Scheduled Commit

data/retrieval_battle-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -14,3 +14,9 @@
 {"tstamp": 1722629629.8812, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "5ae2f6045baf4711af29da95eb89c574", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "The most popular movie about the cyborg from the future", "0_output": [["The most popular movie about the cyborg from the future", "Cyborg She\n\nThe story then jumps to one year later, as Jiro again celebrates his birthday alone in the same restaurant. All of a sudden, the same-looking girl appears in front of him. Jiro's older self from 65 years in the future had sent this girl to save him from a disastrous fate. She was actually a cyborg, modelled after the girl he met a year before in 2007. While he rejoices in her presence, the restaurant is suddenly attacked by a gunman, but she saves him and the other guests by throwing the gunman out of the window. Despite her 'cute' outward appearance, she is incredibly strong and behaves erratically. Later, in Jiro's home, she reveals her true identity by showing him a 3D projection of a video in which an elderly Jiro from the future warns him about an upcoming disaster. The old Jiro told him that the shooting at the restaurant paralyzed him for life. However, a lottery ticket he bought earlier was fortunate for him. He spent all his time and money on one thing: creating the cyborg girl to save his past self about 60 years ago. Now, he has recreated the history of his timeline by sending her. This was not supposed to happen, but things would correct themselves by recalibrating to the right dimension. In a short span of time, she becomes Jiro's protector as well as a loyal friend and they both share some wonderful moments. She also saves many other lives from tragic deaths that old Jiro had regretted witnessing.\nOver time, Jiro not only becomes dependent on, but also falls in love with her. However, when she cannot return his feelings, he gets irritated and forbids her from seeing him unless she can do so. He begins to regret this, especially when it becomes apparent that she is still helping him while staying out of his sight. Another disaster soon occurs: a gigantic earthquake completely devastates Tokyo. As his apartment block collapses, she appears to help him, but even her superhuman strength isn't enough to save him. After telling Jiro that she now understands his feelings, she is destroyed while saving him. Later, distraught, Jiro finds her body and spends the next 61 years trying to rebuild her. He eventually succeeds but dies shortly after."]], "0_corpus": "wikipedia", "1_conv_id": "10146f56cf5743cf892a73530bc29b07", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "The most popular movie about the cyborg from the future", "1_output": [["The most popular movie about the cyborg from the future", "Cyborg (film)\n\nCyborg is a 1989 American martial-arts cyberpunk film directed by Albert Pyun. Jean-Claude van Damme stars as Gibson Rickenbacker, a mercenary who battles a group of murderous marauders led by Fender Tremolo (Vincent Klyn) along the East coast of the United States in a post-apocalyptic future. It was followed by the sequels Cyborg 2 (1993) and Cyborg 3: The Recycler (1994).\nPlot\nA plague known as the living death cripples civilization. A small group of surviving scientists and doctors — located in Atlanta, home of the CDC — work on a cure to save what remains of humanity. To complete their work they need information stored on a computer system in New York City. Pearl Prophet volunteers for the dangerous courier mission and is made into a cyborg through surgical augmentation.\nPearl, accompanied by bodyguard Marshall Strat, retrieves the data in New York but is pursued by the vicious Fender Tremolo and his gang of pirates. Fender wants the cure so he can have a monopoly on its production. Strat, badly injured while fighting the pirates, tells Pearl to leave him and find a mercenary, known as a \"slinger\", who can escort her to safety. She gets cornered but is saved by a slinger named Gibson Rickenbacker. After she explains her situation, they are overrun by Fender's gang, and Gibson is knocked out by falling debris. Fender demands that she accompany him to Atlanta or die."