Scheduled Commit

data/retrieval_battle-5b78c89f-244c-4bc4-9693-2c3c7acb6977.jsonl

@@ -86,3 +86,6 @@
 {"tstamp": 1723214046.9925, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "7a11930c2bc946108b91b6213be3c172", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Que deviens le langage de programmation Prolog ? Réponds moi en français.", "0_output": [["Que deviens le langage de programmation Prolog ? Réponds moi en français.", "Title: Une s\\'emantique observationnelle du mod\\`ele des bo\\^ites pour la r\\'esolution de programmes logiques (version \\'etendue)\n\nAbstract: This report specifies an observational semantics and gives an original presentation of the Byrd's box model. The approach accounts for the semantics of Prolog tracers independently of a particular implementation. Traces are, in general, considered as rather obscure and difficult to use. The proposed formal presentation of a trace constitutes a simple and pedagogical approach for teaching Prolog or for implementing Prolog tracers. It constitutes a form of declarative specification for the tracers. Our approach highlights qualities of the box model which made its success, but also its drawbacks and limits. As a matter of fact, the presented semantics is only one example to illustrate general problems relating to tracers and observing processes. Observing processes know, from observed processes, only their traces. The issue is then to be able to reconstitute by the sole analysis of the trace the main part of the observed process, and if possible, without any loss of information."]], "0_corpus": "arxiv", "1_conv_id": "ed819f13f75f4f89abde0dffbd38c1d4", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Que deviens le langage de programmation Prolog ? Réponds moi en français.", "1_output": [["Que deviens le langage de programmation Prolog ? Réponds moi en français.", "Title: The Logic Programming Paradigm and Prolog\n\nAbstract: This is a tutorial on logic programming and Prolog appropriate for a course on programming languages for students familiar with imperative programming."]], "1_corpus": "arxiv"}
 {"tstamp": 1723214104.296, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c1917f61d9fe4495b7ae7f7e48a75e17", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Que devient le langage de programmation Prolog ? Réponds moi en français.", "0_output": [["Que devient le langage de programmation Prolog ? Réponds moi en français.", "Prolog\n\nProlog is a logic programming language that has its origins in artificial intelligence, automated theorem proving and computational linguistics.\nProlog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program is a set of facts and rules, which define relations.  A computation is initiated by running a query over the program.\nProlog was one of the first logic programming languages and remains the most popular such language today, with several free and commercial implementations available. The language has been used for theorem proving, expert systems, term rewriting, type systems, and automated planning, as well as its original intended field of use, natural language processing.\nProlog is a Turing-complete, general-purpose programming language, which is well-suited for intelligent knowledge-processing applications."]], "0_corpus": "wikipedia", "1_conv_id": "ed8d86ebfa44446e970c2cf5ebd58f31", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Que devient le langage de programmation Prolog ? Réponds moi en français.", "1_output": [["Que devient le langage de programmation Prolog ? Réponds moi en français.", "Prolog\n\nProlog is a logic programming language that has its origins in artificial intelligence, automated theorem proving and computational linguistics.\nProlog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program is a set of facts and rules, which define relations.  A computation is initiated by running a query over the program.\nProlog was one of the first logic programming languages and remains the most popular such language today, with several free and commercial implementations available. The language has been used for theorem proving, expert systems, term rewriting, type systems, and automated planning, as well as its original intended field of use, natural language processing.\nProlog is a Turing-complete, general-purpose programming language, which is well-suited for intelligent knowledge-processing applications."]], "1_corpus": "wikipedia"}
