docs: clean up readme and ndcg (#359)

jina-ai · Jan 27, 2022 · bb8e974 · bb8e974
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diff --git a/README.md b/README.md
@@ -23,11 +23,11 @@ applications.
 🎛 **Designed for finetuning**: a human-in-the-loop deep learning tool for leveling up your pretrained models in domain-specific neural search applications.
 
 🔱 **Powerful yet intuitive**: all you need is `finetuner.fit()` - a one-liner that unlocks rich features such as
-siamese/triplet network, interactive labeling, layer pruning, weights freezing, dimensionality reduction.
+siamese/triplet network, metric learning, self-supervised pretraining, layer pruning, weights freezing, dimensionality reduction.
 
 ⚛️ **Framework-agnostic**: promise an identical API & user experience on PyTorch, Tensorflow/Keras and PaddlePaddle deep learning backends.
 
-🧈 **Jina integration**: buttery smooth integration with Jina, reducing the cost of context-switch between experiment
+🧈 **DocArray integration**: buttery smooth integration with DocArray, reducing the cost of context-switch between experiment
 and production.
 
 <!-- end elevator-pitch -->

diff --git a/docs/get-started/3d-mesh/index.md b/docs/get-started/3d-mesh/index.md
@@ -207,7 +207,7 @@ We'll use two metrics to evaluate our two models (pretrained, straight out of th
 
 **mAP@k** : We'll calculate the average precision at 1, 5 and 10, then we'll calculate the mean of those average precisions for all documents in our test data (these are our queries) because we care about how accurate and precise our retrieved 3D objects are.
 
-**mNDCG@k**: We'll calculate NDCG at 1,5 and 10, then we'll calculate the mean of those for all documents in our test data  because we care about the order of the retrieved 3D objects.
+*nDCG@k**: We'll calculate nDCG at 1,5 and 10, then we'll calculate the mean of those for all documents in our test data  because we care about the order of the retrieved 3D objects.
 
 ````{dropdown} Complete source code
 
@@ -244,11 +244,11 @@ The difference is shown in the tables below:
 | mAP@5  | 0.113       | 0.697      |
 | mAP@10 | 0.100       | 0.686      |
 
-| mNDCG@k  | pre-trained | fine-tuned |
-|--------|-------------|--------------|
-| mNDCG@1  | 0.563       | 0.927      |
-| mNDCG@5  | 0.617       | 0.931      |
-| mNDCG@10 | 0.647       | 0.935      |
+| nDCG@k  | pre-trained | fine-tuned |
+|---------|-------------|--------------|
+| nDCG@1  | 0.563       | 0.927      |
+| nDCG@5  | 0.617       | 0.931      |
+| nDCG@10 | 0.647       | 0.935      |
 
 Now let's do some queries ourselves and check the visualizations