From b87daed62b9b283f588a41e1d6c77029f12f5b04 Mon Sep 17 00:00:00 2001 From: Markus Paff Date: Tue, 13 Apr 2021 09:45:04 +0200 Subject: [PATCH] fixed link to dpr (#962) --- docs/_src/benchmarks/retriever_performance.json | 2 +- docs/v0.4.0/_src/benchmarks/retriever_performance.json | 2 +- docs/v0.5.0/_src/benchmarks/retriever_performance.json | 2 +- docs/v0.6.0/_src/benchmarks/retriever_performance.json | 2 +- docs/v0.7.0/_src/benchmarks/retriever_performance.json | 2 +- test/benchmarks/templates.py | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/_src/benchmarks/retriever_performance.json b/docs/_src/benchmarks/retriever_performance.json index d5075ca3c..07f8a3b41 100644 --- a/docs/_src/benchmarks/retriever_performance.json +++ b/docs/_src/benchmarks/retriever_performance.json @@ -2,7 +2,7 @@ "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model", diff --git a/docs/v0.4.0/_src/benchmarks/retriever_performance.json b/docs/v0.4.0/_src/benchmarks/retriever_performance.json index 1def70d39..19cfa6bd8 100644 --- a/docs/v0.4.0/_src/benchmarks/retriever_performance.json +++ b/docs/v0.4.0/_src/benchmarks/retriever_performance.json @@ -2,7 +2,7 @@ "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model", diff --git a/docs/v0.5.0/_src/benchmarks/retriever_performance.json b/docs/v0.5.0/_src/benchmarks/retriever_performance.json index 944eac526..ab0d8fe60 100644 --- a/docs/v0.5.0/_src/benchmarks/retriever_performance.json +++ b/docs/v0.5.0/_src/benchmarks/retriever_performance.json @@ -2,7 +2,7 @@ "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model", diff --git a/docs/v0.6.0/_src/benchmarks/retriever_performance.json b/docs/v0.6.0/_src/benchmarks/retriever_performance.json index f4985dade..f02763dcf 100644 --- a/docs/v0.6.0/_src/benchmarks/retriever_performance.json +++ b/docs/v0.6.0/_src/benchmarks/retriever_performance.json @@ -2,7 +2,7 @@ "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model", diff --git a/docs/v0.7.0/_src/benchmarks/retriever_performance.json b/docs/v0.7.0/_src/benchmarks/retriever_performance.json index 08b6dcadf..445f88f5c 100644 --- a/docs/v0.7.0/_src/benchmarks/retriever_performance.json +++ b/docs/v0.7.0/_src/benchmarks/retriever_performance.json @@ -2,7 +2,7 @@ "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model", diff --git a/test/benchmarks/templates.py b/test/benchmarks/templates.py index 43eefa196..42f726274 100644 --- a/test/benchmarks/templates.py +++ b/test/benchmarks/templates.py @@ -16,7 +16,7 @@ RETRIEVER_TEMPLATE = { "chart_type": "BarChart", "title": "Retriever Performance", "subtitle": "Time and Accuracy Benchmarks", - "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", + "description": "Comparison of the speed and accuracy of different DocumentStore / Retriever combinations on 100k documents. Indexing speed (in docs/sec) refers to how quickly Documents can be inserted into a DocumentStore. Querying speed (in queries/sec) refers to the speed at which the system returns relevant Documents when presented with a query.\n\nThe dataset used is Wikipedia, split into 100 word passages (from here)). \n\nFor querying, we use the Natural Questions development set in combination with the wiki passages. The Document Store is populated with the 100 word passages in which the answer spans occur (i.e. gold passages) as well as a random selection of 100 word passages in which the answer spans do not occur (i.e. negative passages). We take a total of 100k gold and negative passages. Query and document embedding are generated by the \"facebook/dpr-question_encoder-single-nq-base\" and \"facebook/dpr-ctx_encoder-single-nq-base\" models. The retriever returns 10 candidates and both the recall and mAP scores are calculated on these 10.\n\nFor FAISS HNSW, we use n_links=128, efSearch=20 and efConstruction=80. We use a cosine similarity function with BM25 retrievers, and dot product with DPR. Both index and query benchmarks are performed on an AWS P3.2xlarge instance which is accelerated by an Nvidia V100 GPU.", "bars": "horizontal", "columns": [ "Model",