From e5d149257ef39dfcb4f38ca630208d5745fa0310 Mon Sep 17 00:00:00 2001 From: Liqun Shao Date: Mon, 24 Jun 2019 12:58:39 -0400 Subject: [PATCH] fix broken link for gensen aml notebook in readme --- scenarios/sentence_similarity/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scenarios/sentence_similarity/README.md b/scenarios/sentence_similarity/README.md index 9fa0b68..0bfe528 100644 --- a/scenarios/sentence_similarity/README.md +++ b/scenarios/sentence_similarity/README.md @@ -21,4 +21,4 @@ The following summarizes each notebook for Sentence Similarity. Each notebook pr |---|---|---| |[Creating a Baseline model](baseline_deep_dive.ipynb)| Yes| A baseline model is a basic solution that serves as a point of reference for comparing other models to. The baseline model's performance gives us an indication of how much better our models can perform relative to a naive approach.| |Senteval |[local](senteval_local.ipynb), [AzureML](senteval_azureml.ipynb)|SentEval is a widely used benchmarking tool for evaluating general-purpose sentence embeddings. Running SentEval locally is easy, but not necessarily efficient depending on the model specs. We provide an example on how to do this efficiently in Azure Machine Learning Service. | -|[GenSen on AzureML](gensen_aml_deep_dive.ipynb_)| No | This notebook serves as an introduction to an end-to-end NLP solution for sentence similarity building one of the State of the Art models, GenSen, on the AzureML platform. We show the advantages of AzureML when training large NLP models with GPU. +|[GenSen on AzureML](gensen_aml_deep_dive.ipynb)| No | This notebook serves as an introduction to an end-to-end NLP solution for sentence similarity building one of the State of the Art models, GenSen, on the AzureML platform. We show the advantages of AzureML when training large NLP models with GPU.