Scheduled Commit

data/retrieval_battle-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -157,3 +157,4 @@
 {"tstamp": 1734716330.983, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6f21e2199e424e1c8c3224c98a700b13", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "does aquarius die in fairy tail?", "0_output": [["does aquarius die in fairy tail?", "List of Fairy Tail characters\n\nNumerous spirits are summoned using store-bought silver keys, including , a breed of \"dog\" spirits kept by celestial wizards as pets; Lucy keeps a Nikora that she names , who is identical to the character of the same name from Hiro Mashima's earlier work, Rave Master. There are also twelve golden Gatekeys that summon more powerful spirits that are based on the zodiacal constellations, including Leo, the true form of Loke.  is a cold mermaid who uses an urn that unleashes massive torrents of water on her target.  is a perverted minotaur who wields a giant, double-edged battle axe and has superhuman strength.  is a humanoid hairdresser with six crab legs protruding from his back.  is a masochistic woman dressed like a maid who burrows rapidly underground.  is a tall man who wears a horse costume and possesses superb archery skills.  is a timid young woman with sheep horns who conjures wool clouds that subdue her opponents.  are small twin spirits who mimic the appearances and abilities of others, and can also read their minds.  is a man with a scorpion tail-shaped cannon that produces gusts of sand.  is an anthropomorphic goat and martial artist who dresses like a butler.  is a pair of mother and son fish who attack their opponent in tandem, and can assume a humanoid form.  is a belly dancer who manipulates gravity using a pair of weighing scales. Another spirit called , which takes the form of a giant mechanical snake, and is considered the \"thirteenth\" zodiac spirit.\nEdolas\nis a parallel universe that exists separately from Earth-land, the universe in which Fairy Tail takes place. It is inhabited by counterparts of Earth-land's residents, each with opposing personalities or traits such as age and species. It is also the home of several characters in the series, including Jellal Fernandes's counterpart Mystogan, and Exceed such as Happy and Carla. As opposed to Earth-land, magic is a limited resource in Edolas and cannot be used as a natural ability by humans, who instead use items combined with magic-infused lacrima crystals; the Earth-land wizards are similarly only able to perform magic in Edolas after taking pills provided by Mystogan for this purpose."]], "0_corpus": "wikipedia", "1_conv_id": "e1f5aa2c97404989ace9206ad32f0353", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "does aquarius die in fairy tail?", "1_output": [["does aquarius die in fairy tail?", "Fairy Tail\n\nFairy Tail (stylized in all caps) is a Japanese manga series written and illustrated by Hiro Mashima. It was serialized in Kodansha's Weekly Shōnen Magazine from August 2006 to July 2017, with the individual chapters collected and published into 63 tankōbon volumes. The story follows the adventures of Natsu Dragneel, a member of the popular wizard guild Fairy Tail, as he searches the fictional world of Earth-land for the dragon Igneel."]], "1_corpus": "wikipedia"}
 {"tstamp": 1734762585.1382, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "dc32d15eade14086b984a13449685923", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "0_corpus": "stackexchange", "1_conv_id": "b8268af079de418fa28501219d8021d0", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone. \nThe video stream is written as \"socket\" to a file as H264 stream.\n$ ffmpeg -i [STREAM]\nInput #0, h264, from 'stream_h264':\n  Duration: N/A, bitrate: N/A\n  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc\n\nReading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:\nmplayer -fs -demuxer h264es -benchmark stream_h264\n\nThis plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python? \nAll recommendations are more than welcome!\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1734779113.0864, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6a0ff819b2064f31a750c8f12c843d1a", "0_model_name": "voyage-multilingual-2", "0_prompt": "set up a zfs fs", "0_output": [["set up a zfs fs", "Serverfault Stackexchange\n\nQ: zfs pool not automatically mounted I recently installed freebsd 8 on a file server and created a raidz-pool from 4 drives. It works fine, except the filesystems are not mounted automatically on boot. zpool status and zfs list shows nothing wrong but I have to do a zpool export and import for the pool and filesystems to get mounted, and consequently for the shares to be accessible from the network.\nThis also happens on a vmware test system I have, also running freebsd 8 (x64 in both cases). After the manual import everything works fine.\nI assume this is a small bug in freebsd 8 since it happens with two different installs, and I can work around it with a small script, but what's the (proper) way to solve it?\n\nA: Sounds like your ZFS isn't enabled in rc.conf...\necho 'zfs_enable=\"YES\"' >> /etc/rc.conf\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "e06a78049e92419eace60897d4fc4a3b", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "set up a zfs fs", "1_output": [["set up a zfs fs", "Askubuntu Stackexchange\n\nQ: How to use ZFS on Ubuntu 16.04? It seem that Ubuntu 16.04 LTS supports the ZFS filesystem. But how do I use it?\nI'm sorry, I don't know very much about ZFS, but it sounds good. I want it.