Scheduled Commit

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

@@ -19,3 +19,4 @@
 {"tstamp": 1722445834.473, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ed8435cf63824a24a3b2372e6608f29d", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "how to change share name in windows server 2008?", "0_output": [["how to change share name in windows server 2008?", "Superuser Stackexchange\n\nQ: How to change the administrator account name from command prompt in Windows Server 2008 Server Core? I'm trying to change the administrator account name on my virtual machine for lab work purposes.  I'm running Windows Server 2008 Server Core, therefore I'm only using the standard command prompts.\nHow can I do this?\n\nA: If it domain user install Active Directory Administration module for PowerShell. \ncommand line:\npowershell Import-module ActiveDirectory\n\nrename domain user -\nPowershell:\nGet-ADUser -Identity 'Administrator' | Rename-ADObject -NewName 'Partisan'\n\ncommand line:\npowershell Get-ADUser -Identity 'Administrator' ^| Rename-ADObject -NewName 'Partisan'\n\nvar 2:\ndsquery user -name Administrator | dsmove -newname \"Partisan\"\n\nlocal administrator -\nPowershell:\nRename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'\n\ncommand line:\npowershell Rename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'\n\nvar2:\nwmic UserAccount where Name=\"Administrator\" call Rename Name=\"Partisan\"\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "939f7aa837ce48d9b9700ee6346622d4", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "how to change share name in windows server 2008?", "1_output": [["how to change share name in windows server 2008?", "Serverfault Stackexchange\n\nQ: How do you configure a \\\\domain\\sharedFolder in windows 2008? Background: \n\n\n*\n\n*I've got several Windows 2008 servers setup by someone else.\n\n*They are all on one domain, lets call this mydomain\n\n*Several boxes are accessing a shared folder by going to \\\\mydomain\\share1\n\n*I found the actual files backing the share on box1\n\n*I need to move the location of this share to a larger drive for space reasons\n\n\nI'm familiar with setting up and managing shares like \\\\box1\\share1, but where do you manage/configure shares that are domain-wide; accessed like \\\\mydomain\\share1 ?\n\nA: It sounds like you have a domain based DFS configured, which is managed by the DFS Management utility.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722446198.8045, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9e2f3e5d4eb04b5db030979a91bfe01e", "0_model_name": "text-embedding-3-large", "0_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "0_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "0_corpus": "arxiv", "1_conv_id": "99d70563aab749489c924deb1dc45544", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "1_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "1_corpus": "arxiv"}
 {"tstamp": 1722447804.2737, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a69e4fe18f394932a19bcd5b57c0266f", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "0_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."]], "0_corpus": "arxiv", "1_conv_id": "05b4950e4c6247cd99901948d031c52e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "1_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."]], "1_corpus": "arxiv"}
+{"tstamp": 1722448831.6873, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "7a79ffac6e0d41b3add4d6518d91cca8", "0_model_name": "BAAI/bge-large-en-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: 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"]], "0_corpus": "stackexchange", "1_conv_id": "4e34da23af094e5bae7f2ad3357c2fe9", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "1_corpus": "stackexchange"}

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

@@ -56,3 +56,5 @@
 {"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"}
+{"tstamp": 1722448793.3454, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722448793.1805, "finish": 1722448793.3454, "ip": "", "conv_id": "7a79ffac6e0d41b3add4d6518d91cca8", "model_name": "BAAI/bge-large-en-v1.5", "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": 1722448793.3454, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722448793.1805, "finish": 1722448793.3454, "ip": "", "conv_id": "4e34da23af094e5bae7f2ad3357c2fe9", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "corpus": "stackexchange"}