Scheduled Commit

data/clustering_battle-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -1 +1,2 @@
 {"tstamp": 1726258865.3726, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "d97929e2717145a0affbeea71fc1c197", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1de37c39339e4037ba4f11c597751749", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1726326232.2237, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "0f347b84e05543d4ac17a899e0930810", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["Pussy is great, I love licking pussy", "Pussy is great, I love licking pussyv Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with ", " like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "0941baa16d8b4392a6f4332d76d6994c", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Pussy is great, I love licking pussy", "Pussy is great, I love licking pussyv Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with ", " like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -2,3 +2,7 @@
 {"tstamp": 1726125862.2829, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726125803.5938, "finish": 1726125862.2829, "ip": "", "conv_id": "7a92a40679164208a12004b1f7d96d19", "model_name": "GritLM/GritLM-7B", "prompt": ["Tarım ve Orman Bakanlığının 09.09.2024 tarihli ve E-96301635-251.09.02-15703161 sayılı yazısı ile Havza Bazlı Su Kalitesinin İzlenmesi Projesi Seyhan Havzasında Su Kalitesinin İzlenmesi ve Nehir Havza Yönetim Planının Hazırlanması İşi\" 14 Aralık 2023 tarihi itibarıyla resmi olarak başlamış olup bahse konu Proje'nin 2026 yılı içerisinde tamamlanması planlandığı belirtilmiştir.Söz konusu Proje ile, Seyhan Havzasındaki su kalitesinin belirlenmesi maksadıyla tüm su kütlelerinde olmak üzere gözetimsel, operasyonel ve korunan alan izleme noktalarında kimyasal, biyolojik, hidromorfolojik izleme ve saha çalışmaları yapılarak su kütlesi bazında kalite değerlendirmesi gerçekleştirilecek, denizler hariç, kıyı suları dahil olmak üzere yerüstü suları ve yeraltı sularının korunması ve planlanmasına yönelik \"Seyhan Nehir Havza Yönetim Planı\" hazırlanacaktır. Proje kapsamında, \"Stratejik Çevresel Etki Değerlendirmesi Taslak Raporu\" yüklenici tarafından teslim edilmiş olup hem Su Yönetimi Genel Müdürlüğü \"https://www. tarimorman.gov.tr/SYGM\" web sayfasında yayınlanmaktadır. Bahse konu taslak raporuna ilişkin valilik makamı ayrıca mersin valiliği yatırım izleme müdürlüğümüz görüşü idari mali işler müdürlüğüne mersin vali yardımcısına bağlı kurum yatırım izleme müdürlüğü ve koordinasyon başkanlığının sakınca bulunmadığına dair  görüş yazısını örnek versene?"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1726258795.5682, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726258789.8775, "finish": 1726258795.5682, "ip": "", "conv_id": "d97929e2717145a0affbeea71fc1c197", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1726258795.5682, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726258789.8775, "finish": 1726258795.5682, "ip": "", "conv_id": "1de37c39339e4037ba4f11c597751749", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1726326189.0027, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726326188.956, "finish": 1726326189.0027, "ip": "", "conv_id": "0f347b84e05543d4ac17a899e0930810", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["Pussy is great, I love licking pussy"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1726326189.0027, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726326188.956, "finish": 1726326189.0027, "ip": "", "conv_id": "0941baa16d8b4392a6f4332d76d6994c", "model_name": "GritLM/GritLM-7B", "prompt": ["Pussy is great, I love licking pussy"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1726326243.0255, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726326242.9479, "finish": 1726326243.0255, "ip": "", "conv_id": "2c19ce7420134390b6d9bc16b453d57a", "model_name": "GritLM/GritLM-7B", "prompt": [" Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with ", " like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1726326243.0255, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726326242.9479, "finish": 1726326243.0255, "ip": "", "conv_id": "67267705fa85409e8d8bbf8dc1b42f8f", "model_name": "BAAI/bge-large-en-v1.5", "prompt": [" Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with ", " like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -122,3 +122,7 @@