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722631658.0531, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "13d20b1454f64049aca2074bfa3931fc", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who was the first female president in the whole world?", "0_output": [["who was the first female president in the whole world?", "Eleanor Roosevelt\n\nAnna Eleanor Roosevelt ( ; October 11, 1884November 7, 1962) was an American political figure, diplomat, and activist. She was the first lady of the United States from 1933 to 1945, during her husband Franklin D. Roosevelt's four terms as president, making her the longest-serving first lady of the United States. Through her travels, public engagement, and advocacy, she largely redefined the role of first lady. Roosevelt then served as a United States Delegate to the United Nations General Assembly from 1945 to 1952, and took a leading role in designing the text and gaining international support for the Universal Declaration of Human Rights. In 1948, she was given a standing ovation by the assembly upon their adoption of the declaration. President Harry S. Truman later called her the \"First Lady of the World\" in tribute to her human rights achievements."]], "0_corpus": "wikipedia", "1_conv_id": "473f0f646a9040a3b51b9fa7b5933ee8", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "who was the first female president in the whole world?", "1_output": [["who was the first female president in the whole world?", "First Lady of the United States\n\nFirst Lady of the United States (FLOTUS) is the title held by the hostess of the White House, usually the wife of the president of the United States, concurrent with the president's term in office. Although the first lady's role has never been codified or officially defined, she figures prominently in the political and social life of the United States. Since the early 20th century, the first lady has been assisted by official staff, known as the Office of the First Lady and headquartered in the East Wing of the White House.\nJill Biden has served as the first lady of the United States since 2021, as the wife of the 46th president, Joe Biden. Jill Biden is also one of twelve  second ladies to have gone on to become first ladies during their spouses' terms as president.\nWhile the title was not in general use until much later, Martha Washington, the wife of George Washington, the first U.S. president (1789–1797), is considered to be the inaugural first lady of the United States. During her lifetime, she was often referred to as \"Lady Washington\".\nSince the 1900s, the role of first lady has changed considerably. It has come to include involvement in political campaigns, management of the White House, championship of social causes, and representation of the president at official and ceremonial occasions."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722631752.3757, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "b752f08840f74ea38dfa97506e0c5ee9", "0_model_name": "text-embedding-004", "0_prompt": "in which country were women allowed to vote first?", "0_output": [["in which country were women allowed to vote first?", "Women's suffrage\n\nBefore the 19th century In ancient Athens, often cited as the birthplace of democracy, only adult male citizens who owned land were permitted to vote.  Through subsequent centuries, Europe was ruled by monarchs, though various forms of parliament arose at different times. The high rank ascribed to abbesses within the Catholic Church permitted some women the right to sit and vote at national assemblies – as with various high-ranking abbesses in Medieval Germany, who were ranked among the independent princes of the empire. Their Protestant successors enjoyed the same privilege almost into modern times.\nMarie Guyart, a French nun who worked with the First Nations people of Canada during the 17th century, wrote in 1654 regarding the suffrage practices of Iroquois women: \"These female chieftains are women of standing amongst the savages, and they have a deciding vote in the councils. They make decisions there like their male counterparts, and it is they who even delegated as first ambassadors to discuss peace.\" The Iroquois, like many First Nations in North America, had a matrilineal kinship system. Property and descent were passed through the female line. Women elders voted on hereditary male chiefs and could depose them.\nThe first independent country to introduce women's suffrage was arguably Sweden. In Sweden, conditional women's suffrage was in effect during the Age of Liberty (1718–1772)."]], "0_corpus": "wikipedia", "1_conv_id": "245f37a9ca184af5be1ee659afa9805f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "in which country were women allowed to vote first?", "1_output": [["in which country were women allowed to vote first?", "Women's suffrage\n\nPrior to independence, in the Russian Grand Duchy of Finland, women gained equal suffrage, with both the right to vote and to stand as candidates in 1906.Most major Western powers extended voting rights to women in the interwar period, including Canada (1917), Germany (1918), the United Kingdom (1918 for