 {"tstamp": 1723214164.7579, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "9ec7bcd6380a49a59eacbbea4b7db503", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Explique moi le principe des adjacents possibles.", "0_output": [["Explique moi le principe des adjacents possibles.", "Arrondissements of France\n\nAn arrondissement () is the third level of administrative division in France generally corresponding to the territory overseen by a subprefect. As of 2023, the 101 French departments are divided into 333 arrondissements (including 13 overseas).\nThe capital of an arrondissement is called a subprefecture. When an arrondissement contains the prefecture (capital) of the department, that prefecture is the capital of the arrondissement, acting both as a prefecture and as a subprefecture. Arrondissements are further divided into cantons and communes.\nThe term arrondissement can be roughly translated into English as district.\nSome municipalities in Quebec are divided into arrondissements, reflecting the province’s historical link to New France.\nRole and administration\nThe administration of an arrondissement is assigned to a subprefect () who assists the departmental prefect ()."]], "0_corpus": "wikipedia", "1_conv_id": "2004eb4b5c4348e6b218975d8c4e556d", "1_model_name": "embed-english-v3.0", "1_prompt": "Explique moi le principe des adjacents possibles.", "1_output": [["Explique moi le principe des adjacents possibles.", "French grammar\n\nAdverbs themselves are generally invariable. An exception to this is the adverb tout \"wholly, very\" which agrees in gender and number with the adjective it modifies when it is in the feminine and begins with a consonant (e.g. tout petit \"very small, m.s.\", tous petits \"very small, m.pl.\" but toute petite \"very small, f.s.\", toutes petites \"very small, f.pl.\" — when beginning with a vowel however: tout entier, tout entiers, tout entière, tout entières \"completely, as a whole\" (with liaison)).\nPrepositions\nFrench prepositions link two related parts of a sentence. In word order, they are placed in front of a noun in order to specify the relationship between the noun and the verb, adjective, or other noun that precedes it. Some common French prepositions are: à (to, at, in), à côté de (next to, beside), après (after), au sujet de (about, on the subject of), avant (before), avec (with), chez (at the home/office of, among), contre (against), dans (in), d'après (according to), de (from, of, about), depuis (since, for), derrière (in back of, behind), devant (in front of), durant (during, while), en (in, on, to), en dehors de (outside of), en face de (facing, across from), entre (between), envers (toward), environ (approximately), hors de (outside of), jusque (until, up to, even), loin de (far from), malgré (despite), par (by, through), parmi (among), pendant (during), pour (for), près de (near), quant à (as for, regarding), sans (without), selon (according to), sous (under), suivant (according to), sur (on), vers (toward)."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723214250.5246, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "0eae60be6a9a4aba93f2db3e8c73b3ac", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "is pyjamas an american word?", "0_output": [["is pyjamas an american word?", "English Stackexchange\n\nQ: Difference between \"pajama\", \"pyjama\" and \"paijama\" Which is the correct word: pajama, pyjama or paijama? \nIs there a difference between these three?\n\nA: Pajama is the American spelling while pyjama is the British spelling. Both mean the same.\n\nPajama/Pyjama:  loose-fitting clothes worn for sleep\n\nPaijama is the word from which pajama/pyjama has been derived. Also, the meaning (of pajama/pyjama) has been altered a bit.\n\nPaijama: loose, lightweight trousers fitted with drawstring waistbands\n\nPajama/Pyjama was incorporated into the English language during the British Rule in India.\n(Bonus: Most people in India call it either \"paijama\" or \"pajama\")\n"]], "0_corpus": "stackexchange", "1_conv_id": "49f040f2e8bd479ba79bd8da5e7a97ed", "1_model_name": "BM25", "1_prompt": "is pyjamas an american word?", "1_output": [["is pyjamas an american word?", "English Stackexchange\n\nQ: Difference between \"pajama\", \"pyjama\" and \"paijama\" Which is the correct word: pajama, pyjama or paijama? \nIs there a difference between these three?\n\nA: Pajama is the American spelling while pyjama is the British spelling. Both mean the same.\n\nPajama/Pyjama:  loose-fitting clothes worn for sleep\n\nPaijama is the word from which pajama/pyjama has been derived. Also, the meaning (of pajama/pyjama) has been altered a bit.\n\nPaijama: loose, lightweight trousers fitted with drawstring waistbands\n\nPajama/Pyjama was incorporated into the English language during the British Rule in India.\n(Bonus: Most people in India call it either \"paijama\" or \"pajama\")\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1723214321.8007, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5d900500bce240c5bbf2aef59147a9bc", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "will wout van aert ever will Paris roubaix", "0_output": [["will wout van aert ever will Paris roubaix", "Roubaix\n\nToponymy The current city's name is most likely derived from Frankish rausa \"reed\" and baki \"brook\". Therefore, the meaning of Roubaix can, in all likelihood, find its origin on the banks of its three historical brooks: Espierre, Trichon and Favreuil. The place was mentioned for the first time in a Latinised form in the 9th century: Villa Rusbaci. Thereafter, the following names were in use:  1047 and 1106 Rubais, 1122 Rosbays, 1166 Rusbais, 1156 and 1202 Robais, 1223 Roubais. Over the span of centuries, the name evolved to Roubaix as shown on Mercator's map of Flanders published at Leuven in 1540.\nParallel to the official and usual name Roubaix, some translations are worth a mention. Firstly, though the city has never belonged to the Flemish-speaking area, the seldom-heard renderings Robeke and Roodebeeke are documented for Roubaix. Furthermore, the Dutch Language Union established Robaais as the city's proper Dutch name. Lastly, one can cite Rosbacum as the definite Latin transcription of Roubaix which has been in use since the 19th century, as recorded on dedication statements sealed in the first stones of the foundations of the City Hall laid in 1840 and the Church of Notre Dame laid in 1842.\nHistory\nHeraldry\nPeople\nInhabitants of Roubaix are known in English as \"Roubaisians\" and in French as Roubaisiens () or in the feminine form Roubaisiennes (), also natively called Roubaignots () or in the feminine form Roubaignotes ()."]], "0_corpus": "wikipedia", "1_conv_id": "d406de720188454babb25d33b24c7f74", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "will wout van aert ever will Paris roubaix", "1_output": [["will wout van aert ever will Paris roubaix", "Wout van Aert\n\nWout van Aert (born 15 September 1994) is a Belgian professional road and cyclo-cross racer who rides for UCI WorldTeam . Van Aert won three consecutive men's races at the UCI Cyclo-cross World Championships in 2016, 2017 and 2018.\nHaving initially prioritised competing in cyclo-cross, Van Aert  terminated his contract with  in 2018 and  joined  in March 2019, on a three-year deal. Van Aert has since taken more than thirty professional road wins, including nine stage victories at the Tour de France between 2019 and 2022 (also winning the points classification in 2022), and won Milan–San Remo – one of the five Cycling monuments – in 2020."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723214376.7533, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b9286918cc114d8bb1a337f3424394b8", "0_model_name": "voyage-multilingual-2", "0_prompt": "Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "0_output": [["Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "Title: MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis\n\nAbstract: Recently, numerous embedding models have been made available and widely used for various NLP tasks. The Massive Text Embedding Benchmark (MTEB) has primarily simplified the process of choosing a model that performs well for several tasks in English, but extensions to other languages remain challenging. This is why we expand MTEB to propose the first massive benchmark of sentence embeddings for French. We gather 15 existing datasets in an easy-to-use interface and create three new French datasets for a global evaluation of 8 task categories. We compare 51 carefully selected embedding models on a large scale, conduct comprehensive statistical tests, and analyze the correlation between model performance and many of their characteristics. We find out that even if no model is the best on all tasks, large multilingual models pre-trained on sentence similarity perform exceptionally well. Our work comes with open-source code, new datasets and a public leaderboard."]], "0_corpus": "arxiv", "1_conv_id": "52edf7a1ae264368a62d5b66e4d9b210", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "1_output": [["Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "Title: FlauBERT: Unsupervised Language Model Pre-training for French\n\nAbstract: Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP."]], "1_corpus": "arxiv"}

data/retrieval_individual-5b78c89f-244c-4bc4-9693-2c3c7acb6977.jsonl

@@ -356,3 +356,7 @@
 {"tstamp": 1723214137.3189, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723214135.1574, "finish": 1723214137.3189, "ip": "", "conv_id": "2004eb4b5c4348e6b218975d8c4e556d", "model_name": "embed-english-v3.0", "prompt": "Explique moi le principe des adjacents possibles.", "output": [["Explique moi le principe des adjacents possibles.", "French grammar\n\nAdverbs themselves are generally invariable. An exception to this is the adverb tout \"wholly, very\" which agrees in gender and number with the adjective it modifies when it is in the feminine and begins with a consonant (e.g. tout petit \"very small, m.s.\", tous petits \"very small, m.pl.\" but toute petite \"very small, f.s.\", toutes petites \"very small, f.pl.\" — when beginning with a vowel however: tout entier, tout entiers, tout entière, tout entières \"completely, as a whole\" (with liaison)).\nPrepositions\nFrench prepositions link two related parts of a sentence. In word order, they are placed in front of a noun in order to specify the relationship between the noun and the verb, adjective, or other noun that precedes it. Some common French prepositions are: à (to, at, in), à côté de (next to, beside), après (after), au sujet de (about, on the subject of), avant (before), avec (with), chez (at the home/office of, among), contre (against), dans (in), d'après (according to), de (from, of, about), depuis (since, for), derrière (in back of, behind), devant (in front of), durant (during, while), en (in, on, to), en dehors de (outside of), en face de (facing, across from), entre (between), envers (toward), environ (approximately), hors de (outside of), jusque (until, up to, even), loin de (far from), malgré (despite), par (by, through), parmi (among), pendant (during), pour (for), près de (near), quant à (as for, regarding), sans (without), selon (according to), sous (under), suivant (according to), sur (on), vers (toward)."]], "corpus": "wikipedia"}
 {"tstamp": 1723214232.1113, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723214229.9382, "finish": 1723214232.1113, "ip": "", "conv_id": "0eae60be6a9a4aba93f2db3e8c73b3ac", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "is pyjamas an american word?", "output": [["is pyjamas an american word?", "English Stackexchange\n\nQ: Difference between \"pajama\", \"pyjama\" and \"paijama\" Which is the correct word: pajama, pyjama or paijama? \nIs there a difference between these three?\n\nA: Pajama is the American spelling while pyjama is the British spelling. Both mean the same.\n\nPajama/Pyjama:  loose-fitting clothes worn for sleep\n\nPaijama is the word from which pajama/pyjama has been derived. Also, the meaning (of pajama/pyjama) has been altered a bit.\n\nPaijama: loose, lightweight trousers fitted with drawstring waistbands\n\nPajama/Pyjama was incorporated into the English language during the British Rule in India.\n(Bonus: Most people in India call it either \"paijama\" or \"pajama\")\n"]], "corpus": "stackexchange"}