 {"tstamp": 1726319476.8746, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726319476.6508, "finish": 1726319476.8746, "ip": "", "conv_id": "cb6a56a042354fd08577d0f1a95d72d0", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1726325894.8692, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1726325892.3995, "finish": 1726325894.8692, "ip": "", "conv_id": "ecace566d4ee4f1f8398adb4636e47b9", "model_name": "embed-english-v3.0", "prompt": "Pussy is great, I love licking pussy", "output": [["Pussy is great, I love licking pussy", "Eat Pussy\n\nEat Pussy may refer to:\nCunnilingus, an oral sex act\n\"Eat Pussy\", a song by Akinyele from Anakonda, 2001\n\"Eat Pussy\", a song by N.O.R.E. from Noreality, 2007\n“Smoke Weed Eat Pussy”, a song by Ängie, 2016"]], "corpus": "wikipedia"}
 {"tstamp": 1726325894.8692, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1726325892.3995, "finish": 1726325894.8692, "ip": "", "conv_id": "1cbfa3f7f0584453878f9fbb41ecf3f9", "model_name": "voyage-multilingual-2", "prompt": "Pussy is great, I love licking pussy", "output": [["Pussy is great, I love licking pussy", "Pussy\n\nIn the 2002 film 8 Mile, a rapper insults his rivals by including the line, \"How can six dicks be pussies?\" The line relies on double meanings of both dick (either \"contemptible person\" or \"male genitalia\") and pussy (\"weak\" or \"female genitalia\"). Such word play presents a challenge for translators of the film.\nPussy Riot is a Russian radical feminist punk rock collective that stages illegal events in Moscow protesting President Vladimir Putin and the status of women in Russian society. Band member \"Kot\" says that she knows how the word is used in English, and that it is also used in Russian as term of endearment for little girls. These various meanings create a tension with the word \"riot\", which the group likes.\nIn 2017 Planned Parenthood released a series of short videos on YouTube about female sexual health, with the overall title \"How to take care of your pussy\". Instead of the word \"pussy\" being shown or spoken, a cat appears instead. The visuals consist mainly of cats, playing on the popularity of cat videos, with a voiceover by Sasheer Zamata. Refinery29 called it \"a pretty genius metaphor\" and Metro said: \"If there are two things left in this world that are inherently wonderful, it's cats and vaginas. Don't argue. It's true.[...] It makes sense, then, that Planned Parenthood has decided to combine the two to create a truly splendid video series.\" The series has been shortlisted for a Shorty Award."]], "corpus": "wikipedia"}
+{"tstamp": 1726325989.2914, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1726325987.0849, "finish": 1726325989.2914, "ip": "", "conv_id": "28856df34a64452693796a04bd771423", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A language model incorporating real-world sensor data for robotics and multimodal tasks", "output": [["A language model incorporating real-world sensor data for robotics and multimodal tasks", "Title: Interactive Language: Talking to Robots in Real Time\n\nAbstract: We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchmark, and policies). Trained with behavioral cloning on a dataset of hundreds of thousands of language-annotated trajectories, a produced policy can proficiently execute an order of magnitude more commands than previous works: specifically we estimate a 93.5% success rate on a set of 87,000 unique natural language strings specifying raw end-to-end visuo-linguo-motor skills in the real world. We find that the same policy is capable of being guided by a human via real-time language to address a wide range of precise long-horizon rearrangement goals, e.g. \"make a smiley face out of blocks\". The dataset we release comprises nearly 600,000 language-labeled trajectories, an order of magnitude larger than prior available datasets. We hope the demonstrated results and associated assets enable further advancement of helpful, capable, natural-language-interactable robots. See videos at https://interactive-language.github.io."]], "corpus": "arxiv"}
+{"tstamp": 1726325989.2914, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726325987.0849, "finish": 1726325989.2914, "ip": "", "conv_id": "220c76f4e23348e294149421b8e2998c", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A language model incorporating real-world sensor data for robotics and multimodal tasks", "output": [["A language model incorporating real-world sensor data for robotics and multimodal tasks", "Title: Can Large Language Models design a Robot?\n\nAbstract: Large Language Models can lead researchers in the design of robots."]], "corpus": "arxiv"}
+{"tstamp": 1726326068.4737, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1726326066.1024, "finish": 1726326068.4737, "ip": "", "conv_id": "9535bb676f8f41438540237e57a4da34", "model_name": "voyage-multilingual-2", "prompt": "List best LLM prompts", "output": [["List best LLM prompts", "Stackoverflow Stackexchange\n\nQ: Predicting a probability of a sentence using tensorflow I am using this pre-trained model of tensorflow and trying to get a probability of a sentence. My primary task is, out of several sentences find a sentence with the largest probability. \nI am able to predict next words, using this code.\nbazel-bin/lm_1b/lm_1b_eval --mode sample \\\n                           --prefix \"I love that I\" \\\n                           --pbtxt data/graph-2016-09-10.pbtxt \\\n                           --vocab_file data/vocab-2016-09-10.txt  \\\n                           --ckpt 'data/ckpt-*'\n\nHowever, how can I get a probability of a sentence from a list of many sentences? \n"]], "corpus": "stackexchange"}
+{"tstamp": 1726326068.4737, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726326066.1024, "finish": 1726326068.4737, "ip": "", "conv_id": "994319efb88f41cbb08d72a4c104adea", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "List best LLM prompts", "output": [["List best LLM prompts", "Stackoverflow Stackexchange\n\nQ: Command for finding the best linear model in R Is there a way to get R to run all possible models (with all combinations of variables in a dataset) to produce the best/most accurate linear model and then output that model?\nI feel like there is a way to do this, but I am having a hard time finding the information. \n\nA: There are numerous ways this could be achieved, but for a simple way of doing this I would suggest that you have a look at the glmulti package, which is described in detail in this paper:\n\n\n*\n\n*glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models\nAlternatively, very simple example of the model selection as available on the Quick-R website:\n# Stepwise Regression\nlibrary(MASS)\nfit <- lm(y~x1+x2+x3,data=mydata)\nstep <- stepAIC(fit, direction=\"both\")\nstep$anova # display results \n\nOr to simplify even more, you can do more manual model comparison:\nfit1 <- lm(y ~ x1 + x2 + x3 + x4, data=mydata)\nfit2 <- lm(y ~ x1 + x2, data=mydata)\nanova(fit1, fit2) \n\n"]], "corpus": "stackexchange"}

data/retrieval_side_by_side-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -10,3 +10,4 @@
 {"tstamp": 1726261773.3011, "task_type": "retrieval", "type": "rightvote", "models": ["intfloat/e5-mistral-7b-instruct", "intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "4680f19025494e078f2f11de15592a61", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "من هو الملقب بملك الحجاز وسلطان نجد؟", "0_output": [["من هو الملقب بملك الحجاز وسلطان نجد؟", "Kingdom of Hejaz and Nejd\n\nThe Kingdom of Hejaz and Nejd (, ), initially the Kingdom of Hejaz and Sultanate of Nejd (Arabic: , ), was a dual monarchy ruled by Abdulaziz following the victory of the Saudi Sultanate of Nejd over the Hashemite Kingdom of the Hejaz in 1925. It was the fourth iteration of the Saudi State.\nIn 1932, the two kingdoms were unified as the Kingdom of Saudi Arabia."]], "0_corpus": "wikipedia", "1_conv_id": "e37c5fd3ce8a42f7b5f03c86dde648a3", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "من هو الملقب بملك الحجاز وسلطان نجد؟", "1_output": [["من هو الملقب بملك الحجاز وسلطان نجد؟", "King of Saudi Arabia\n\nThe King of Saudi Arabia, officially the King of the Kingdom of Saudi Arabia (), is the monarch and head of state/government of the Kingdom of Saudi Arabia who holds absolute power. He is the head of the Saudi Arabian royal family, the House of Saud. The king is the supreme commander-in-chief of the Royal Saudi Armed Forces and the head of the Saudi national honors system. The king is called the \"Custodian of the Two Holy Mosques\" (), a title that signifies Saudi Arabia's jurisdiction over the mosques of Masjid al-Haram in Mecca and Al-Masjid an-Nabawi in Medina. The title has been used many times through the history of Islam. The first Saudi king to use the title was Faisal; however, King Khalid did not use the title after him. In 1986, King Fahd replaced \"His Majesty\" with the title of Custodian of the Two Holy Mosques, and it has been since used by both King Abdullah and King Salman. The king has been named the most powerful and influential Muslim and Arab leader in the world according to the Muslim 500.\nHistory\nKing Abdul-Aziz, known in the West as Ibn Saud, regained his patrimony, which is known as today's Saudi Arabia in 1902. Restoring his family as emirs of Emirate of Riyadh, he then established Sultanate of Nejd as his headquarters in 1922. Following the establishment of Riyadh as the capital of his state, Ibn Saud then captured Hejaz in 1925.\nIbn Saud proclaimed his dominions as the Sultanate of Nejd in 1921, shortly before completing the unification of the region. He was proclaimed king (malik) of Hejaz in 1926, and raised Nejd to a kingdom as well in 1927. For the next five years, Ibn Saud administered the two parts of his realm, the Kingdom of Hejaz and Nejd as separate units. On 23 September 1932, he formally united his territories into the Kingdom of Saudi Arabia."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726272736.5194, "task_type": "retrieval", "type": "rightvote", "models": ["intfloat/e5-mistral-7b-instruct", "Alibaba-NLP/gte-Qwen2-7B-instruct"], "ip": "", "0_conv_id": "8a8bca63a1654601933bf17ce339dfb4", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "من هو ملك نجد و الحجاز", "0_output": [["من هو ملك نجد و الحجاز", "Kingdom of Hejaz and Nejd\n\nThe Kingdom of Hejaz and Nejd (, ), initially the Kingdom of Hejaz and Sultanate of Nejd (Arabic: , ), was a dual monarchy ruled by Abdulaziz following the victory of the Saudi Sultanate of Nejd over the Hashemite Kingdom of the Hejaz in 1925. It was the fourth iteration of the Saudi State.\nIn 1932, the two kingdoms were unified as the Kingdom of Saudi Arabia."]], "0_corpus": "wikipedia", "1_conv_id": "78e4cbed433f4591b074ecf5b7b52e93", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "من هو ملك نجد و الحجاز", "1_output": [["من هو ملك نجد و الحجاز", "Ibn Saud\n\nAbdulaziz bin Abdul Rahman Al Saud (; 15 January 1875 – 9 November 1953), known in the Western world mononymously as Ibn Saud (; Ibn Suʿūd), was an Arab political and religious leader who founded Saudi Arabia – the third Saudi state – and reigned as its first king from 23 September 1932 until his death in 1953. He had ruled parts of the kingdom since 1902, having previously been Emir, Sultan, and King of Nejd, and King of Hejaz."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726310450.5611, "task_type": "retrieval", "type": "share", "models": ["### Model A: jinaai/jina-embeddings-v2-base-en", "### Model B: mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "992c559bdf7140cda5f51c740bbff136", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "35f7cbdae6b0444ca08827a195de3ba6", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "1_corpus": "wikipedia"}
