Fix tables

README.md

@@ -117,12 +117,14 @@ We performed a number of small benchmarks to assess both the changes in 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 changein 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 |
 
@@ -131,6 +133,7 @@ To test the English out-of-domain performance, we used the test split of various
 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% |