
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| cosine_accuracy@3 | 0.9744 | 0.9231 | 0.9231 | 0.8974 | 0.8718 | |
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| cosine_accuracy@5 | 1.0 | 1.0 | 0.9487 | 0.9487 | 0.9231 | |
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| cosine_accuracy@10 | 1.0 | 1.0 | 1.0 | 0.9487 | 0.9487 | |
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| cosine_precision@1 | 0.8205 | 0.8462 | 0.8462 | 0.7692 | 0.5641 | |
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| cosine_precision@3 | 0.3248 | 0.3077 | 0.3077 | 0.2991 | 0.2906 | |
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| cosine_precision@5 | 0.2 | 0.2 | 0.1897 | 0.1897 | 0.1846 | |
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| cosine_precision@10 | 0.1 | 0.1 | 0.1 | 0.0949 | 0.0949 | |
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| cosine_recall@1 | 0.8205 | 0.8462 | 0.8462 | 0.7692 | 0.5641 | |
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| cosine_recall@3 | 0.9744 | 0.9231 | 0.9231 | 0.8974 | 0.8718 | |
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| cosine_recall@5 | 1.0 | 1.0 | 0.9487 | 0.9487 | 0.9231 | |
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| cosine_recall@10 | 1.0 | 1.0 | 1.0 | 0.9487 | 0.9487 | |
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| **cosine_ndcg@10** | **0.9208** | **0.9233** | **0.9234** | **0.8688** | **0.7682** | |
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| cosine_mrr@10 | 0.894 | 0.8983 | 0.899 | 0.8419 | 0.7081 | |
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| cosine_map@100 | 0.894 | 0.8983 | 0.899 | 0.8444 | 0.7089 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### q_asimple_for_bge_241019 |
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* Dataset: [q_asimple_for_bge_241019](https://huggingface.co/datasets/YxBxRyXJx/QAsimple_for_BGE_241019) at [66635cd](https://huggingface.co/datasets/YxBxRyXJx/QAsimple_for_BGE_241019/tree/66635cde6ada74a8cf5a84db10518119fc1c221d) |
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* Size: 183 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 183 samples: |
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| | positive | anchor | |
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|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 191 tokens</li><li>mean: 356.1 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 36.04 tokens</li><li>max: 66 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>1 Introduction Why do we watch horror films? What makes horror films so exciting to watch? Why do our bodies sweat and muscles tense when we are scared? How do filmmakers, producers, sound engineers, and cinematographers specifically design a horror film? Can horror movies cause negative, lasting effects on the audience? These are some of the questions that are answered by exploring the aesthetics of horror films and the psychology behind horror movies. Chapter 1, The Allure of Horror Film, illustrates why we are drawn to scary films by studying different psychological theories and factors. Ideas include: catharsis, subconscious mind, curiosity, thrill, escape from reality, relevance, unrealism, and imagination. Also, this chapter demonstrates why people would rather watch fiction films than documentaries and the motivations for viewing graphic horror. Chapter 2, Mise-en-scène in Horror Movies, includes purposeful arrangement of scenery and stage properties of horror movie. Also...</code> | <code>What is the name of the emerging field of scientists and filmmakers that uses fMRI and EEG to read people's brain activity while watching movie scenes?</code> | |
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| <code>3 Chapter 1: The Allure of Horror Film Overview Although watching horror films can make us feel anxious and uneasy, we still continue to watch other horror films one after another. It is ironic how we hate the feeling of being scared, but we still enjoy the thrill. So why do we pay money to watch something to be scared? Eight Theories on why we watch Horror Films From research by philosophers, psychoanalysts, and psychologists there are theories that can explain why we are drawn to watching horror films. The first theory, psychoanalyst, Sigmund Freud portrays that horror comes from the “uncanny” emergence of images and thoughts of the primitive id. The purpose of horror films is to highlight unconscious fears, desire, urges, and primeval archetypes that are buried deep in our collective subconscious images of mothers and shadows play important roles because they are common to us all. For example, in Alfred Hitchcock's Psycho, a mother plays the role of evil in the main character...</code> | <code>What process, introduced by the Greek Philosopher Aristotle, involves the release of negative emotions through the observation of violent or scary events, resulting in a purging of aggressive emotions?</code> | |
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| <code>5 principle unknowable (Jancovich, 2002, p. 35). This meaning, the audience already knows that the plot and the characters are already disgusting, but the surprises in the horror narrative through the discovery of curiosity should give satisfaction. Marvin Zuckerman (1979) proposed that people who scored high in sensation seeking scale often reported a greater interest in exciting things like rollercoasters, bungee jumping and horror films. He argued more individuals who are attracted to horror movies desire the sensation of experience. However, researchers did not find the correlation to thrill-seeking activities and enjoyment of watching horror films always significant. The Gender Socialization theory (1986) by Zillman, Weaver, Mundorf and Aust exposed 36 male and 36 female undergraduates to a horror movie with the same age, opposite-gender companion of low or high initial appeal who expressed mastery, affective indifference, or distress. They reported that young men enjoyed the fi...</code> | <code>What is the proposed theory by Marvin Zuckerman (1979) regarding the relationship between sensation seeking and interest in exciting activities, including horror films?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
|
### Training Logs |
|
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 1.0 | 1 | 0.8987 | 0.8983 | 0.8835 | 0.8419 | 0.7773 | |
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| 2.0 | 2 | 0.9218 | 0.9141 | 0.9075 | 0.8721 | 0.8124 | |
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| 1.0 | 1 | 0.9218 | 0.9141 | 0.9075 | 0.8721 | 0.8124 | |
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| 2.0 | 2 | 0.9356 | 0.9302 | 0.9118 | 0.8750 | 0.8057 | |
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| **3.0** | **4** | **0.9302** | **0.9233** | **0.9234** | **0.8783** | **0.7759** | |
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| 4.0 | 5 | 0.9208 | 0.9233 | 0.9234 | 0.8688 | 0.7682 | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
|
### Framework Versions |
|
- Python: 3.10.12 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.46.3 |
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- PyTorch: 2.5.1+cu121 |
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- Accelerate: 1.1.1 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.20.3 |
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|
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## Citation |
|
|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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