A quantized version of multilingual-e5-small. Quantization was performed per-layer under the same conditions as our ELSERv2 model, as described here.
Text Embeddings by Weakly-Supervised Contrastive Pre-training. 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
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