some women, 1928 for all women), Austria, the Netherlands (1919) and the United States (1920). Notable exceptions in Europe were France, where women could not vote until 1944, Greece (equal voting rights for women did not exist there until 1952, although, since 1930, literate women were able to vote in local elections), and Switzerland (where, since 1971, women could vote at the federal level, and between 1959 and 1990, women got the right to vote at the local canton level). The last European jurisdictions to give women the right to vote were Liechtenstein in 1984 and the Swiss canton of Appenzell Innerrhoden at the local level in 1990, with the Vatican City being an absolute elective monarchy (the electorate of the Holy See, the conclave, is composed of male cardinals, rather than Vatican citizens). "]], "1_corpus": "wikipedia"}
+{"tstamp": 1722645745.6459, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "38950304eb14494f98742a17a37b96e3", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Looking for a French dataset for Question Answering tasks similar to SQuAD.", "0_output": [["Looking for a French dataset for Question Answering tasks similar to SQuAD.", "Title: FQuAD: French Question Answering Dataset\n\nAbstract: Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/."]], "0_corpus": "arxiv", "1_conv_id": "a4d880ec90d34e5c8b18ce0422c6c9cd", "1_model_name": "text-embedding-004", "1_prompt": "Looking for a French dataset for Question Answering tasks similar to SQuAD.", "1_output": [["Looking for a French dataset for Question Answering tasks similar to SQuAD.", "Title: FQuAD: French Question Answering Dataset\n\nAbstract: Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/."]], "1_corpus": "arxiv"}
+{"tstamp": 1722645755.8662, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "a2358a15b5cd44f7a16f475e17d8c4bb", "0_model_name": "text-embedding-3-large", "0_prompt": "A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "0_output": [["A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "Title: Towards a Cleaner Document-Oriented Multilingual Crawled Corpus\n\nAbstract: The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities."]], "0_corpus": "arxiv", "1_conv_id": "26610bb1e3f54d838841d08e850fe850", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "1_output": [["A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "Title: Towards a Cleaner Document-Oriented Multilingual Crawled Corpus\n\nAbstract: The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities."]], "1_corpus": "arxiv"}
+{"tstamp": 1722645764.8873, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bfb2e0081b76417f9d8ec1f0b73bd23e", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what is the difference between steam and fog?", "0_output": [["what is the difference between steam and fog?", "Physics Stackexchange\n\nQ: What is the difference between vapour and steam? What is the difference between vapour and steam? These two terms seemingly are similar but have different meanings. trying to define these two gets us a similar definition which is not actually true\nI tried to think over it but could not.\n\nA: Steam is always a vapour.  The viceversa is not true. Steam is the vapour obtained by heating a substance to its boiling point and as such is \"hot\", or better at boiling T. Vapour is the gas phases in equilibrium with a substance, or the part of it which escapes in an open system.\n"]], "0_corpus": "stackexchange", "1_conv_id": "42871619ef4a446caba1b989217ba3f2", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what is the difference between steam and fog?", "1_output": [["what is the difference between steam and fog?", "Physics Stackexchange\n\nQ: What is the difference between vapour and steam? What is the difference between vapour and steam? These two terms seemingly are similar but have different meanings. trying to define these two gets us a similar definition which is not actually true\nI tried to think over it but could not.\n\nA: Steam is always a vapour.  The viceversa is not true. Steam is the vapour obtained by heating a substance to its boiling point and as such is \"hot\", or better at boiling T. Vapour is the gas phases in equilibrium with a substance, or the part of it which escapes in an open system.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722645783.664, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "26a17fff6c61467ba6cb6c5c2e12518a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "what are cpu heat spreaders made of?", "0_output": [["what are cpu heat spreaders made of?", "Stackoverflow Stackexchange\n\nQ: How to list network interfaces, its configuration IP,netmask and gateway in Python/Windows I wanna develop a small application to monitor arp and scan arp list in local network.