Update README.md

xhluca · Oct 16, 2024 · e1b39e5 · e1b39e5
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Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/README.md b/README.md
@@ -410,7 +410,7 @@ Using the `index_nq.py` to create an index, we can retrieve with:
 * `examples/retrieve_nq.py`: setting `mmap=False` in the `main` function to load the index in memory, and `mmap=True` to load the index as a memory-mapped file. 
 * `examples/retrieve_nq_with_batching.py`: This takes it a step further by batching the retrieval process, which allows for reloading the index after each batch (see *Mmap+Reload* below). This is useful when you have a large index and want to save memory.
 
-we show the following results on the NQ dataset (2M+ documents, 100M+ tokens):
+We show the following results on the NQ dataset (2M+ documents, 100M+ tokens):
 
 | Method        | Load Index (s) | Retrieval (s) | RAM post-index (GB) | RAM post-retrieve (GB) |
 | ------------- | -------------- | ------------- | ------------------- | ---------------------- |
@@ -420,7 +420,7 @@ we show the following results on the NQ dataset (2M+ documents, 100M+ tokens):
 
 We can see that memory-mapping the index allows for a significant reduction in memory usage, with comparable retrieval times. 
 
-Similarly, for MSMARCO (8M+ documents, 300M+ tokens), we show the following results (running on the validation set), although the retrieval did not complete for the in-memory case (* denotes that the retrieval did not terminate):
+Similarly, for MSMARCO (8M+ documents, 300M+ tokens), we show the following results (running on the validation set), although the retrieval did not complete for the in-memory case:
 
 | Method        | Load Index (s) | Retrieval (s) | RAM post-index (GB) | RAM post-retrieve (GB) |
 | ------------- | -------------- | ------------- | ------------------- | ---------------------- |