Update README.md

README.md

@@ -33,23 +33,22 @@ widget:
 <h3>Introduction</h3>
 
 This model is a <b>lightweight</b> and uncased version of <b>BERT</b> <b>[1]</b> for the <b>italian</b> language. With its <b>55M parameters</b> and <b>220MB</b> size, 
-it's <b>50% lighter</b> than a typical mono-lingual BERT model, and ideal 
-for circumstances where memory consumption and execution speed are critical aspects, while maintaining high quality results.
+it's <b>50% lighter</b> than a typical mono-lingual BERT model. It is ideal when memory consumption and execution speed are critical while maintaining high quality results.
 
 
 <h3>Model description</h3>
 
-The model has been obtained by taking the multilingual <b>DistilBERT</b> <b>[2]</b> model (from the HuggingFace team: [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)) as a starting point, 
-and then focusing it on the italian language while at the same time turning it into an uncased model by modifying the embedding layer 
+The model builds on the multilingual <b>DistilBERT</b> <b>[2]</b> model (from the HuggingFace team: [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)) as a starting point, 
+focusing it on the italian language while at the same time turning it into an uncased model by modifying the embedding layer 
 (as in <b>[3]</b>, but computing document-level frequencies over the <b>Wikipedia</b> dataset and setting a frequency threshold of 0.1%), which brings a considerable
 reduction in the number of parameters.
 
-In order to compensate for the deletion of cased tokens, which now forces the model to exploit lowercase representations of words which were previously capitalized, 
+To compensate for the deletion of cased tokens, which now forces the model to exploit lowercase representations of words previously capitalized, 
 the model has been further pre-trained on the italian split of the [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset, using the <b>whole word masking [4]</b> technique to make it more robust 
-with respect to the new uncased representations.
+to the new uncased representations.
 
 The resulting model has 55M parameters, a vocabulary of 13.832 tokens, and a size of 220MB, which makes it <b>50% lighter</b> than a typical mono-lingual BERT model and
-20% lighter than a typical mono-lingual DistilBERT model.
+20% lighter than a standard mono-lingual DistilBERT model.
 
 
 <h3>Training procedure</h3>
@@ -71,12 +70,12 @@ provided with the dataset, while for Named Entity Recognition the metrics have b
 | Part of Speech Tagging | 97.48  | 97.29 | 97.37 |
 | Named Entity Recognition | 89.29  | 89.84 | 89.53 |
 
-The metrics have been computed at token level and macro-averaged over the classes.
+The metrics have been computed at the token level and macro-averaged over the classes.
 
 
 <h3>Demo</h3>
 
-You can try the model online (fine-tuned on named entity recognition) using this webapp: https://huggingface.co/spaces/osiria/next-it-demo
+You can try the model online (fine-tuned on named entity recognition) using this web app: https://huggingface.co/spaces/osiria/next-it-demo
 
 <h3>Quick usage</h3>
 
@@ -92,8 +91,8 @@ pipeline_mlm = pipeline(task="fill-mask", model=model, tokenizer=tokenizer)
 
 <h3>Limitations</h3>
 
-This lightweight model is mainly trained on Wikipedia, so it's particularly suitable as an agile analyzer for large volumes of natively digital text taken 
-from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). It may show limitations when it comes to chaotic text, containing errors and slang expressions
+This lightweight model is mainly trained on Wikipedia, so it's particularly suitable as an agile analyzer for large volumes of natively digital text 
+from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). Hpwever, it may show limitations when it comes to chaotic text, containing errors and slang expressions
 (like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
 
 <h3>References</h3>