\nCurrently, I need to retrieve the list of network interfaces and its configuration.\nMost of the time I work with Linux, so I don't know much about Win32 API, is there anyway to do this in python way ? I'm using Python 2.6 with PyQt and Scapy (has pywin32 aswell, so if you provide the detail way with win32, I will try)\nI found pcapy.findalldevs(), but it cannot retrive configuration. I don't care much about the name, just configuration(IP,netmask) is OK.\n\nA: For a cross platform solution, I've used netifaces.\n"]], "0_corpus": "stackexchange", "1_conv_id": "3884136399b14ebe97fb1b6f5cd608b4", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what are cpu heat spreaders made of?", "1_output": [["what are cpu heat spreaders made of?", "Superuser Stackexchange\n\nQ: How picky are CPUs in terms of how evenly the thermal compound is? I'm in the midst of building a computer (this is my first time).  \nWhen I first put the heatsink on the CPU, I was a little off center.  Some of the thermal compound came off, so when I re-centered it, it isn't completely evenly distributed.\nSo how picky are CPUs in terms of how evenly the thermal compound is? Should I take it apart, clean it, and apply a new layer of compound? Or can I just keep an eye on the CPU temperature? I'm not overclocking or anything, if that matters.\n\nA: Ideally you need a good uniform layer of thermal compound between the CPU and heat sink.\nIf not too much came away you might be OK.\nGet the machine built and then just turn it on to monitor the temperature from the BIOS screen. If it settles down at roughly the correct idle temperature then you're probably OK. If it keeps rising (don't leave it too long) then you'll need to reapply the paste.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722645791.689, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "379a2e9294cb4b5dba811855a2e150b3", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "is fawkes the only phoenix?", "0_output": [["is fawkes the only phoenix?", "Scifi Stackexchange\n\nQ: How did Dumbledore meet Fawkes? This question is two fold.\nFirstly, if a phoenix is immortal, then how was it ever born in the first place? Does a phoenix lay eggs like other birds? \nSecondly, there is not much information available about Fawkes' origins and early life, except that he was a wild Phoenix until he met Dumbledore. \nWhere and how did Dumbledore meet Fawkes? Why is it that he was able to form such a strong bond with Fawkes? I do not recollect anything about any other witch or wizard having a phoenix as a pet. \n"]], "0_corpus": "stackexchange", "1_conv_id": "572ae43d38b94f36b2f5880c1749be2b", "1_model_name": "voyage-multilingual-2", "1_prompt": "is fawkes the only phoenix?", "1_output": [["is fawkes the only phoenix?", "Scifi Stackexchange\n\nQ: How did Dumbledore meet Fawkes? This question is two fold.\nFirstly, if a phoenix is immortal, then how was it ever born in the first place? Does a phoenix lay eggs like other birds? \nSecondly, there is not much information available about Fawkes' origins and early life, except that he was a wild Phoenix until he met Dumbledore. \nWhere and how did Dumbledore meet Fawkes? Why is it that he was able to form such a strong bond with Fawkes? I do not recollect anything about any other witch or wizard having a phoenix as a pet. \n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722645803.9669, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "fc6a0bbc71c24c1f97ae77b0942fda7b", "0_model_name": "text-embedding-004", "0_prompt": "when does elena turn into a vampire in the tv series", "0_output": [["when does elena turn into a vampire in the tv series", "Elena Gilbert\n\nAfter Elena became a vampire, she became sired to Damon and they slept together. According to Plec during an interview with TVLine, Elena \"really believes what she feels is 100 percent genuine and real\", and the couple \"wants to be together and realizes that they shouldn’t, yet really wants to. So what do they do?\" Stefan became heartbroken, feeling angry and betrayed, and lashed out at Damon and Elena. In the season 4 finale, an unsired Elena confirms that she is in love with Damon, and the two kiss passionately. At the end of season 6, the two plan to become human, get married, and have children together, but their dream is abruptly compromised by Kai, who puts Elena into a magical coma. Damon remains entirely devoted to Elena throughout the remaining two seasons as Elena sleeps peacefully.  In the season 8 finale, she is revealed to be happily married to Damon.