update text

introduction.md

@@ -131,9 +131,6 @@ The multilingual CLIP (henceforth, mCLIP), is a model introduced by [Nils Reimer
 [sentence-transformer](https://www.sbert.net/index.html) library. mCLIP is based on a multilingual encoder
 that was created through multilingual knowledge distillation (see [Reimers et al., 2020](https://aclanthology.org/2020.emnlp-main.365/)).
 
-### Experiments Replication
-We provide two colab notebooks to replicate both experiments.
-
 ### Tasks
 
 We selected two different tasks: 
@@ -152,7 +149,7 @@ Both experiments should be very easy to replicate, we share the two colab notebo
 
 This experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input
 a caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics
-we use the MRR.
+we use the MRR@K.
 
 | MRR             | CLIP-Italian | mCLIP |
 | --------------- | ------------ |-------|
@@ -167,7 +164,7 @@ on 400million images (and some of them probably were from MSCOCO).
 ### Zero-shot image classification
 
 This experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet. 
-To do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy. 
+To do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy at different levels. 
 
 
 | Accuracy        | CLIP-Italian | mCLIP |