Update readme.md

readme.md

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 # Italian CLIP
 
-With a few tricks, we have been able to fine-tune a competitive CLIP-italian model with only 1 million training samples.
+With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with only 1.4 million training samples.
 
 In building this project we kept in mind the following things:
 
-+ **Novel Contributions**: we tried to bring something new to the table;
-+ **Scientific Validity**: models can look very cool, but external validation is important to assess the real impact;
++ **Novel Contributions**: We created a dataset of ~1.4 million Italian image-text pairs and to our knowledge trained the best Italian CLIP model currently in existence;
++ **Scientific Validity**: Claim are easy, facts are hard. That's why validation is important to assess the real impact of a model. That's why we thoroughly evaluated our models and made the validation reproducible for everybody.
 + **Broader Outlook**: we always considered which are the possible usages for this model.
 
-We put our **hearts** and **souls** in this project during this week! Not only we worked on a cool project, but we were
-able to meet new people and make new friends that worked together for a common goal! 
-Thank you for this amazing opportunity, we hope you will like our project :heart:.
+We put our **hearts** and **souls** into the project during this week! Not only did we work on a cool project, but we were
+able to make new friends and and learn a lot from each other to work towards a common goal! 
+Thank you for this amazing opportunity, we hope you will like the results. :heart:
 
 # Novel Contributions
 
-The original CLIP model was trained on 400millions text-image pairs; this amount of data is not available for Italian and the only datasets for captioning in the literature are MSCOCO-IT (translated version of MSCOCO) and WIT. To get competitive results we follewed three directions: 1) more data 2) better augmentation and 3) better training.
+The original CLIP model was trained on 400 million image-text pairs; this amount of data is not available for Italian and the only datasets for captioning in the literature are MSCOCO-IT (a translated version of MSCOCO) and WIT. To get competitive results we followed three strategies: 1) more data, 2) better augmentations and 3) better training.
 
 ## More Data
 
 We eventually had to deal with the fact that we do not have the same data that OpenAI had during the training of CLIP.
-Thus, we opted for one choice, data of medium-high quality.
+Thus, we tried to add as much data as possible while keeping the data-quality as high as possible.
 
 We considered three main sources of data:
 
@@ -67,7 +67,6 @@ We selected two different tasks:
 + image-retrieval 
 + zero-shot classification
 
-            
 ### Image Retrieval
 
 | MRR             | CLIP-Italian | mCLIP |
@@ -79,7 +78,7 @@ We selected two different tasks:
 
 ### Zero-shot classification
 
-| Accuracy          | CLIP-Italian | mCLIP |
+| Accuracy        | CLIP-Italian | mCLIP |
 | --------------- | ------------ |-------|
 | Accuracy@1      |              |       |   
 | Accuracy@5      |              |       |