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

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 {"tstamp": 1722446232.0034, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722446231.7002, "finish": 1722446232.0034, "ip": "", "conv_id": "43255f0f01d84ab2ad728e93ed4f8906", "model_name": "embed-english-v3.0", "prompt": "Hvad er en normtid", "output": [["Hvad er en normtid", "Normativity\n\nNormative generally means relating to an evaluative standard. Normativity is the phenomenon in human societies of designating some actions or outcomes as good, desirable, or permissible, and others as bad, undesirable, or impermissible. A norm in this sense means a standard for evaluating or making judgments about behavior or outcomes. \"Normative\" is sometimes also used, somewhat confusingly, to mean relating to a descriptive standard: doing what is normally done or what most others are expected to do in practice. In this sense a norm is not evaluative, a basis for judging behavior or outcomes; it is simply a fact or observation about behavior or outcomes, without judgment. Many researchers in science, law, and philosophy try to restrict the use of the term \"normative\" to the evaluative sense and refer to the description of behavior and outcomes as positive, descriptive, predictive, or empirical.\nNormative has specialised meanings in different academic disciplines such as philosophy, social sciences, and law. In most contexts, normative means 'relating to an evaluation or value judgment.' Normative propositions tend to evaluate some object or some course of action. Normative content differs from descriptive content.\nThough philosophers disagree about how normativity should be understood; it has become increasingly common to understand normative claims as claims about reasons. As Derek Parfit explains:"]], "corpus": "wikipedia"}
 {"tstamp": 1722446289.5149, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722446289.3077, "finish": 1722446289.5149, "ip": "", "conv_id": "a60921de87f34a499dac2cf89e0be350", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What's the point of a main function and/or __name__ == \"__main__\" check in Python? I occasionally notice something like the following in Python scripts:\nif __name__ == \"__main__\":\n    # do stuff like call main()\n\nWhat's the point of this?\n\nA: This allows a python script to be imported or run standalone is a sane way.\nIf you run a python file directly, the __name__ variable will contain __main__. If you import the script that will not be the case. Normally, if you import the script you want to call functions or reference classes from the file.\nIf you did not have this check, any code that was not in a class or function would run when you import.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722446289.5149, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722446289.3077, "finish": 1722446289.5149, "ip": "", "conv_id": "0923dcd96155417db71132f430c88c5b", "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": 1722447761.2931, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722447761.1024, "finish": 1722447761.2931, "ip": "", "conv_id": "a69e4fe18f394932a19bcd5b57c0266f", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: Order Preservation in Limit Algebras\n\nAbstract: The matrix units of a digraph algebra, A, induce a relation, known as the diagonal order, on the projections in a masa in the algebra. Normalizing partial isometries in A act on these projections by conjugation; they are said to be order preserving when they respect the diagonal order. Order preserving embeddings, in turn, are those embeddings which carry order preserving normalizers to order preserving normalizers. This paper studies operator algebras which are direct limits of finite dimensional algebras with order preserving embeddings. We give a complete classification of direct limits of full triangular matrix algebras with order preserving embeddings. We also investigate the problem of characterizing algebras with order preserving embeddings."]], "corpus": "arxiv"}
+{"tstamp": 1722447761.2931, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722447761.1024, "finish": 1722447761.2931, "ip": "", "conv_id": "05b4950e4c6247cd99901948d031c52e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: An Explanation of In-context Learning as Implicit Bayesian Inference\n\nAbstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning."]], "corpus": "arxiv"}