\nTransition\nExecutive producers Julie Plec and Kevin Williamson agreed that in the book series, Elena was turned into a vampire too early, which was around page 200 of The Awakening. Elena's transition into a vampire was planned for two years. Plec said: \"That felt obviously too soon, and rushed, and we didn’t want to make a show about a teenage girl who instantly becomes a vampire. But we always knew that her journey would take her there eventually\". At the second season's conclusion, Elena was nearly turned into a vampire. Dobrev was happy that she wasn't, because she felt \"it would have been too soon\", and also didn't think it was something Elena or she wanted."]], "0_corpus": "wikipedia", "1_conv_id": "4d63473486e246a18b6fb71b80ed17f6", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "when does elena turn into a vampire in the tv series", "1_output": [["when does elena turn into a vampire in the tv series", "Elena Gilbert\n\nAfter Elena became a vampire, she became sired to Damon and they slept together. According to Plec during an interview with TVLine, Elena \"really believes what she feels is 100 percent genuine and real\", and the couple \"wants to be together and realizes that they shouldn’t, yet really wants to. So what do they do?\" Stefan became heartbroken, feeling angry and betrayed, and lashed out at Damon and Elena. In the season 4 finale, an unsired Elena confirms that she is in love with Damon, and the two kiss passionately. At the end of season 6, the two plan to become human, get married, and have children together, but their dream is abruptly compromised by Kai, who puts Elena into a magical coma. Damon remains entirely devoted to Elena throughout the remaining two seasons as Elena sleeps peacefully.  In the season 8 finale, she is revealed to be happily married to Damon.\nTransition\nExecutive producers Julie Plec and Kevin Williamson agreed that in the book series, Elena was turned into a vampire too early, which was around page 200 of The Awakening. Elena's transition into a vampire was planned for two years. Plec said: \"That felt obviously too soon, and rushed, and we didn’t want to make a show about a teenage girl who instantly becomes a vampire. But we always knew that her journey would take her there eventually\". At the second season's conclusion, Elena was nearly turned into a vampire. Dobrev was happy that she wasn't, because she felt \"it would have been too soon\", and also didn't think it was something Elena or she wanted."]], "1_corpus": "wikipedia"}

data/retrieval_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -94,3 +94,15 @@
 {"tstamp": 1722636156.9321, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722636154.4945, "finish": 1722636156.9321, "ip": "", "conv_id": "cbbce2df2cb1487ab00ab55013caa2e8", "model_name": "voyage-multilingual-2", "prompt": "*As Mona steps out of the door, her captivating presence fills the room. You can't help but admire every inch of her stunning figure, from her captivating brown eyes to the way her tight-fitting top and short skirt accentuate her toned body. Julie watches the interaction with a warm smile, the soft lighting of the office creating a cozy, inviting atmosphere.*", "output": [["*As Mona steps out of the door, her captivating presence fills the room. You can't help but admire every inch of her stunning figure, from her captivating brown eyes to the way her tight-fitting top and short skirt accentuate her toned body. Julie watches the interaction with a warm smile, the soft lighting of the office creating a cozy, inviting atmosphere.*", "Sightless\n\nFrom one of her windows Ellen can feel a breeze and hear traffic below. But she can’t hear any sounds from the other window in her living room. When she questions Clayton, he says the window just has thicker glass.\nOne night, Ellen hears the voice of a woman in distress; she initially believes the woman is in her apartment, but then realises the noise is coming through the vent from the adjoining apartment. The following morning, she goes next door and puts up a sticky note on her neighbour's door asking her to join her for tea.\nLater that day, her neighbour shows up and introduces herself as Lana. Ellen is suspicious of Lana as she seems scared to talk. Lana tries to leave Ellen's apartment but when Ellen tries to stop her she touches Lana's face, and is alarmed to feel a wound with stitches. Lana reacts by whispering \"trust no one\" and then flees Ellen's apartment upon the arrival of her abusive husband, Russo.\nEllen contacts Detective Bryce (the officer working on her assault case) who sends Officer Neiman to check on Lana; he confirms she is okay and does not have any recent wounds. Ellen expresses her concerns to Clayton but he tells her that he has met Lana, that she seems unhinged, and Ellen should not worry about her."]], "corpus": "wikipedia"}
