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type: cosine_recall value: 79.4987 - type: cosine_ap value: 79.1752 - type: manhattan_accuracy value: 87.8107 - type: manhattan_accuracy_threshold value: 2587.3787 - type: manhattan_f1 value: 72.8159 - type: manhattan_f1_threshold value: 2713.7127 - type: manhattan_precision value: 69.176 - type: manhattan_recall value: 76.8602 - type: manhattan_ap value: 79.1243 - type: euclidean_accuracy value: 87.7988 - type: euclidean_accuracy_threshold value: 71.0715 - type: euclidean_f1 value: 72.9982 - type: euclidean_f1_threshold value: 76.0618 - type: euclidean_precision value: 67.4804 - type: euclidean_recall value: 79.4987 - type: euclidean_ap value: 79.1752 - type: dot_accuracy value: 87.7988 - type: dot_accuracy_threshold value: 74.7442 - type: dot_f1 value: 72.9982 - type: dot_f1_threshold value: 71.073 - type: dot_precision value: 67.4804 - type: dot_recall value: 79.4987 - type: dot_ap value: 79.1752 - type: max_accuracy value: 87.8107 - type: max_f1 value: 72.9982 - type: max_precision value: 69.176 - type: max_recall value: 79.4987 - type: max_ap value: 79.1752 - type: main_score value: 79.1752 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: similarity_accuracy value: 89.4749 - type: similarity_accuracy_threshold value: 73.03659999999999 - type: similarity_f1 value: 79.4416 - type: similarity_f1_threshold value: 70.7413 - type: similarity_precision value: 75.9696 - type: similarity_recall value: 83.2461 - type: similarity_ap value: 87.1156 - type: cosine_accuracy value: 89.4749 - type: cosine_accuracy_threshold value: 73.03659999999999 - type: cosine_f1 value: 79.4416 - type: cosine_f1_threshold value: 70.7413 - type: cosine_precision value: 75.9696 - type: cosine_recall value: 83.2461 - type: cosine_ap value: 87.1156 - type: manhattan_accuracy value: 89.471 - type: manhattan_accuracy_threshold value: 2630.4434 - type: manhattan_f1 value: 79.4078 - type: manhattan_f1_threshold value: 2764.4342 - type: manhattan_precision value: 75.7675 - type: manhattan_recall value: 83.4155 - type: manhattan_ap value: 87.0938 - type: euclidean_accuracy value: 89.4749 - type: euclidean_accuracy_threshold value: 73.4349 - type: euclidean_f1 value: 79.4416 - type: euclidean_f1_threshold value: 76.4966 - type: euclidean_precision value: 75.9696 - type: euclidean_recall value: 83.2461 - type: euclidean_ap value: 87.1156 - type: dot_accuracy value: 89.4749 - type: dot_accuracy_threshold value: 73.03659999999999 - type: dot_f1 value: 79.4416 - type: dot_f1_threshold value: 70.7413 - type: dot_precision value: 75.9696 - type: dot_recall value: 83.2461 - type: dot_ap value: 87.1156 - type: max_accuracy value: 89.4749 - type: max_f1 value: 79.4416 - type: max_precision value: 75.9696 - type: max_recall value: 83.4155 - type: max_ap value: 87.1156 - type: main_score value: 87.1156 task: type: PairClassification tags: - mteb --- # voyage-3-m-exp This repo contains the tokenizer and evaluation results of the `voyage-3-m-exp` embedding model. `voyage-3-m-exp` is an intermediate snapshot of voyage general-purpose embeddings that are tailored to datasets similar to MTEB. The training sets of MTEB datasets are used in training `voyage-3-m-exp`. **We note that for production use cases, `voyage-3-large` is highly recommended and likey strictly better than `voyage-3-m-exp`. Please see the [blogpost](https://blog.voyageai.com/2025/01/07/voyage-3-large) for more information about `voyage-3-large`.** `voyage-3-m-exp` can be accessed via the [Voyage API](https://docs.voyageai.com/docs/embeddings) with the model name `"voyage-3-m-exp"`. ## Reproduction of MTEB results Similar to most open source models on top of the leaderboard, `voyage-3-m-exp` uses task-specific prompts. To reproduce MTEB results on the leaderboard, please set `input_type` of the API to `None` and prepend the following prompts to the input. For retrieval tasks, please use only `text` without adding the `title` field in front of the text. ```python { "BIOSSES" : { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "SICK-R": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS12": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS13": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS14": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS15": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS16": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STSBenchmark": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS17": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS22": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "AmazonCounterfactualClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonPolarityClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonReviewsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "Banking77Classification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "EmotionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ImdbClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveScenarioClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPDomainClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ToxicConversationsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "TweetSentimentExtractionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ArxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "ArxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "TwentyNewsgroupsClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "SprintDuplicateQuestions": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterSemEval2015": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterURLCorpus": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'StackOverflowDupQuestions': { 'query': 'Represent the sentence to retrieve similar sentences: ', 'document': 'Represent the sentence to retrieve similar sentences: ', }, 'SciDocsRR': { 'query': 'Represent the paper title to retrieve relevant paper titles: ', 'document': 'Represent the paper title to retrieve relevant paper titles: ', }, 'MindSmallReranking': { 'query': 'Represent the query to retrieve relevant documents: ', 'document': 'Represent the document for retrieval: ', }, 'AskUbuntuDupQuestions': { 'query': 'Represent the question to retrieve similar questions: ', 'document': 'Represent the question to retrieve similar questions: ', }, "SummEval": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'ClimateFEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'HotpotQA': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'FEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'MSMARCO': { 'query': 'Represent the question for retrieving evidence documents: ', 'document': 'Represent the document for retrieval: ', }, 'DBPedia': { 'query': 'Represent the entity-based query for retrieving relevant articles: ', 'document': 'Represent the article for retrieval: ', }, 'NQ': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'QuoraRetrieval': { 'query': 'Represent the quora question for retrieving similar questions: ', 'document': 'Represent the question for retrieval: ', }, 'SCIDOCS': { 'query': 'Represent the paper title for retrieving possible citation documents: ', 'document': 'Represent the scitific paper document for retrieval: ', }, 'TRECCOVID': { 'query': 'Represent the query for retrieving supporting articles: ', 'document': 'Represent the article for retrieval: ', }, 'Touche2020': { 'query': 'Represent the question for retrieving supporting documents: ', 'document': 'Represent the document for retrieval: ', }, "SciFact": { "query": "Represent the scientific fact for retrieving supporting document: ", "document": "Represent the document for retrieval: " }, 'NFCorpus': { 'query': 'Represent the nutrition fact for retrieving evidence documents: ', 'document': 'Represent the pubmed document for retrieval: ', }, 'ArguAna': { 'query': 'Represent the argument for retrieving counterarguments: ', 'document': 'Represent the conterargument for retrieval: ', }, 'FiQA2018': { 'query': "Represent the query for retrieving supporting documents: ", 'document': "Represent the document for retrieval: ", } } ```