new stuff in readme.md

readme.md

@@ -3,6 +3,7 @@
 ....
 
 # 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.
 
 ## More Data
@@ -12,11 +13,13 @@ Thus, we opted for one choice, data of medium-high quality.
 
 We considered three main sources of data:
 
-+ WIT. Most of this caption describe ontological knowledge and encyclopedic facts (e.g., Roberto Baggio in 1994). 
++ WIT. Most of these captions describe ontological knowledge and encyclopedic facts (e.g., Roberto Baggio in 1994). 
 However, this kind of text, without more information, is not useful to learn a good mapping between images and captions. On the other hand,
 this text is written in Italian and it is good quality. To prevent polluting the data with captions that are not meaningful, we used POS tagging
 on the data and removed all the captions that were composed for the 80% or more by PROPN.
+
 + MSCOCO-IT
+
 + CC
 
 
@@ -29,11 +32,12 @@ on the data and removed all the captions that were composed for the 80% or more
 
 ### Backbone Freezing
 
-![Backbone Freezing](static/img/clip-italian.png)
+![Backbone Freezing](https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/clip-italian.png)
 
 
 
 # Scientific Validity
+
 Those images are definitely cool and interesting, but a model is nothing without validation.
 To better understand how well our clip-italian model works we run an experimental evaluation. Since this is the first clip-based model in Italian, we used the multilingual CLIP model as a comparison baseline. 
 
@@ -46,7 +50,7 @@ We selected two different tasks:
 + zero-shot classification
 
             
-## Image Retrieval
+### Image Retrieval
 
 | MRR             | CLIP-Italian | mCLIP |
 | --------------- | ------------ |-------|
@@ -55,7 +59,7 @@ We selected two different tasks:
 | MRR@10          |              |       |
 
 
-## Zero-shot classification
+### Zero-shot classification
 
 | Accuracy          | CLIP-Italian | mCLIP |
 | --------------- | ------------ |-------|
@@ -68,5 +72,5 @@ We selected two different tasks:
 
 
 
-
+# Other Notes
 This readme has been designed using resources from Flaticon.com