 {"tstamp": 1723214232.1113, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1723214229.9382, "finish": 1723214232.1113, "ip": "", "conv_id": "49f040f2e8bd479ba79bd8da5e7a97ed", "model_name": "BM25", "prompt": "is pyjamas an american word?", "output": [["is pyjamas an american word?", "English Stackexchange\n\nQ: Difference between \"pajama\", \"pyjama\" and \"paijama\" Which is the correct word: pajama, pyjama or paijama? \nIs there a difference between these three?\n\nA: Pajama is the American spelling while pyjama is the British spelling. Both mean the same.\n\nPajama/Pyjama:  loose-fitting clothes worn for sleep\n\nPaijama is the word from which pajama/pyjama has been derived. Also, the meaning (of pajama/pyjama) has been altered a bit.\n\nPaijama: loose, lightweight trousers fitted with drawstring waistbands\n\nPajama/Pyjama was incorporated into the English language during the British Rule in India.\n(Bonus: Most people in India call it either \"paijama\" or \"pajama\")\n"]], "corpus": "stackexchange"}
+{"tstamp": 1723214295.5454, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723214293.2511, "finish": 1723214295.5454, "ip": "", "conv_id": "5d900500bce240c5bbf2aef59147a9bc", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "will wout van aert ever will Paris roubaix", "output": [["will wout van aert ever will Paris roubaix", "Roubaix\n\nToponymy The current city's name is most likely derived from Frankish rausa \"reed\" and baki \"brook\". Therefore, the meaning of Roubaix can, in all likelihood, find its origin on the banks of its three historical brooks: Espierre, Trichon and Favreuil. The place was mentioned for the first time in a Latinised form in the 9th century: Villa Rusbaci. Thereafter, the following names were in use:  1047 and 1106 Rubais, 1122 Rosbays, 1166 Rusbais, 1156 and 1202 Robais, 1223 Roubais. Over the span of centuries, the name evolved to Roubaix as shown on Mercator's map of Flanders published at Leuven in 1540.\nParallel to the official and usual name Roubaix, some translations are worth a mention. Firstly, though the city has never belonged to the Flemish-speaking area, the seldom-heard renderings Robeke and Roodebeeke are documented for Roubaix. Furthermore, the Dutch Language Union established Robaais as the city's proper Dutch name. Lastly, one can cite Rosbacum as the definite Latin transcription of Roubaix which has been in use since the 19th century, as recorded on dedication statements sealed in the first stones of the foundations of the City Hall laid in 1840 and the Church of Notre Dame laid in 1842.\nHistory\nHeraldry\nPeople\nInhabitants of Roubaix are known in English as \"Roubaisians\" and in French as Roubaisiens () or in the feminine form Roubaisiennes (), also natively called Roubaignots () or in the feminine form Roubaignotes ()."]], "corpus": "wikipedia"}
+{"tstamp": 1723214295.5454, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723214293.2511, "finish": 1723214295.5454, "ip": "", "conv_id": "d406de720188454babb25d33b24c7f74", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "will wout van aert ever will Paris roubaix", "output": [["will wout van aert ever will Paris roubaix", "Wout van Aert\n\nWout van Aert (born 15 September 1994) is a Belgian professional road and cyclo-cross racer who rides for UCI WorldTeam . Van Aert won three consecutive men's races at the UCI Cyclo-cross World Championships in 2016, 2017 and 2018.\nHaving initially prioritised competing in cyclo-cross, Van Aert  terminated his contract with  in 2018 and  joined  in March 2019, on a three-year deal. Van Aert has since taken more than thirty professional road wins, including nine stage victories at the Tour de France between 2019 and 2022 (also winning the points classification in 2022), and won Milan–San Remo – one of the five Cycling monuments – in 2020."]], "corpus": "wikipedia"}
+{"tstamp": 1723214298.1618, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1723214295.8043, "finish": 1723214298.1618, "ip": "", "conv_id": "b9286918cc114d8bb1a337f3424394b8", "model_name": "voyage-multilingual-2", "prompt": "Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "output": [["Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "Title: MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis\n\nAbstract: Recently, numerous embedding models have been made available and widely used for various NLP tasks. The Massive Text Embedding Benchmark (MTEB) has primarily simplified the process of choosing a model that performs well for several tasks in English, but extensions to other languages remain challenging. This is why we expand MTEB to propose the first massive benchmark of sentence embeddings for French. We gather 15 existing datasets in an easy-to-use interface and create three new French datasets for a global evaluation of 8 task categories. We compare 51 carefully selected embedding models on a large scale, conduct comprehensive statistical tests, and analyze the correlation between model performance and many of their characteristics. We find out that even if no model is the best on all tasks, large multilingual models pre-trained on sentence similarity perform exceptionally well. Our work comes with open-source code, new datasets and a public leaderboard."]], "corpus": "arxiv"}
+{"tstamp": 1723214298.1618, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723214295.8043, "finish": 1723214298.1618, "ip": "", "conv_id": "52edf7a1ae264368a62d5b66e4d9b210", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "output": [["Quel est le modèle de text embedding le plus performant pour travailler sur des documents en français et en anglais et qui utilise au maximum 8 GB de mémoire vive en FP32 ?", "Title: FlauBERT: Unsupervised Language Model Pre-training for French\n\nAbstract: Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP."]], "corpus": "arxiv"}

data/sts_battle-5b78c89f-244c-4bc4-9693-2c3c7acb6977.jsonl

@@ -13,3 +13,5 @@
 {"tstamp": 1723182130.3022, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "182e6b09b00e4be5bedfe9b1ab5ca097", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "A male uses paint to stencil words on  a circular sign laying horizontal yet made to be erected.", "0_txt1": "A woman is writing on a napkin and showing it to her friend.", "0_txt2": "A man is painting letters using a stencil onto a round stand-up sign that is lying sideways.", "0_output": "", "1_conv_id": "4e0339f112a6422ab90386d3d7ab4bf0", "1_model_name": "voyage-multilingual-2", "1_txt0": "A male uses paint to stencil words on  a circular sign laying horizontal yet made to be erected.", "1_txt1": "A woman is writing on a napkin and showing it to her friend.", "1_txt2": "A man is painting letters using a stencil onto a round stand-up sign that is lying sideways.", "1_output": ""}
 {"tstamp": 1723182262.1573, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "90f207ba23e04430a9d52923b112eecc", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_txt0": "the man in workboots walked alone", "0_txt1": "A male and female, both wearing sandals, walking by a fence.", "0_txt2": "people are wearing sandals", "0_output": "", "1_conv_id": "7563f7581d314345b4b17c0caf08e758", "1_model_name": "text-embedding-004", "1_txt0": "the man in workboots walked alone", "1_txt1": "A male and female, both wearing sandals, walking by a fence.", "1_txt2": "people are wearing sandals", "1_output": ""}
 {"tstamp": 1723182734.7639, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d0aa7aff91f7446b8874ab2427fd8a74", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "An old man in a pink polo looks through a telescope with a small child standing next to him.", "0_txt1": "An old elephant in a pink polo looks through a telescope with a small child standing next to him", "0_txt2": "An old man in a pink polo looks through a telescope with a small child standing next to him while others stand with their telescopes in the background.", "0_output": "", "1_conv_id": "a53cdafd961c4d37a5312cfef681550e", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_txt0": "An old man in a pink polo looks through a telescope with a small child standing next to him.", "1_txt1": "An old elephant in a pink polo looks through a telescope with a small child standing next to him", "1_txt2": "An old man in a pink polo looks through a telescope with a small child standing next to him while others stand with their telescopes in the background.", "1_output": ""}
+{"tstamp": 1723214467.0138, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bb21844231484a559f61dd6a3afacbad", "0_model_name": "text-embedding-004", "0_txt0": "That's a good attitude!", "0_txt1": "That is quite a fine stance!", "0_txt2": "That is a negative and unwanted perspective.", "0_output": "", "1_conv_id": "51db5c8bfb4747d984ca6e989a7f281a", "1_model_name": "GritLM/GritLM-7B", "1_txt0": "That's a good attitude!", "1_txt1": "That is quite a fine stance!", "1_txt2": "That is a negative and unwanted perspective.", "1_output": ""}