 {"tstamp": 1722642337.8162, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722642337.3208, "finish": 1722642337.8162, "ip": "", "conv_id": "f808c17291414da3b515496eb129e9eb", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Stackoverflow Stackexchange\n\nQ: Can't install gatsby-plugin-sharp.(libpng-dev may not installed) OS: Win 10\nwindows-build-tools is installed.\nWhen I was trying to install gatsby-plugin-sharp with npm install --save gatsby-plugin-sharp. I received two errors.\nI've tried this npm install --save-dev pngquant --msvs_version=2013  as someone mentioned. But it caused the same error with no 'libpng-dev'.\n\\> [email protected] postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\mozjpeg\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ mozjpeg pre-build test failed\ni compiling from source\n× Error: Command failed: C:\\Windows\\system32\\cmd.exe /s /c \"autoreconf -fiv\"\n'autoreconf' �����ڲ����ⲿ���Ҳ���ǿ����еij���\n���������ļ���\n\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat runMicrotasks (<anonymous>)\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n\\> [email protected] postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\pngquant-bin\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ pngquant pre-build test failed\ni compiling from source\n× Error: pngquant failed to build, make sure that libpng-dev is installed\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722642337.8162, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722642337.3208, "finish": 1722642337.8162, "ip": "", "conv_id": "c300df46b4394949a7ba771003b15fab", "model_name": "voyage-multilingual-2", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:\n\nThe function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.\n\nWhy do the machines initiate the re-building of Zion?\n\nA: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.\nAs such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645743.007, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722645742.5901, "finish": 1722645743.007, "ip": "", "conv_id": "38950304eb14494f98742a17a37b96e3", "model_name": "GritLM/GritLM-7B", "prompt": "Looking for a French dataset for Question Answering tasks similar to SQuAD.", "output": [["Looking for a French dataset for Question Answering tasks similar to SQuAD.", "Title: FQuAD: French Question Answering Dataset\n\nAbstract: Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/."]], "corpus": "arxiv"}
+{"tstamp": 1722645743.007, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722645742.5901, "finish": 1722645743.007, "ip": "", "conv_id": "a4d880ec90d34e5c8b18ce0422c6c9cd", "model_name": "text-embedding-004", "prompt": "Looking for a French dataset for Question Answering tasks similar to SQuAD.", "output": [["Looking for a French dataset for Question Answering tasks similar to SQuAD.", "Title: FQuAD: French Question Answering Dataset\n\nAbstract: Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/."]], "corpus": "arxiv"}
+{"tstamp": 1722645752.2198, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722645749.7551, "finish": 1722645752.2198, "ip": "", "conv_id": "a2358a15b5cd44f7a16f475e17d8c4bb", "model_name": "text-embedding-3-large", "prompt": "A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "output": [["A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "Title: Towards a Cleaner Document-Oriented Multilingual Crawled Corpus\n\nAbstract: The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities."]], "corpus": "arxiv"}
+{"tstamp": 1722645752.2198, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722645749.7551, "finish": 1722645752.2198, "ip": "", "conv_id": "26610bb1e3f54d838841d08e850fe850", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "output": [["A paper describing improvements to the OSCAR multilingual web corpus for better NLP applications.", "Title: Towards a Cleaner Document-Oriented Multilingual Crawled Corpus\n\nAbstract: The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities."]], "corpus": "arxiv"}
