Addressed CR. added note of errors to openai int8 indexes.

docs/regressions/regressions-dl19-passage-openai-ada2-int8.md

@@ -1,5 +1,7 @@
 # Anserini Regressions: TREC 2019 Deep Learning Track (Passage)
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [TREC 2019 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper:

docs/regressions/regressions-dl20-passage-openai-ada2-int8.md

@@ -1,5 +1,7 @@
 # Anserini Regressions: TREC 2020 Deep Learning Track (Passage)
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [TREC 2020 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper:

docs/regressions/regressions-msmarco-passage-openai-ada2-int8.md

@@ -1,5 +1,7 @@
 # Anserini Regressions: MS MARCO Passage Ranking
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [MS MARCO passage ranking task](https://github.com/microsoft/MSMARCO-Passage-Ranking), as described in the following paper:

src/main/java/io/anserini/index/IndexCollection.java

@@ -177,7 +177,7 @@ public IndexCollection(Args args) throws Exception {
     }
 
     final Directory dir = FSDirectory.open(Paths.get(args.index));
-    final IndexWriterConfig config = new IndexWriterConfig(getAnalyzer()).setCodec(new Lucene99Codec());
+    final IndexWriterConfig config = new IndexWriterConfig(getAnalyzer());
 
     if (args.bm25Accurate) {
       // Necessary during indexing as the norm used in BM25 is already determined at index time.

src/main/java/io/anserini/index/IndexInvertedDenseVectors.java

@@ -100,7 +100,7 @@ public IndexInvertedDenseVectors(Args args) {
 
     try {
       final Directory dir = FSDirectory.open(Paths.get(args.index));
-      final IndexWriterConfig config = new IndexWriterConfig(analyzer).setCodec(new Lucene99Codec());
+      final IndexWriterConfig config = new IndexWriterConfig(analyzer);
       config.setOpenMode(IndexWriterConfig.OpenMode.CREATE);
       config.setRAMBufferSizeMB(args.memoryBuffer);
       config.setUseCompoundFile(false);

src/main/resources/docgen/templates/dl19-passage-openai-ada2-int8.template

@@ -1,5 +1,7 @@
 # Anserini Regressions: TREC 2019 Deep Learning Track (Passage)
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [TREC 2019 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper:

src/main/resources/docgen/templates/dl20-passage-openai-ada2-int8.template

@@ -1,5 +1,7 @@
 # Anserini Regressions: TREC 2020 Deep Learning Track (Passage)
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [TREC 2020 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper:

src/main/resources/docgen/templates/msmarco-passage-openai-ada2-int8.template

@@ -1,5 +1,7 @@
 # Anserini Regressions: MS MARCO Passage Ranking
 
+**NOTE:** We're currently having issues with this regression, which throws "Retried waiting for GCLocker too often" errors.
+
 **Model**: OpenAI-ada2 embeddings (using pre-encoded queries) with HNSW indexes
 
 This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the [MS MARCO passage ranking task](https://github.com/microsoft/MSMARCO-Passage-Ranking), as described in the following paper: