Scheduled Commit

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

@@ -43,3 +43,7 @@
 {"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"}
+{"tstamp": 1728815344.4438, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1728815343.1313, "finish": 1728815344.4438, "ip": "", "conv_id": "ff8e802002ca4f26b7a650d243728539", "model_name": "text-embedding-3-large", "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"}
+{"tstamp": 1728815344.4438, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728815343.1313, "finish": 1728815344.4438, "ip": "", "conv_id": "90844bace3fc4550b7f5030877b3ac7a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "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: How much is enough?: Data requirements for statistical NLP\n\nAbstract: In this paper I explore a number of issues in the analysis of data requirements for statistical NLP systems. A preliminary framework for viewing such systems is proposed and a sample of existing works are compared within this framework. The first steps toward a theory of data requirements are made by establishing some results relevant to bounding the expected error rate of a class of simplified statistical language learners as a function of the volume of training data."]], "corpus": "arxiv"}
+{"tstamp": 1728815409.6903, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1728815409.3181, "finish": 1728815409.6903, "ip": "", "conv_id": "059aa820e0ce477bba63f95209e0c180", "model_name": "embed-english-v3.0", "prompt": "I am looking for papers on how to best extract legal requirements from legal text documents", "output": [["I am looking for papers on how to best extract legal requirements from legal text documents", "Title: An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts\n\nAbstract: Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%."]], "corpus": "arxiv"}
+{"tstamp": 1728815409.6903, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1728815409.3181, "finish": 1728815409.6903, "ip": "", "conv_id": "b6e1d8bac23f4f87b2d07dc49f74b401", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "I am looking for papers on how to best extract legal requirements from legal text documents", "output": [["I am looking for papers on how to best extract legal requirements from legal text documents", "Title: An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts\n\nAbstract: Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%."]], "corpus": "arxiv"}