\n\nA: As root in an Ubuntu 16.04.1 LTS this works:\napt update\napt install -y zfs\n\nYou are then done. You can check it is there by doing:\nzfs list\n\nyou can then create a pool by doing:\nzpool create -f zxavi /dev/sdb\n\nor whatever disk you want to use; and after that:\nzpool status\n\nBoom, done!\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1734792910.5666, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "dd0b0ae34d414976b4de1e8025595445", "0_model_name": "voyage-multilingual-2", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding\n\nAbstract: The evaluation of English text embeddings has transitioned from evaluating a handful of datasets to broad coverage across many tasks through benchmarks such as MTEB. However, this is not the case for multilingual text embeddings due to a lack of available benchmarks. To address this problem, we introduce the Scandinavian Embedding Benchmark (SEB). SEB is a comprehensive framework that enables text embedding evaluation for Scandinavian languages across 24 tasks, 10 subtasks, and 4 task categories. Building on SEB, we evaluate more than 26 models, uncovering significant performance disparities between public and commercial solutions not previously captured by MTEB. We open-source SEB and integrate it with MTEB, thus bridging the text embedding evaluation gap for Scandinavian languages."]], "0_corpus": "arxiv", "1_conv_id": "6ac49c918e974913a9efa1d23e48ddf2", "1_model_name": "text-embedding-004", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "1_corpus": "arxiv"}

data/retrieval_individual-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -552,3 +552,5 @@
 {"tstamp": 1734762512.2182, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1734762509.9973, "finish": 1734762512.2182, "ip": "", "conv_id": "b8268af079de418fa28501219d8021d0", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone. \nThe video stream is written as \"socket\" to a file as H264 stream.\n$ ffmpeg -i [STREAM]\nInput #0, h264, from 'stream_h264':\n  Duration: N/A, bitrate: N/A\n  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc\n\nReading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:\nmplayer -fs -demuxer h264es -benchmark stream_h264\n\nThis plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python? \nAll recommendations are more than welcome!\n"]], "corpus": "stackexchange"}
 {"tstamp": 1734779096.9528, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1734779096.6449, "finish": 1734779096.9528, "ip": "", "conv_id": "6a0ff819b2064f31a750c8f12c843d1a", "model_name": "voyage-multilingual-2", "prompt": "set up a zfs fs", "output": [["set up a zfs fs", "Serverfault Stackexchange\n\nQ: zfs pool not automatically mounted I recently installed freebsd 8 on a file server and created a raidz-pool from 4 drives. It works fine, except the filesystems are not mounted automatically on boot. zpool status and zfs list shows nothing wrong but I have to do a zpool export and import for the pool and filesystems to get mounted, and consequently for the shares to be accessible from the network.\nThis also happens on a vmware test system I have, also running freebsd 8 (x64 in both cases). After the manual import everything works fine.\nI assume this is a small bug in freebsd 8 since it happens with two different installs, and I can work around it with a small script, but what's the (proper) way to solve it?\n\nA: Sounds like your ZFS isn't enabled in rc.conf...\necho 'zfs_enable=\"YES\"' >> /etc/rc.conf\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1734779096.9528, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734779096.6449, "finish": 1734779096.9528, "ip": "", "conv_id": "e06a78049e92419eace60897d4fc4a3b", "model_name": "GritLM/GritLM-7B", "prompt": "set up a zfs fs", "output": [["set up a zfs fs", "Askubuntu Stackexchange\n\nQ: How to use ZFS on Ubuntu 16.04? It seem that Ubuntu 16.04 LTS supports the ZFS filesystem. But how do I use it?\nI'm sorry, I don't know very much about ZFS, but it sounds good. I want it.\n\nA: As root in an Ubuntu 16.04.1 LTS this works:\napt update\napt install -y zfs\n\nYou are then done. You can check it is there by doing:\nzfs list\n\nyou can then create a pool by doing:\nzpool create -f zxavi /dev/sdb\n\nor whatever disk you want to use; and after that:\nzpool status\n\nBoom, done!\n"]], "corpus": "stackexchange"}
+{"tstamp": 1734792896.8454, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1734792894.3709, "finish": 1734792896.8454, "ip": "", "conv_id": "dd0b0ae34d414976b4de1e8025595445", "model_name": "voyage-multilingual-2", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding\n\nAbstract: The evaluation of English text embeddings has transitioned from evaluating a handful of datasets to broad coverage across many tasks through benchmarks such as MTEB. However, this is not the case for multilingual text embeddings due to a lack of available benchmarks. To address this problem, we introduce the Scandinavian Embedding Benchmark (SEB). SEB is a comprehensive framework that enables text embedding evaluation for Scandinavian languages across 24 tasks, 10 subtasks, and 4 task categories. Building on SEB, we evaluate more than 26 models, uncovering significant performance disparities between public and commercial solutions not previously captured by MTEB. We open-source SEB and integrate it with MTEB, thus bridging the text embedding evaluation gap for Scandinavian languages."]], "corpus": "arxiv"}
+{"tstamp": 1734792896.8454, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734792894.3709, "finish": 1734792896.8454, "ip": "", "conv_id": "6ac49c918e974913a9efa1d23e48ddf2", "model_name": "text-embedding-004", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "corpus": "arxiv"}