Model: YxBxRyXJx/bge-base-movie-matryoshka
Author: YxBxRyXJx
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:183, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, dataset:YxBxRyXJx/QAsimple_for_BGE_241019, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: language: en
license: apache-2.0
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:183, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget: source_sentence: 14 William O. Douglas, quoted in Charles Hurd, Film Booking Issue Ordered Reopened,” New York Times, May 4, 1948, 1. 15 Movie Crisis Laid to Video Inroads And Dwindling of Foreign Market, New York Times, February 27, 1949, F1. For details on the lawsuit and its effects, see Arthur De Vany and Henry McMillan, Was the Antitrust Action that Broke Up the Movie Studios Good for the Movies? Evidence from the Stock Market. American Law and Economics Review 6, no. 1 (2004): 135-53; and J.C. Strick, The Economics of the Motion Picture Industry: A Survey, Philosophy of the Social Sciences 8, no. 4 (December 1978): 406-17. 16 The Hollywood feature films for which Eisler provided music are Hangmen Also Die (1942), None But the Lonely Heart (1944), Jealousy (1945), The Spanish Main (1945); A Scandal in Paris (1946), Deadline at Dawn (1946), Woman on the Beach (1947), and So Well Remembered (1947). Most of these are middle-of-the-road genre pieces, but the first NOTES 267
sentences: What is the opinion of Ernest Irving, a pioneer of British film music, on the overall quality of American film music?, What is the title of the 2007 film directed by David Fincher, produced by Michael Medavoy, and featuring a storyline based on a real-life serial killer, as mentioned in the provided context information?, What was the primary reason behind the lawsuit that led to the breakup of the movie studios, as suggested by the article in the New York Times on February 27, 1949?, source_sentence: But Gorbman (who like Flinn and Kalinak approached film music from a formal background not in musicology but in literary criticism) was certainly not the first scholar engaged in so-called film studies44 to address the role that extra-diegetic music played in classical-style films. Two years before Gorbman's book was published, the trio of Bordwell, Staiger, and Thompson brought out their monumental The Classical Hollywood Cinema: Film Style and Production to 1960. As noted above, and apropos of its title, the book focuses on filmic narrative style and the technical devices that made this style possible. In its early pages, however, it also contains insightful comments on classical cinema's use of music. The book's first music-related passage lays a foundation for Gorbman's point about how a score might lend unity to a film by recycling distinctive themes that within the THE GOLDEN AGE OF FILM MUSIC, 1933-49 143
sentences: 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)?, What shift in the portrayal of hard body male characters in film, as exemplified by the actors who played these roles in the 1980s and 1990s, suggests that societal expectations and norms may be changing?, What is the significance of the authors' formal background in literary criticism rather than musicology, as mentioned in the context of Gorbman's approach to film music?, source_sentence: (1931); Georg Wilhelm Pabst's Kameradschaft (1931); Fritz Lang's M (1931) and Das Testament der Dr. Mabuse (1932); and Carl Theodor Dreyer's Vampyr (1932). These films’ subtle mix of actual silence with accompanying music and more or less realistic sound effects has drawn and doubtless will continue to draw serious analytical attention from film scholars.45 And even in their own time they drew due attention aplenty from critics of avant-garde persuasion.46 The mere fact that these films differed from the sonic norm attracted the notice, if not always the praise, of movie reviewers for the popular press. Writing from London, a special correspondent for the New York Times observed that Hitchcock's Blackmail goes some way to showing how the cinematograph and the microphone can be mated without their union being forced upon the attention of a punctilious world as VITAPHONE AND MOVIETONE, 1926-8 101
sentences: What was the primary limitation that led to the failure of Edison's first Kinetophone, which was an early attempt at sound film featuring musical accompaniment?, What was the specific sonic approach employed by the mentioned films of Georg Wilhelm Pabst, Fritz Lang, and Carl Theodor Dreyer that drew serious analytical attention from film scholars?, What limitation in Martin Scorsese's background, as mentioned in the text, restricted his choice of subjects at this stage in his career?, source_sentence: 39	divided into small, three-dimensional cubes known as volumetric pixels, or voxels. When viewers are watching certain images, the voxel demonstrates how these images in the movie are mapped into brain activity. Clips of the movie are reconstructed through brain imaging and computer stimulation by associating visual patterns in the movie with the corresponding brain activity. However, these reconstructions are blurry and are hard to make because researchers say, blood flow signals measured using fMRI change much more slowly than the neural signals that encode dynamic information in movies. Psychology and neuroscience professor, Jack Gallant explains in an interview that primary visual cortex responds to the local features of the movie such as edges, colors, motion, and texture but this part of the brain cannot understand the objects in the movie. In addition, movies that show people are reconstructed with better accuracy than abstract images. Using Neuroimaging For Entertainment Success Can brain scans predict movie success in the box office? Two marketing researchers from the Rotterdam School of Management devised an experiment by using EEG on participants. EEG demonstrated that individual choice and box office success correlate with different types of brain activity. From article, How Neuroimaging Can Save The Entertainment Industry Millions of Dollars, it states, individual choice is predicted best by high frontocentral beta activity, the choice of the general population is predicted by frontal gamma activity. Perhaps, with quickly advanced technology, predicting movie genre and plots that can hit the box office could be successful. Neurocinema in Hollywood One strategy that helps filmmakers, producers, and distributors to achieve global market success is by using fMRI and EEG to make a better storyline, characters, sound effects, and other
sentences: What significant change in the portrayal of Rocky's character is evident in the 2015 movie Creed, as compared to the original 1976 film Rocky?, What factors led to the selection of the films "Spider-man" (2002), "Cars" (2006), and "Avatar" (2009) for the research project examining the relationship between film and society in the early 2000s?, What is the main reason why researchers find it challenging to reconstruct abstract images from movie clips using brain imaging and computer stimulation?, source_sentence: 11	documentary 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
sentences: 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 who perceive the film as unreal?
datasets: YxBxRyXJx/QAsimple_for_BGE_241019
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: 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: BGE base Movie Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.8205128205128205
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9743589743589743
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8205128205128205
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.32478632478632485
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8205128205128205
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9743589743589743
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9207838928594967
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8940170940170941
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8940170940170938
name: Cosine Map@100
verified: no, 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
verified: no, type: cosine_accuracy@3
value: 0.9230769230769231
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8461538461538461
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.30769230769230776
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8461538461538461
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9230769230769231
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9233350110390831
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8982905982905982
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8982905982905982
name: Cosine Map@100
verified: no, 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
verified: no, type: cosine_accuracy@3
value: 0.9230769230769231
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9487179487179487
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8461538461538461
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.30769230769230776
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18974358974358976
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8461538461538461
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9230769230769231
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9487179487179487
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9234104189545929
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.898962148962149
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.898962148962149
name: Cosine Map@100
verified: no, 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
verified: no, type: cosine_accuracy@3
value: 0.8974358974358975
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9487179487179487
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9487179487179487
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.7692307692307693
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.29914529914529925
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18974358974358976
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09487179487179488
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.7692307692307693
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8974358974358975
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9487179487179487
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9487179487179487
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8688480033444261
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8418803418803418
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8443986568986569
name: Cosine Map@100
verified: no, 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
verified: no, type: cosine_accuracy@3
value: 0.8717948717948718
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9230769230769231
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9487179487179487
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.5641025641025641
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2905982905982907
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18461538461538465
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09487179487179488
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.5641025641025641
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8717948717948718
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9230769230769231
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9487179487179487
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.768187565996018
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.708119658119658
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.7088711597999523
name: Cosine Map@100
verified: no
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Created: 2024-11-21 04:19:05