Scheduled Commit

data/retrieval_individual-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -66,3 +66,5 @@
 {"tstamp": 1722365301.1655, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722365300.9919, "finish": 1722365301.1655, "ip": "", "conv_id": "95f3ec488c314e2b81635422386eedb6", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722365480.6099, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722365480.2948, "finish": 1722365480.6099, "ip": "", "conv_id": "a4c7486a646e42299791238df7dd3ffd", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is dense retrieval", "output": [["what is dense retrieval", "Latent semantic analysis\n\nSemantic hashing In semantic hashing  documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses. Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method.\nLatent semantic indexing\nLatent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text.  LSI is based on the principle that words that are used in the same contexts tend to have similar meanings.  A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts.\nLSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents."]], "corpus": "wikipedia"}
 {"tstamp": 1722365480.6099, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722365480.2948, "finish": 1722365480.6099, "ip": "", "conv_id": "960bea43fd02452d9429b486a6719b0e", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what is dense retrieval", "output": [["what is dense retrieval", "Information retrieval\n\nInformation retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.\nAutomated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications."]], "corpus": "wikipedia"}
+{"tstamp": 1722366414.2932, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722366413.3732, "finish": 1722366414.2932, "ip": "", "conv_id": "3406771838ae4294a308437176647a93", "model_name": "embed-english-v3.0", "prompt": "Research introducing a technique where visual vocabulary learned from image tags is used for generating captions for novel objects.", "output": [["Research introducing a technique where visual vocabulary learned from image tags is used for generating captions for novel objects.", "Title: Captioning Images with Novel Objects via Online Vocabulary Expansion\n\nAbstract: In this study, we introduce a low cost method for generating descriptions from images containing novel objects. Generally, constructing a model, which can explain images with novel objects, is costly because of the following: (1) collecting a large amount of data for each category, and (2) retraining the entire system. If humans see a small number of novel objects, they are able to estimate their properties by associating their appearance with known objects. Accordingly, we propose a method that can explain images with novel objects without retraining using the word embeddings of the objects estimated from only a small number of image features of the objects. The method can be integrated with general image-captioning models. The experimental results show the effectiveness of our approach."]], "corpus": "arxiv"}
+{"tstamp": 1722366414.2932, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722366413.3732, "finish": 1722366414.2932, "ip": "", "conv_id": "3f5d842f29464f66b1432b7a355fab52", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Research introducing a technique where visual vocabulary learned from image tags is used for generating captions for novel objects.", "output": [["Research introducing a technique where visual vocabulary learned from image tags is used for generating captions for novel objects.", "Title: Young massive star clusters in the era of the Hubble Space Telescope\n\nAbstract: The Hubble Space Telescope (HST) has been instrumental in the discovery of large numbers of extragalactic young massive star clusters (YMCs), often assumed to be proto-globular clusters (GCs). As a consequence, the field of YMC formation and evolution is thriving, generating major breakthroughs as well as controversies on annual (or shorter) time-scales. Here, I review the long-term survival chances of YMCs, hallmarks of intense starburst episodes often associated with violent galaxy interactions. In the absence of significant external perturbations, the key factor determining a cluster's long-term survival chances is the shape of its stellar initial mass function (IMF). It is, however, not straightforward to assess the IMF shape in unresolved extragalactic YMCs. I also discuss the latest progress in worldwide efforts to better understand the evolution of entire cluster populations, predominantly based on HST observations, and conclude that there is an increasing body of evidence that GC formation appears to be continuing until today; their long-term evolution crucially depends on their environmental conditions, however."]], "corpus": "arxiv"}