RAG
Collection
the best collection of RAG model, like embedding, ranker etc.
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1 item
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Updated
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A try for emebdding model:
The method is the same as the stella-v2, I just extend the length of the context on tao.(I found if you want to use the fully-8k context, you maybe need to convert the model to float32).
Now I'm working on the tao-v2, It will have a different sturcture.
I will release tao-v2 as fast as I can.
Thank you to the open source community.
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Collection including amu/tao-8k
Evaluation results
- cos_sim_pearson on MTEB AFQMCvalidation set self-reported47.336
- cos_sim_spearman on MTEB AFQMCvalidation set self-reported49.940
- euclidean_pearson on MTEB AFQMCvalidation set self-reported48.120
- euclidean_spearman on MTEB AFQMCvalidation set self-reported49.940
- manhattan_pearson on MTEB AFQMCvalidation set self-reported48.074
- manhattan_spearman on MTEB AFQMCvalidation set self-reported49.892
- cos_sim_pearson on MTEB ATECtest set self-reported50.976
- cos_sim_spearman on MTEB ATECtest set self-reported53.112
- euclidean_pearson on MTEB ATECtest set self-reported55.120
- euclidean_spearman on MTEB ATECtest set self-reported53.112
- manhattan_pearson on MTEB ATECtest set self-reported55.097
- manhattan_spearman on MTEB ATECtest set self-reported53.107
- accuracy on MTEB AmazonReviewsClassification (zh)test set self-reported40.804
- f1 on MTEB AmazonReviewsClassification (zh)test set self-reported39.011
- cos_sim_pearson on MTEB BQtest set self-reported62.844
- cos_sim_spearman on MTEB BQtest set self-reported65.541
- euclidean_pearson on MTEB BQtest set self-reported64.088
- euclidean_spearman on MTEB BQtest set self-reported65.541
- manhattan_pearson on MTEB BQtest set self-reported64.094
- manhattan_spearman on MTEB BQtest set self-reported65.554
- v_measure on MTEB CLSClusteringP2Ptest set self-reported40.028
- v_measure on MTEB CLSClusteringS2Stest set self-reported38.190
- map on MTEB CMedQAv1test set self-reported85.343
- mrr on MTEB CMedQAv1test set self-reported88.038
- map on MTEB CMedQAv2test set self-reported85.813
- mrr on MTEB CMedQAv2test set self-reported88.521
- map_at_1 on MTEB CmedqaRetrievalself-reported23.982
- map_at_10 on MTEB CmedqaRetrievalself-reported36.210
- map_at_100 on MTEB CmedqaRetrievalself-reported38.072
- map_at_1000 on MTEB CmedqaRetrievalself-reported38.194
- map_at_3 on MTEB CmedqaRetrievalself-reported32.239
- map_at_5 on MTEB CmedqaRetrievalself-reported34.377
- mrr_at_1 on MTEB CmedqaRetrievalself-reported36.859
- mrr_at_10 on MTEB CmedqaRetrievalself-reported45.085
- mrr_at_100 on MTEB CmedqaRetrievalself-reported46.104
- mrr_at_1000 on MTEB CmedqaRetrievalself-reported46.154
- mrr_at_3 on MTEB CmedqaRetrievalself-reported42.623
- mrr_at_5 on MTEB CmedqaRetrievalself-reported43.995
- ndcg_at_1 on MTEB CmedqaRetrievalself-reported36.859
- ndcg_at_10 on MTEB CmedqaRetrievalself-reported42.735
- ndcg_at_100 on MTEB CmedqaRetrievalself-reported50.181
- ndcg_at_1000 on MTEB CmedqaRetrievalself-reported52.309
- ndcg_at_3 on MTEB CmedqaRetrievalself-reported37.728
- ndcg_at_5 on MTEB CmedqaRetrievalself-reported39.664
- precision_at_1 on MTEB CmedqaRetrievalself-reported36.859
- precision_at_10 on MTEB CmedqaRetrievalself-reported9.615
- precision_at_100 on MTEB CmedqaRetrievalself-reported1.564
- precision_at_1000 on MTEB CmedqaRetrievalself-reported0.183
- precision_at_3 on MTEB CmedqaRetrievalself-reported21.514
- precision_at_5 on MTEB CmedqaRetrievalself-reported15.569
- recall_at_1 on MTEB CmedqaRetrievalself-reported23.982
- recall_at_10 on MTEB CmedqaRetrievalself-reported53.046
- recall_at_100 on MTEB CmedqaRetrievalself-reported84.113
- recall_at_1000 on MTEB CmedqaRetrievalself-reported98.370
- recall_at_3 on MTEB CmedqaRetrievalself-reported37.825
- recall_at_5 on MTEB CmedqaRetrievalself-reported44.023
- cos_sim_accuracy on MTEB Cmnlivalidation set self-reported77.715
- cos_sim_ap on MTEB Cmnlivalidation set self-reported86.845
- cos_sim_f1 on MTEB Cmnlivalidation set self-reported79.320
- cos_sim_precision on MTEB Cmnlivalidation set self-reported72.706
- cos_sim_recall on MTEB Cmnlivalidation set self-reported87.257
- dot_accuracy on MTEB Cmnlivalidation set self-reported77.715
- dot_ap on MTEB Cmnlivalidation set self-reported86.839
- dot_f1 on MTEB Cmnlivalidation set self-reported79.320
- dot_precision on MTEB Cmnlivalidation set self-reported72.706
- dot_recall on MTEB Cmnlivalidation set self-reported87.257
- euclidean_accuracy on MTEB Cmnlivalidation set self-reported77.715
- euclidean_ap on MTEB Cmnlivalidation set self-reported86.845
- euclidean_f1 on MTEB Cmnlivalidation set self-reported79.320
- euclidean_precision on MTEB Cmnlivalidation set self-reported72.706
- euclidean_recall on MTEB Cmnlivalidation set self-reported87.257
- manhattan_accuracy on MTEB Cmnlivalidation set self-reported77.811
- manhattan_ap on MTEB Cmnlivalidation set self-reported86.811
- manhattan_f1 on MTEB Cmnlivalidation set self-reported79.420
- manhattan_precision on MTEB Cmnlivalidation set self-reported72.536
- manhattan_recall on MTEB Cmnlivalidation set self-reported87.748
- max_accuracy on MTEB Cmnlivalidation set self-reported77.811
- max_ap on MTEB Cmnlivalidation set self-reported86.845
- max_f1 on MTEB Cmnlivalidation set self-reported79.420