Merge pull request #3591 from vespa-engine/kkraune/links

Fix links

en/cross-encoders.md

@@ -221,7 +221,7 @@ Quantization of model weights can drastically improve serving efficiency on CPU.
 
 ## Examples
 
-The [Transformers](https://github.com/vespa-engine/sample-apps/tree/master/transformers)
+The [MS Marco](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking)
 sample application demonstrates using cross-encoders. 
 
 ## Using cross-encoders with multi-vector indexing

en/onnx.md

@@ -131,9 +131,8 @@ over multiple files, is currently not supported.
 
 ## Examples
 
-The [transformers](https://github.com/vespa-engine/sample-apps/tree/master/transformers)
-sample application uses a cross-encoder model to
-[re-rank documents](https://github.com/vespa-engine/sample-apps/blob/master/transformers/application/schemas/msmarco.sd).
+The [MS Marco](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking)
+sample application uses a cross-encoder model to re-rank documents.
 The [simple-semantic-search](https://github.com/vespa-engine/sample-apps/tree/master/simple-semantic-search) sample application
 uses onnx models for embedding inference. [custom-embeddings](https://github.com/vespa-engine/sample-apps/tree/master/custom-embeddings) 
 has an example of a PyTorch model that is exported to onnx format for use in re-ranking. 

en/use-case-text-image-search.md

@@ -3,43 +3,27 @@
 title: "Use Case - Text-Image Search"
 ---
 
-The [text-image use
-case](https://github.com/vespa-engine/sample-apps/tree/master/text-image-search/) is an example
-of a text-to-image search application. Taking a textual query, such as "two
-people bicycling", it will return images containing two people on bikes. This
-application is built using [CLIP (Contrastive Language-Image
+The [text-image use case](https://github.com/vespa-engine/sample-apps/tree/master/text-image-search/) is an example
+of a text-to-image search application.
+Taking a textual query, such as "two people bicycling", it will return images containing two people on bikes.
+This application is built using [CLIP (Contrastive Language-Image
 Pre-Training)](https://github.com/openai/CLIP) which enables "zero-shot prediction".
 This means that the system can return sensible results for images it hasn't
 seen during training, allowing it to process and index any image. In this
 use case, we use the [Flickr8k](https://github.com/jbrownlee/Datasets/blob/master/Flickr8k_Dataset.names)
 dataset, which was not used during training of the CLIP model.
 
 
-To start the application, please follow the instructions in the
+To start the application, follow the instructions in the
 [README](https://github.com/vespa-engine/sample-apps/blob/master/text-image-search/README.md).
-
-This sample application can be used in two different ways. The first is by
-using a [Python-based search
-app](https://github.com/vespa-engine/sample-apps/blob/master/text-image-search/src/python/README.md),
-which is suitable for exploration and analysis. The other is a [stand-alone
-Vespa
-application](https://github.com/vespa-engine/sample-apps/blob/master/text-image-search/README.md),
-which is more suitable for production.
-
-After deploying the application, you can ask questions like this:
+After deploying the application, you can ask questions like:
 
 ```
 http://localhost:8080/search/?input=two+people+bicylcing
 ```
 
 ### Highlighted features
 
-* [PyVespa](https://pyvespa.readthedocs.io/en/latest/index.html)
-
-    PyVespa is the official Python API for Vespa. This can be used to easily
-    create, modify, deploy and interact with Vespa instances. The main
-    goal of the library is to allow for faster prototyping and to facilitate
-    Machine Learning experiments for Vespa applications.
 
 * [Approximate nearest neighbors using an HNSW index](approximate-nn-hnsw.html)
 
@@ -73,4 +57,3 @@ http://localhost:8080/search/?input=two+people+bicylcing
     class with data in `services.xml`, and the resulting object is dependency
     injected to the component during construction. This application uses custom
     config to set up the token vocabulary used in tokenization.
-