+{"tstamp": 1723214519.003, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "6620285caa7742f08d73a727e21c2342", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_txt0": "Two men standing together are dressed up.", "0_txt1": "A man with glasses wearing a tuxedo and corsage stands near a man in a suit at a formal event.", "0_txt2": "The two men are wearing surfer shorts and tank tops.", "0_output": "", "1_conv_id": "efa40af5ec1a4f5eb5e019f97b8824dd", "1_model_name": "voyage-multilingual-2", "1_txt0": "Two men standing together are dressed up.", "1_txt1": "A man with glasses wearing a tuxedo and corsage stands near a man in a suit at a formal event.", "1_txt2": "The two men are wearing surfer shorts and tank tops.", "1_output": ""}

data/sts_individual-5b78c89f-244c-4bc4-9693-2c3c7acb6977.jsonl

@@ -38,3 +38,7 @@
 {"tstamp": 1723182592.7579, "task_type": "sts", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723182592.7121, "finish": 1723182592.7579, "ip": "", "conv_id": "f11e369d686240f4a04a085dac5e8c8a", "model_name": "Salesforce/SFR-Embedding-2_R", "txt0": "இசை", "txt1": "ஒலி", "txt2": "இசைக்குழு", "output": ""}
 {"tstamp": 1723182705.452, "task_type": "sts", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723182705.4056, "finish": 1723182705.452, "ip": "", "conv_id": "d0aa7aff91f7446b8874ab2427fd8a74", "model_name": "nomic-ai/nomic-embed-text-v1.5", "txt0": "An old man in a pink polo looks through a telescope with a small child standing next to him.", "txt1": "An old elephant in a pink polo looks through a telescope with a small child standing next to him", "txt2": "An old man in a pink polo looks through a telescope with a small child standing next to him while others stand with their telescopes in the background.", "output": ""}
 {"tstamp": 1723182705.452, "task_type": "sts", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723182705.4056, "finish": 1723182705.452, "ip": "", "conv_id": "a53cdafd961c4d37a5312cfef681550e", "model_name": "intfloat/e5-mistral-7b-instruct", "txt0": "An old man in a pink polo looks through a telescope with a small child standing next to him.", "txt1": "An old elephant in a pink polo looks through a telescope with a small child standing next to him", "txt2": "An old man in a pink polo looks through a telescope with a small child standing next to him while others stand with their telescopes in the background.", "output": ""}
+{"tstamp": 1723214428.0254, "task_type": "sts", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1723214427.8014, "finish": 1723214428.0254, "ip": "", "conv_id": "bb21844231484a559f61dd6a3afacbad", "model_name": "text-embedding-004", "txt0": "That's a good attitude!", "txt1": "That is quite a fine stance!", "txt2": "That is a negative and unwanted perspective.", "output": ""}
+{"tstamp": 1723214428.0254, "task_type": "sts", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723214427.8014, "finish": 1723214428.0254, "ip": "", "conv_id": "51db5c8bfb4747d984ca6e989a7f281a", "model_name": "GritLM/GritLM-7B", "txt0": "That's a good attitude!", "txt1": "That is quite a fine stance!", "txt2": "That is a negative and unwanted perspective.", "output": ""}
+{"tstamp": 1723214492.676, "task_type": "sts", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723214492.3542, "finish": 1723214492.676, "ip": "", "conv_id": "6620285caa7742f08d73a727e21c2342", "model_name": "jinaai/jina-embeddings-v2-base-en", "txt0": "Two men standing together are dressed up.", "txt1": "A man with glasses wearing a tuxedo and corsage stands near a man in a suit at a formal event.", "txt2": "The two men are wearing surfer shorts and tank tops.", "output": ""}
+{"tstamp": 1723214492.676, "task_type": "sts", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1723214492.3542, "finish": 1723214492.676, "ip": "", "conv_id": "efa40af5ec1a4f5eb5e019f97b8824dd", "model_name": "voyage-multilingual-2", "txt0": "Two men standing together are dressed up.", "txt1": "A man with glasses wearing a tuxedo and corsage stands near a man in a suit at a formal event.", "txt2": "The two men are wearing surfer shorts and tank tops.", "output": ""}