Add pipeline tag, library name and Github link

This PR adds the `pipeline_tag` and `library_name` to the model card metadata, and includes a link to the GitHub repository for easier access to the code. The `pipeline_tag` is set to `text-generation` as this is a language model. The `library_name` is set to `transformers` because the model is used with the `transformers` library.

README.md

@@ -1,8 +1,10 @@
 ---
-license: apache-2.0
 language:
 - it
 - en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # Mistral-7B-v0.1-Italian-FVT
@@ -14,9 +16,9 @@ language:
 
 The **Mistral-7B-v0.1-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 7B (text in/text out), adapted models from **Mistral-7B-Base-v0.1**.
 
-*Mistral-v0.1-Italian-FVT* is a continual trained mistral model, after tokenizer substitution.
+*Mistral-v0.1-Italian-FVT* is a continually trained Mistral model, after tokenizer substitution.
 
-The tokenizer of this models after adaptation is the same of [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
+The tokenizer of this model after adaptation is the same as [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
 
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
 
@@ -24,7 +26,7 @@ The tokenizer of this models after adaptation is the same of [Minverva-3B](https
 
 ## Data used for the adaptation
 
-The **Mistral-7B-v0.1-Adapted** model are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
+The **Mistral-7B-v0.1-Adapted** model is trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
 The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
 
 
@@ -32,7 +34,7 @@ The data are extracted to be skewed toward Italian language with a ration of one
 
 You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
 
-Make sure to update your transformers installation via pip install --upgrade transformers.
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
 
 ```python
 import transformers
@@ -47,6 +49,8 @@ pipeline = transformers.pipeline(
 pipeline("Cosa si può fare in una bella giornata di sole?")
 ```
 
+Code: https://github.com/Andrew-Wyn/Italian-LLM-Adaptation
+
 ## Citation
 
 If you use any part of this work, please consider citing the paper as follows: