Scheduled Commit

data/clustering_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -2,3 +2,5 @@
 {"tstamp": 1722451741.9841, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722451711.9569, "finish": 1722451741.9841, "ip": "", "conv_id": "962bddddb9da48c59856ddeb029c1f57", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722465608.9748, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722465608.2091, "finish": 1722465608.9748, "ip": "", "conv_id": "7ffe7dac8e644f5a970652a0d40ebad6", "model_name": "GritLM/GritLM-7B", "prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722465608.9748, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722465608.2091, "finish": 1722465608.9748, "ip": "", "conv_id": "a410501fd5954b768b52dda204d4868d", "model_name": "text-embedding-3-large", "prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722497199.4688, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722497198.0356, "finish": 1722497199.4688, "ip": "", "conv_id": "114d9a0c8a624b8a965faf4512ef2987", "model_name": "text-embedding-3-large", "prompt": ["square", "circle", "hexagon", "rectangle", "octagon", "triangle", "livestock", "mixed", "dairy", "fruit", "vegetable", "poultry", "sushi bar", "fine dining", "cafe", "pu-erh", "rooibos", "chamomile", "white", "comma", "colon", "hyphen", "exclamation point", "semicolon"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722497199.4688, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722497198.0356, "finish": 1722497199.4688, "ip": "", "conv_id": "d59845cab9fc485580acae1f32625473", "model_name": "embed-english-v3.0", "prompt": ["square", "circle", "hexagon", "rectangle", "octagon", "triangle", "livestock", "mixed", "dairy", "fruit", "vegetable", "poultry", "sushi bar", "fine dining", "cafe", "pu-erh", "rooibos", "chamomile", "white", "comma", "colon", "hyphen", "exclamation point", "semicolon"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -140,3 +140,4 @@
 {"tstamp": 1722486696.5674, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "900497a87ae946f2b060df82b9851089", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "How fast can a wombat run?", "0_output": [["How fast can a wombat run?", "Aardvark\n\nHabitat and range Aardvarks are found in sub-Saharan Africa, where suitable habitat (savannas, grasslands, woodlands and bushland) and food (i.e., ants and termites) is available. They spend the daylight hours in dark burrows to avoid the heat of the day. The only major habitat that they are not present in is swamp forest, as the high water table precludes digging to a sufficient depth. They also avoid terrain rocky enough to cause problems with digging. They have been documented as high as  in Ethiopia. They are present throughout sub-Saharan Africa all the way to South Africa with few exceptions including the coastal areas of Namibia, Ivory Coast, and Ghana. They are not found in Madagascar.\nEcology and behaviour\nAardvarks live for up to 23 years in captivity. Its keen hearing warns it of predators: lions, leopards, cheetahs, African wild dogs, hyenas, and pythons. Some humans also hunt aardvarks for meat. Aardvarks can dig fast or run in zigzag fashion to elude enemies, but if all else fails, they will strike with their claws, tail and shoulders, sometimes flipping onto their backs lying motionless except to lash out with all four feet. They are capable of causing substantial damage to unprotected areas of an attacker. They will also dig to escape as they can. Sometimes, when pressed, aardvarks can dig extremely quickly."]], "0_corpus": "wikipedia", "1_conv_id": "2b37bc7362e541d59eda9e49c49a7780", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "How fast can a wombat run?", "1_output": [["How fast can a wombat run?", "Wombat\n\nWombats are short-legged, muscular quadrupedal marsupials of the family Vombatidae that are native to Australia. Living species are about  in length with small, stubby tails and weigh between . They are adaptable and habitat tolerant, and are found in forested, mountainous, and heathland areas of southern and eastern Australia, including Tasmania, as well as an isolated patch of about  in Epping Forest National Park in central Queensland."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722486743.7104, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "376d83968bd94f46b1efc326d1ad4b42", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "What are some options for recsys architectures?", "0_output": [["What are some options for recsys architectures?", "ReFS\n\nResilient File System (ReFS), codenamed \"Protogon\", is a Microsoft proprietary file system introduced with Windows Server 2012 with the intent of becoming the \"next generation\" file system after NTFS.\nReFS was designed to overcome problems that had become significant over the years since NTFS was conceived, which are related to how data storage requirements have changed. These requirements arose from two major changes in storage systems and usage – the size of storage in use (large or massive arrays of multi-terabyte drives now being fairly common), and the need for continual reliability. As a result, the file system needs to be self-repairing (to prevent disk checking from being impractically slow or disruptive), along with abstraction or  virtualization between physical disks and  logical volumes.\nThe key design advantages of ReFS include automatic integrity checking and data scrubbing, elimination of the need for running chkdsk, protection against data degradation, built-in handling of hard disk drive failure and redundancy, integration of RAID functionality, a switch to copy/allocate on write for data and metadata updates, handling of very long paths and filenames, and storage virtualization and  pooling, including almost arbitrarily sized logical volumes (unrelated to the physical sizes of the used drives).\nComparison with NTFS"]], "0_corpus": "wikipedia", "1_conv_id": "f0ef4dc777594fedaed202f309266d07", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What are some options for recsys architectures?", "1_output": [["What are some options for recsys architectures?", "Recommender system\n\nOverview Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach), as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties.\nThe differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems, Last.fm and Pandora Radio.\nLast.fm creates a \"station\" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique.\nPandora uses the properties of a song or artist (a subset of the 400 attributes provided by the Music Genome Project) to seed a \"station\" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user \"dislikes\" a particular song and emphasizing other attributes when a user \"likes\" a song. This is an example of a content-based approach."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722495176.11, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5c79b101146f4ecd84bd69216e4e1c1b", "0_model_name": "text-embedding-3-large", "0_prompt": "what medical LLMs is out there?", "0_output": [["what medical LLMs is out there?", "Title: LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties\n\nAbstract: We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field."]], "0_corpus": "arxiv", "1_conv_id": "fb33411d7900485285f5fa5039d64941", "1_model_name": "text-embedding-004", "1_prompt": "what medical LLMs is out there?", "1_output": [["what medical LLMs is out there?", "Title: Large language models in medicine: the potentials and pitfalls\n\nAbstract: Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine."]], "1_corpus": "arxiv"}
+{"tstamp": 1722497076.256, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6cf2da5edcbe44f4a8d69338e28da46e", "0_model_name": "text-embedding-004", "0_prompt": "how many seasons are there for the good doctor", "0_output": [["how many seasons are there for the good doctor", "The Good Doctor (American TV series)\n\nDevelopment In May 2014, CBS Television Studios began development on an American remake of the hit South Korean medical drama Good Doctor with Daniel Dae Kim as producer. Kim explained the appeal of adapting the series as \"something that can fit into a recognizable world with a breadth of characters that can be explored in the long run\". The story of an autistic pediatric surgeon was to be set in Boston, and projected to air in August 2015, but CBS did not pick up the project, and it moved to Sony Pictures Television with a put pilot commitment from ABC in October 2016. The series was developed by David Shore who is credited as executive producer alongside Kim, Sebastian Lee and David Kim. ABC officially ordered the series to pilot in January 2017.\nOn May 11, 2017, ABC ordered to series as a co-production with Sony Pictures Television and ABC Studios, and it was officially picked up for a season of 18 episodes on October 3, 2017. On March 7, 2018, ABC renewed the series for a second season. On February 5, 2019, during the TCA press tour, ABC renewed the series for a third season which premiered on September 23, 2019. On February 10, 2020, ABC renewed the series for a fourth season which premiered on November 2, 2020. On August 6, 2020, it was reported that the fourth season opener is set to focus on the COVID-19 pandemic. On May 3, 2021, ABC renewed the series for a fifth season which premiered on September 27, 2021. On March 30, 2022, ABC renewed the series for a sixth season which premiered on October 3, 2022. On May 13, 2022, executive producer Liz Friedman was promoted to co-showrunner alongside Shore for the sixth season.  On April 19, 2023, ABC renewed the series for a seventh season which premiered on February 20, 2024. On January 11, 2024, it was announced that the seventh season will be its final season."]], "0_corpus": "wikipedia", "1_conv_id": "a91e24086b6c47d598ea1e5b5da80416", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "how many seasons are there for the good doctor", "1_output": [["how many seasons are there for the good doctor", "The Good Doctor (American TV series)\n\nPaige Spara as Lea Dilallo-Murphy (seasons 2–7; recurring, season 1), Shaun's love interest until she left to pursue her dream. Later after she returned, they decide to be platonic close friends and roommates, but they finally became a couple in the season 3 finale. In season 4, she and Shaun were expecting a baby girl together before she has a miscarriage due to a medical problem. In the season 4 finale, Lea gets engaged to Shaun. In the season 5 finale, they get married. In season 6, she and Shaun are expecting a baby boy and she gives birth to Steven in the finale. In a flashforward in the series finale, Shaun and Lea have a daughter together as well. Lea worked as an automotive engineer in the first two seasons before becoming Glassman's assistant in season 3. In season 4, she has become the head of the hospital's IT department.