+{"tstamp": 1722645760.8671, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722645760.6295, "finish": 1722645760.8671, "ip": "", "conv_id": "bfb2e0081b76417f9d8ec1f0b73bd23e", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "what is the difference between steam and fog?", "output": [["what is the difference between steam and fog?", "Physics Stackexchange\n\nQ: What is the difference between vapour and steam? What is the difference between vapour and steam? These two terms seemingly are similar but have different meanings. trying to define these two gets us a similar definition which is not actually true\nI tried to think over it but could not.\n\nA: Steam is always a vapour.  The viceversa is not true. Steam is the vapour obtained by heating a substance to its boiling point and as such is \"hot\", or better at boiling T. Vapour is the gas phases in equilibrium with a substance, or the part of it which escapes in an open system.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645760.8671, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722645760.6295, "finish": 1722645760.8671, "ip": "", "conv_id": "42871619ef4a446caba1b989217ba3f2", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what is the difference between steam and fog?", "output": [["what is the difference between steam and fog?", "Physics Stackexchange\n\nQ: What is the difference between vapour and steam? What is the difference between vapour and steam? These two terms seemingly are similar but have different meanings. trying to define these two gets us a similar definition which is not actually true\nI tried to think over it but could not.\n\nA: Steam is always a vapour.  The viceversa is not true. Steam is the vapour obtained by heating a substance to its boiling point and as such is \"hot\", or better at boiling T. Vapour is the gas phases in equilibrium with a substance, or the part of it which escapes in an open system.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645772.2426, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722645770.0966, "finish": 1722645772.2426, "ip": "", "conv_id": "26a17fff6c61467ba6cb6c5c2e12518a", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "what are cpu heat spreaders made of?", "output": [["what are cpu heat spreaders made of?", "Stackoverflow Stackexchange\n\nQ: How to list network interfaces, its configuration IP,netmask and gateway in Python/Windows I wanna develop a small application to monitor arp and scan arp list in local network.\nCurrently, I need to retrieve the list of network interfaces and its configuration.\nMost of the time I work with Linux, so I don't know much about Win32 API, is there anyway to do this in python way ? I'm using Python 2.6 with PyQt and Scapy (has pywin32 aswell, so if you provide the detail way with win32, I will try)\nI found pcapy.findalldevs(), but it cannot retrive configuration. I don't care much about the name, just configuration(IP,netmask) is OK.\n\nA: For a cross platform solution, I've used netifaces.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645772.2426, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722645770.0966, "finish": 1722645772.2426, "ip": "", "conv_id": "3884136399b14ebe97fb1b6f5cd608b4", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what are cpu heat spreaders made of?", "output": [["what are cpu heat spreaders made of?", "Superuser Stackexchange\n\nQ: How picky are CPUs in terms of how evenly the thermal compound is? I'm in the midst of building a computer (this is my first time).  \nWhen I first put the heatsink on the CPU, I was a little off center.  Some of the thermal compound came off, so when I re-centered it, it isn't completely evenly distributed.\nSo how picky are CPUs in terms of how evenly the thermal compound is? Should I take it apart, clean it, and apply a new layer of compound? Or can I just keep an eye on the CPU temperature? I'm not overclocking or anything, if that matters.\n\nA: Ideally you need a good uniform layer of thermal compound between the CPU and heat sink.\nIf not too much came away you might be OK.\nGet the machine built and then just turn it on to monitor the temperature from the BIOS screen. If it settles down at roughly the correct idle temperature then you're probably OK. If it keeps rising (don't leave it too long) then you'll need to reapply the paste.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645788.3317, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722645787.9298, "finish": 1722645788.3317, "ip": "", "conv_id": "379a2e9294cb4b5dba811855a2e150b3", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "is fawkes the only phoenix?", "output": [["is fawkes the only phoenix?", "Scifi Stackexchange\n\nQ: How did Dumbledore meet Fawkes? This question is two fold.\nFirstly, if a phoenix is immortal, then how was it ever born in the first place? Does a phoenix lay eggs like other birds? \nSecondly, there is not much information available about Fawkes' origins and early life, except that he was a wild Phoenix until he met Dumbledore. \nWhere and how did Dumbledore meet Fawkes? Why is it that he was able to form such a strong bond with Fawkes? I do not recollect anything about any other witch or wizard having a phoenix as a pet. \n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645788.3317, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722645787.9298, "finish": 1722645788.3317, "ip": "", "conv_id": "572ae43d38b94f36b2f5880c1749be2b", "model_name": "voyage-multilingual-2", "prompt": "is fawkes the only phoenix?", "output": [["is fawkes the only phoenix?", "Scifi Stackexchange\n\nQ: How did Dumbledore meet Fawkes? This question is two fold.