Scheduled Commit

data/retrieval_individual-3c73e733-b3f5-49ee-809f-5dd8857c42b8.jsonl

@@ -41,3 +41,5 @@
 {"tstamp": 1728808291.2599, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1728808291.0797, "finish": 1728808291.2599, "ip": "", "conv_id": "571a51991f0d4bc5b8de1194ec74acd9", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "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"]], "corpus": "stackexchange"}
 {"tstamp": 1728808354.992, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728808353.6967, "finish": 1728808354.992, "ip": "", "conv_id": "e329c11d1a1d4173bbc703f36c153a20", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "python `difflib`", "output": [["python `difflib`", "Python\n\nPython may refer to:\nSnakes\nPythonidae, a family of nonvenomous snakes found in Africa, Asia, and Australia\nPython (genus), a genus of Pythonidae found in Africa and Asia\nPython (mythology), a mythical serpent\nComputing\nPython (programming language), a widely used high-level programming language\nPython, a native code compiler for CMU Common Lisp\nPython, the internal project name for the PERQ 3 computer workstation\nPeople\nPython of Aenus (4th-century BCE), student of Plato\nPython (painter), (ca. 360–320 BCE) vase painter in Poseidonia\nPython of Byzantium, orator, diplomat of Philip II of Macedon\nPython of Catana, poet who accompanied Alexander the Great\nPython Anghelo (1954–2014) Romanian graphic artist\nRoller coasters\nPython (Efteling), a roller coaster in the Netherlands\nPython (Busch Gardens Tampa Bay), a defunct roller coaster\nPython (Coney Island, Cincinnati, Ohio), a steel roller coaster\nVehicles\nPython (automobile maker), an Australian car company\nPython (Ford prototype), a Ford prototype sports car\nWeaponry\nPython (missile), a series of Israeli air-to-air missiles\nPython (nuclear primary), a gas-boosted fission primary used in thermonuclear weapons\nColt Python, a revolver\nOther uses\nPython (codename), a British nuclear war contingency plan\nPython (film), a 2000 horror film by Richard Clabaugh\nMonty Python or the Pythons, a British comedy group\nPython (Monty) Pictures, a company owned by the troupe's surviving members\nPython, a work written by philosopher Timon of Phlius"]], "corpus": "wikipedia"}
 {"tstamp": 1728808354.992, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1728808353.6967, "finish": 1728808354.992, "ip": "", "conv_id": "5710fd6a448b4cc8baec063f28c3499d", "model_name": "embed-english-v3.0", "prompt": "python `difflib`", "output": [["python `difflib`", "Diff\n\nAlgorithmic derivatives Utilities that compare source files by their syntactic structure have been built mostly as research tools for some programming languages; some are available as commercial tools. In addition, free tools that perform syntax-aware diff include:\nC++: zograscope, AST-based.\nHTML: Daisydiff, html-differ.\nXML: xmldiffpatch by Microsoft and xmldiffmerge for IBM.\nJavaScript: astii (AST-based).\nMulti-language: Pretty Diff (format code and then diff)\nspiff is a variant of diff that ignores differences in floating point calculations with roundoff errors and whitespace, both of which are generally irrelevant to source code comparison. Bellcore wrote the original version. An HPUX port is the most current public release. spiff does not support binary files. spiff outputs to the standard output in standard diff format and accepts inputs in the C, Bourne shell, Fortran, Modula-2 and Lisp programming languages.\nLibXDiff is an LGPL library that provides an interface to many algorithms from 1998. An improved Myers algorithm with Rabin fingerprint was originally implemented (as of the final release of 2008), but git and libgit2's fork has since expanded the repository with many of its own. One algorithm called \"histogram\" is generally regarded as much better than the original Myers algorithm, both in speed and quality. This is the modern version of LibXDiff used by Vim."]], "corpus": "wikipedia"}
+{"tstamp": 1728808629.2639, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1728808628.8692, "finish": 1728808629.2639, "ip": "", "conv_id": "4caa1990336948d19bed734804601bd5", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences\n\nAbstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their \"types\" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers."]], "corpus": "arxiv"}
+{"tstamp": 1728808629.2639, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1728808628.8692, "finish": 1728808629.2639, "ip": "", "conv_id": "2ffca4bfcc6b4ce98c8f96fc859966d1", "model_name": "voyage-multilingual-2", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}

data/sts_individual-3c73e733-b3f5-49ee-809f-5dd8857c42b8.jsonl

@@ -0,0 +1,2 @@
+{"tstamp": 1728808668.231, "task_type": "sts", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1728808667.5833, "finish": 1728808668.231, "ip": "", "conv_id": "c5dad915bcf94d8b98a7ba561f41258b", "model_name": "jinaai/jina-embeddings-v2-base-en", "txt0": "hello", "txt1": "good morning", "txt2": "sup", "output": ""}
+{"tstamp": 1728808668.231, "task_type": "sts", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1728808667.5833, "finish": 1728808668.231, "ip": "", "conv_id": "6e777f63ddc54559be5cabc62f95d47e", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "txt0": "hello", "txt1": "good morning", "txt2": "sup", "output": ""}