\nJasika Nicole as Dr. Carly Lever (season 3; recurring seasons 1–2), the hospital's head and board certified pathologist introduced in season 1 who becomes Shaun's co-worker in season 2 and girlfriend in season 3. However, Carly breaks up with Shaun near the end of season 3 after realizing that he's in love with Lea."]], "1_corpus": "wikipedia"}

data/sts_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -4,3 +4,9 @@
 {"tstamp": 1722465680.2296, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722465680.2018, "finish": 1722465680.2296, "ip": "", "conv_id": "c982e91e50d84cd3a8f98b88d36ebeb0", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "She trained a neural network to recognize faces.", "txt1": "She developed an AI to identify human features.", "txt2": "She trained a new recruit to recognize faces.", "output": ""}
 {"tstamp": 1722470902.8689, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722470902.5608, "finish": 1722470902.8689, "ip": "", "conv_id": "34a40f71159343edb6af34dbba264aa9", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "Further chapters describe what he felt about their intercourse.", "txt1": "Further chapters give no information about what he felt.", "txt2": "Pages later (Chapter XXXVII), it is acknowledged that  He, too, felt that this was their last free intercourse.", "output": ""}
 {"tstamp": 1722470902.8689, "task_type": "sts", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722470902.5608, "finish": 1722470902.8689, "ip": "", "conv_id": "4e124f5513804741b04157ae8567d279", "model_name": "text-embedding-004", "txt0": "Further chapters describe what he felt about their intercourse.", "txt1": "Further chapters give no information about what he felt.", "txt2": "Pages later (Chapter XXXVII), it is acknowledged that  He, too, felt that this was their last free intercourse.", "output": ""}
+{"tstamp": 1722497128.6136, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722497128.4037, "finish": 1722497128.6136, "ip": "", "conv_id": "3b3edf097da44084bab386dde3291ddd", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "A man in a red shirt is reading at the park.", "txt1": "Several people wearing green shirts walk on the beach together.", "txt2": "People are walking on the beach.", "output": ""}
+{"tstamp": 1722497128.6136, "task_type": "sts", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722497128.4037, "finish": 1722497128.6136, "ip": "", "conv_id": "0974afb9b33345e289cfa6c1a78956e3", "model_name": "text-embedding-004", "txt0": "A man in a red shirt is reading at the park.", "txt1": "Several people wearing green shirts walk on the beach together.", "txt2": "People are walking on the beach.", "output": ""}
+{"tstamp": 1722497170.5176, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722497170.4884, "finish": 1722497170.5176, "ip": "", "conv_id": "f8d969c3bd704071ba2b1eb1c05a6d4b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "Some were in business and some were talking about starting a business.", "txt1": "No one in the room ever started a business before.", "txt2": "There were some that already had a business and others that were just starting.", "output": ""}
+{"tstamp": 1722497170.5176, "task_type": "sts", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722497170.4884, "finish": 1722497170.5176, "ip": "", "conv_id": "9806610adc424648ab453a195cba6932", "model_name": "nomic-ai/nomic-embed-text-v1.5", "txt0": "Some were in business and some were talking about starting a business.", "txt1": "No one in the room ever started a business before.", "txt2": "There were some that already had a business and others that were just starting.", "output": ""}
+{"tstamp": 1722497295.089, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722497295.0482, "finish": 1722497295.089, "ip": "", "conv_id": "2f92e15a145342e5a983b6ee2f4c1a8a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "A man is holding the leash of a dog.", "txt1": "The man is wearing a baseball cap.", "txt2": "A man wearing a straw hat sitting in a chair holding the leash on a dog who is laying on the sidewalk next to him.", "output": ""}
+{"tstamp": 1722497295.089, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722497295.0482, "finish": 1722497295.089, "ip": "", "conv_id": "c743edfaa317490cbebaaf6b3149a965", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "A man is holding the leash of a dog.", "txt1": "The man is wearing a baseball cap.", "txt2": "A man wearing a straw hat sitting in a chair holding the leash on a dog who is laying on the sidewalk next to him.", "output": ""}