| --- |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-similarity |
| - sentence-transformers |
| license: mit |
| language: |
| - multilingual |
| - af |
| - am |
| - ar |
| - as |
| - az |
| - be |
| - bg |
| - bn |
| - br |
| - bs |
| - ca |
| - cs |
| - cy |
| - da |
| - de |
| - el |
| - en |
| - eo |
| - es |
| - et |
| - eu |
| - fa |
| - fi |
| - fr |
| - fy |
| - ga |
| - gd |
| - gl |
| - gu |
| - ha |
| - he |
| - hi |
| - hr |
| - hu |
| - hy |
| - id |
| - is |
| - it |
| - ja |
| - jv |
| - ka |
| - kk |
| - km |
| - kn |
| - ko |
| - ku |
| - ky |
| - la |
| - lo |
| - lt |
| - lv |
| - mg |
| - mk |
| - ml |
| - mn |
| - mr |
| - ms |
| - my |
| - ne |
| - nl |
| - no |
| - om |
| - or |
| - pa |
| - pl |
| - ps |
| - pt |
| - ro |
| - ru |
| - sa |
| - sd |
| - si |
| - sk |
| - sl |
| - so |
| - sq |
| - sr |
| - su |
| - sv |
| - sw |
| - ta |
| - te |
| - th |
| - tl |
| - tr |
| - ug |
| - uk |
| - ur |
| - uz |
| - vi |
| - xh |
| - yi |
| - zh |
| --- |
| |
| A quantized version of [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). Quantization was performed per-layer under the same conditions as our ELSERv2 model, as described [here](https://www.elastic.co/search-labs/blog/articles/introducing-elser-v2-part-1#quantization). |
|
|
| [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
| Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
|
|
| ## Benchmarks |
|
|
| We performed a number of small benchmarks to assess both the changes in quality as well as inference latency against the baseline original model. |
|
|
| ### Quality |
|
|
| Measuring NDCG@10 using the dev split of the MIRACL datasets for select languages, we see mostly a marginal change in quality of the quantized model. |
|
|
| | | de | yo| ru | ar | es | th | |
| | --- | --- | ---| --- | --- | --- | --- | |
| | multilingual-e5-small | 0.75862 | 0.56193 | 0.80309 | 0.82778 | 0.81672 | 0.85072 | |
| | multilingual-e5-small-optimized | 0.75992 | 0.48934 | 0.79668 | 0.82017 | 0.8135 | 0.84316 | |
|
|
| To test the English out-of-domain performance, we used the test split of various datasets in the BEIR evaluation. Measuring NDCG@10, we see a larger change in SCIFACT, but marginal in the other datasets evaluated. |
|
|
| | | FIQA | SCIFACT | nfcorpus | |
| | --- | --- | --- | --- | |
| | multilingual-e5-small | 0.33126 | 0.677 | 0.31004 | |
| | multilingual-e5-small-optimized | 0.31734 | 0.65484 | 0.30126 | |
|
|
| ### Performance |
|
|
| Using a PyTorch model traced for Linux and Intel CPUs, we performed performance benchmarking with various lengths of input. Overall, we see on average a 50-20% performance improvement with the optimized model. |
|
|
| | input length (characters) | multilingual-e5-small | multilingual-e5-small-optimized | speedup | |
| | --- | --- | --- | --- | |
| | 0 - 50 | 0.0181 | 0.00826 | 54.36% | |
| | 50 - 100 | 0.0275 | 0.0164 | 40.36% | |
| | 100 - 150 | 0.0366 | 0.0237 | 35.25% | |
| | 150 - 200 | 0.0435 | 0.0301 | 30.80% | |
| | 200 - 250 | 0.0514 | 0.0379 | 26.26% | |
| | 250 - 300 | 0.0569 | 0.043 | 24.43% | |
| | 300 - 350 | 0.0663 | 0.0513 | 22.62% | |
| | 350 - 400 | 0.0737 | 0.0576 | 21.85% | |
|
|
| ### Disclaimer |
|
|
| This e5 model, as defined, hosted, integrated and used in conjunction with our other Elastic Software is covered by our standard warranty. |
|
|