+{"tstamp": 1726326008.1761, "task_type": "retrieval", "type": "bothbadvote", "models": ["Salesforce/SFR-Embedding-2_R", "BM25"], "ip": "", "0_conv_id": "28856df34a64452693796a04bd771423", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "A language model incorporating real-world sensor data for robotics and multimodal tasks", "0_output": [["A language model incorporating real-world sensor data for robotics and multimodal tasks", "Title: Interactive Language: Talking to Robots in Real Time\n\nAbstract: We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchmark, and policies). Trained with behavioral cloning on a dataset of hundreds of thousands of language-annotated trajectories, a produced policy can proficiently execute an order of magnitude more commands than previous works: specifically we estimate a 93.5% success rate on a set of 87,000 unique natural language strings specifying raw end-to-end visuo-linguo-motor skills in the real world. We find that the same policy is capable of being guided by a human via real-time language to address a wide range of precise long-horizon rearrangement goals, e.g. \"make a smiley face out of blocks\". The dataset we release comprises nearly 600,000 language-labeled trajectories, an order of magnitude larger than prior available datasets. We hope the demonstrated results and associated assets enable further advancement of helpful, capable, natural-language-interactable robots. See videos at https://interactive-language.github.io."]], "0_corpus": "arxiv", "1_conv_id": "220c76f4e23348e294149421b8e2998c", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "A language model incorporating real-world sensor data for robotics and multimodal tasks", "1_output": [["A language model incorporating real-world sensor data for robotics and multimodal tasks", "Title: Can Large Language Models design a Robot?\n\nAbstract: Large Language Models can lead researchers in the design of robots."]], "1_corpus": "arxiv"}

data/sts_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -12,3 +12,5 @@
 {"tstamp": 1726221242.1171, "task_type": "sts", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726221242.072, "finish": 1726221242.1171, "ip": "", "conv_id": "85633c3f8bbc44a8b546475efe934a6b", "model_name": "intfloat/multilingual-e5-large-instruct", "txt0": "Two men in dark clothing, one is reaching into a bag.", "txt1": "A man in a dark jacket stands next to a man dressed in brown reaching down into a bag.", "txt2": "The men are wearing blue uniforms and wrestling in a competition.", "output": ""}
 {"tstamp": 1726221288.6039, "task_type": "sts", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726221288.3413, "finish": 1726221288.6039, "ip": "", "conv_id": "8bc8872180b8444c92575059936661a4", "model_name": "intfloat/e5-mistral-7b-instruct", "txt0": "The man is living in a mansion.", "txt1": "The man does not have a home.", "txt2": "A homeless man carries a sign that says \"hungry\".", "output": ""}
 {"tstamp": 1726221288.6039, "task_type": "sts", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1726221288.3413, "finish": 1726221288.6039, "ip": "", "conv_id": "0cc2eeb31c9043b3b6efde2344386c1c", "model_name": "text-embedding-004", "txt0": "The man is living in a mansion.", "txt1": "The man does not have a home.", "txt2": "A homeless man carries a sign that says \"hungry\".", "output": ""}
+{"tstamp": 1726326269.5476, "task_type": "sts", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726326269.5117, "finish": 1726326269.5476, "ip": "", "conv_id": "f8d002cccf264510a6e299479d52222d", "model_name": "intfloat/multilingual-e5-large-instruct", "txt0": " Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now! Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!", "txt1": "b", "txt2": " Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!", "output": ""}
+{"tstamp": 1726326269.5476, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1726326269.5117, "finish": 1726326269.5476, "ip": "", "conv_id": "675663fd34b441cab8d3a861c622f79b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": " Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now! Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!", "txt1": "b", "txt2": " Rules (Play the video for an explanation ➡️)\nInput & submit texts one-by-one to two anonymous models & vote which clusters them better.\nYou can enter >1 texts at once if you separate them with <|SEP|> like in the examples.\nIf you specify a number of clusters >1, a KMeans will be trained on the embeddings and clusters colored according to its predictions.\nClusters are 1D for 1 text, 2D for 2-3 texts, 3D for >3 texts.\nYou have to enter at least 3 texts, else cluster qualities cannot be judged.\n👇 Vote now!", "output": ""}