diff --git "a/model_card.md" "b/model_card.md" new file mode 100644--- /dev/null +++ "b/model_card.md" @@ -0,0 +1,8895 @@ +--- +model-index: +- name: voyageai/voyage-3-m-exp + results: + - dataset: + config: en + name: MTEB AmazonCounterfactualClassification (en) + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + split: test + type: mteb/amazon_counterfactual + metrics: + - type: accuracy + value: 95.7761 + - type: f1 + value: 93.8227 + - type: f1_weighted + value: 95.9368 + - type: ap + value: 82.63589999999999 + - type: ap_weighted + value: 82.63589999999999 + - type: main_score + value: 95.7761 + task: + type: Classification + - dataset: + config: default + name: MTEB AmazonPolarityClassification (default) + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + split: test + type: mteb/amazon_polarity + metrics: + - type: accuracy + value: 97.7143 + - type: f1 + value: 97.7143 + - type: f1_weighted + value: 97.7143 + - type: ap + value: 96.5356 + - type: ap_weighted + value: 96.5356 + - 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type: nauc_mrr_at_1000_diff1 + value: 6.7397 + - type: main_score + value: 39.385999999999996 + task: + type: Retrieval + - dataset: + config: default + name: MTEB ToxicConversationsClassification (default) + revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de + split: test + type: mteb/toxic_conversations_50k + metrics: + - type: accuracy + value: 97.5928 + - type: f1 + value: 92.9042 + - type: f1_weighted + value: 97.7586 + - type: ap + value: 77.5261 + - type: ap_weighted + value: 77.5261 + - type: main_score + value: 97.5928 + task: + type: Classification + - dataset: + config: default + name: MTEB TweetSentimentExtractionClassification (default) + revision: d604517c81ca91fe16a244d1248fc021f9ecee7a + split: test + type: mteb/tweet_sentiment_extraction + metrics: + - type: accuracy + value: 88.2343 + - type: f1 + value: 88.4321 + - type: f1_weighted + value: 88.13069999999999 + - type: main_score + value: 88.2343 + task: + type: Classification + - dataset: + config: default + name: MTEB TwentyNewsgroupsClustering (default) + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + split: test + type: mteb/twentynewsgroups-clustering + metrics: + - type: v_measure + value: 83.486 + - type: v_measure_std + value: 2.0008000000000004 + - type: main_score + value: 83.486 + task: + type: Clustering + - dataset: + config: default + name: MTEB TwitterSemEval2015 (default) + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + split: test + type: mteb/twittersemeval2015-pairclassification + metrics: + - type: similarity_accuracy + value: 87.7988 + - type: similarity_accuracy_threshold + value: 74.7442 + - type: similarity_f1 + value: 72.9982 + - type: similarity_f1_threshold + value: 71.073 + - type: similarity_precision + value: 67.4804 + - type: similarity_recall + value: 79.4987 + - type: similarity_ap + value: 79.1752 + - type: cosine_accuracy + value: 87.7988 + - type: cosine_accuracy_threshold + value: 74.7442 + - type: cosine_f1 + value: 72.9982 + - type: cosine_f1_threshold + value: 71.073 + - type: cosine_precision + value: 67.4804 + - 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 of the `voyage-3-m-exp` embedding model. `voyage-3-m-exp` is an intermediate snapshot of voyage generalist embeddings optimized for academic retrieval. **For production use cases, `voyage-3-large` is highly recommended and strictly better than `voyage-3-m-exp`.** Please see the [blogpost]() for more information about `voyage-3-large`. + +`voyage-3-m-exp` can be accessed via the [Voyage API](https://docs.voyageai.com/docs/embeddings). + +## 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: ", + } +} +``` \ No newline at end of file