Datasets:
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README.md
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@@ -30,6 +30,43 @@ All embeddings are created using [Snowflake's Arctic Embed M v1.5](https://huggi
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It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More details on model release can be found in this [blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/) along with methods for [quantization and compression](https://github.com/Snowflake-Labs/arctic-embed/blob/main/compressed_embeddings_examples/score_arctic_embed_m_v1dot5_with_quantization.ipynb).
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Note, that the embeddings are not normalized so you will need to normalize them before usage.
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## Loading the dataset
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### Loading the document embeddings
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It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More details on model release can be found in this [blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/) along with methods for [quantization and compression](https://github.com/Snowflake-Labs/arctic-embed/blob/main/compressed_embeddings_examples/score_arctic_embed_m_v1dot5_with_quantization.ipynb).
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Note, that the embeddings are not normalized so you will need to normalize them before usage.
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## Retrieval Performance
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Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both systems, retrieval is at the segment level and Doc Score = Max (passage score).
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Retrieval is done via a dot product and happens in BF16. Since the M-v1.5 model supports Vector Truncation we do so to 256 dimensions
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### NDCG@10
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| Dataset | BM25 | Arctic-M-V1.5 (768 Dimensions) | Arctic-M-V1.5 (256 Dimensions) |
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| Deep Learning 2021 | 0.5778 | 0.6936 | 0.69392 |
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| Deep Learning 2022 | 0.3576 | 0.55199 | 0.55608 |
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| Deep Learning 2023 | 0.3356 | 0.46963 | 0.45196 |
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| msmarcov2-dev | N/A | 0.346 | 0.34074 |
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| msmarcov2-dev2 | N/A | 0.34518 | 0.34339 |
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| Raggy Queries | 0.4227 | 0.57439 | 0.56686 |
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### Recall@100
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| Dataset | BM25 | Arctic-M-V1.5 (768 Dimensions) | Arctic-M-V1.5 (256 Dimensions) |
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| Deep Learning 2021 | 0.3811 | 0.43 | 0.42245 |
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| Deep Learning 2022 | 0.233 | 0.32125 | 0.3165 |
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| Deep Learning 2023 | 0.3049 | 0.37622 | 0.36089 |
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| msmarcov2-dev | 0.6683 | 0.85435 | 0.84985 |
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| msmarcov2-dev2 | 0.6771 | 0.8576 | 0.8526 |
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| Raggy Queries | 0.2807 | 0.36915 | 0.36149 |
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### Recall@1000
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| Dataset | BM25 | Arctic-M-V1.5 (768 Dimensions) | Arctic-M-V1.5 (256 Dimensions) |
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| Deep Learning 2021 | 0.7115 | 0.74895 | 0.73511 |
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| Deep Learning 2022 | 0.479 | 0.55413 | 0.54499 |
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| Deep Learning 2023 | 0.5852 | 0.62262 | 0.61199 |
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| msmarcov2-dev | 0.8528 | 0.94156 | 0.94014 |
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| msmarcov2-dev2 | 0.8577 | 0.94277 | 0.94047 |
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| Raggy Queries | 0.5745 | 0.64527 | 0.63826 |
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## Loading the dataset
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### Loading the document embeddings
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