\nFirstly, if a phoenix is immortal, then how was it ever born in the first place? Does a phoenix lay eggs like other birds? \nSecondly, there is not much information available about Fawkes' origins and early life, except that he was a wild Phoenix until he met Dumbledore. \nWhere and how did Dumbledore meet Fawkes? Why is it that he was able to form such a strong bond with Fawkes? I do not recollect anything about any other witch or wizard having a phoenix as a pet. \n"]], "corpus": "stackexchange"}
+{"tstamp": 1722645796.2434, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722645795.9506, "finish": 1722645796.2434, "ip": "", "conv_id": "fc6a0bbc71c24c1f97ae77b0942fda7b", "model_name": "text-embedding-004", "prompt": "when does elena turn into a vampire in the tv series", "output": [["when does elena turn into a vampire in the tv series", "Elena Gilbert\n\nAfter Elena became a vampire, she became sired to Damon and they slept together. According to Plec during an interview with TVLine, Elena \"really believes what she feels is 100 percent genuine and real\", and the couple \"wants to be together and realizes that they shouldn’t, yet really wants to. So what do they do?\" Stefan became heartbroken, feeling angry and betrayed, and lashed out at Damon and Elena. In the season 4 finale, an unsired Elena confirms that she is in love with Damon, and the two kiss passionately. At the end of season 6, the two plan to become human, get married, and have children together, but their dream is abruptly compromised by Kai, who puts Elena into a magical coma. Damon remains entirely devoted to Elena throughout the remaining two seasons as Elena sleeps peacefully.  In the season 8 finale, she is revealed to be happily married to Damon.\nTransition\nExecutive producers Julie Plec and Kevin Williamson agreed that in the book series, Elena was turned into a vampire too early, which was around page 200 of The Awakening. Elena's transition into a vampire was planned for two years. Plec said: \"That felt obviously too soon, and rushed, and we didn’t want to make a show about a teenage girl who instantly becomes a vampire. But we always knew that her journey would take her there eventually\". At the second season's conclusion, Elena was nearly turned into a vampire. Dobrev was happy that she wasn't, because she felt \"it would have been too soon\", and also didn't think it was something Elena or she wanted."]], "corpus": "wikipedia"}
+{"tstamp": 1722645796.2434, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722645795.9506, "finish": 1722645796.2434, "ip": "", "conv_id": "4d63473486e246a18b6fb71b80ed17f6", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "when does elena turn into a vampire in the tv series", "output": [["when does elena turn into a vampire in the tv series", "Elena Gilbert\n\nAfter Elena became a vampire, she became sired to Damon and they slept together. According to Plec during an interview with TVLine, Elena \"really believes what she feels is 100 percent genuine and real\", and the couple \"wants to be together and realizes that they shouldn’t, yet really wants to. So what do they do?\" Stefan became heartbroken, feeling angry and betrayed, and lashed out at Damon and Elena. In the season 4 finale, an unsired Elena confirms that she is in love with Damon, and the two kiss passionately. At the end of season 6, the two plan to become human, get married, and have children together, but their dream is abruptly compromised by Kai, who puts Elena into a magical coma. Damon remains entirely devoted to Elena throughout the remaining two seasons as Elena sleeps peacefully.  In the season 8 finale, she is revealed to be happily married to Damon.\nTransition\nExecutive producers Julie Plec and Kevin Williamson agreed that in the book series, Elena was turned into a vampire too early, which was around page 200 of The Awakening. Elena's transition into a vampire was planned for two years. Plec said: \"That felt obviously too soon, and rushed, and we didn’t want to make a show about a teenage girl who instantly becomes a vampire. But we always knew that her journey would take her there eventually\". At the second season's conclusion, Elena was nearly turned into a vampire. Dobrev was happy that she wasn't, because she felt \"it would have been too soon\", and also didn't think it was something Elena or she wanted."]], "corpus": "wikipedia"}