Fix sentences ,grammar, and punctuation in description in docshub

docs/hub/model-card-appendix.md

@@ -8,18 +8,18 @@ _Full text responses to key questions_
 ***Insight: Respondents had generally similar views of what model cards are: documentation focused on issues like training, use cases, and bias/limitations***
 
 * Model cards are model descriptions, both of how they were trained, their use cases, and potential biases and limitations
-* Documents describing the essential features of a model in order for the reader/user to understand the artefact he/she has in front, the background/training, how it can be used, and its technical/ethical limitations.
-* They serve as a living artefact of models to document them. Model cards contain information that go from a high level description of what the specific model can be used to, to limitations, biases, metrics, and much more. They are used primarily to understand what the model does.
+* Documents describing the essential features of a model in order for the reader/user to understand the artifact they have in front, the background/training, how it can be used, and its technical/ethical limitations.
+* They serve as a living artifact of models to document them. Model cards contain information that goes from a high-level description of what the specific model can be used to, to limitations, biases, metrics, and much more. They are used primarily to understand what the model does.
 * Model cards are to models what GitHub READMEs are to GitHub projects. It tells people all the information they need to know about the model. If you don't write one, nobody will use your model.
-* From what I understand, a model card uses certain benchmarks (geography, culture, sex, etc) to define both a model's usability and limitations. It's essentially a model's 'nutrition facts label' that can show how a model was created and educates others on its reusability.
-* Model cards are the metadata and documentation about the model, everything I need to know to use the model properly: info about the model, what paper introduced it, what dataset was it trained on or fine-tuned on, whom does it belong to, are there known risks and limitations with this model, any useful technical info.
+* From what I understand, a model card uses certain benchmarks (geography, culture, sex, etc.) to define both a model's usability and limitations. It's essentially a model's 'nutrition facts label' that can show how a model was created and educates others on its reusability.
+* Model cards are the metadata and documentation about the model, everything I need to know to use the model properly: info about the model, what paper introduced it, what dataset was it trained on or fine-tuned on, whom it belongs to, are there known risks and limitations with this model, any useful technical info.
 * IMO model cards are a brief presentation of a model which includes:
   *  short summary of the architectural particularities of the model
   *  describing the data it was trained on
-  *  what is the performance on reference datasets (accuracy and speed metrics if possible)
-  *  limitations
+  *  What is the performance on reference datasets (accuracy and speed metrics if possible)
+  *  Limitations
   *  how to use it in the context of the Transformers library
-  *  source (original article, Github repo,...)
+  *  source (original article, GitHub repo,...)
 *  Easily accessible documentation that any background can read and learn about critical model components and social impact
 
 

docs/hub/model-card-guidebook.md

@@ -1,12 +1,12 @@
 # Model Card Guidebook 
 
-Model cards are an important documentation and transparency framework for machine learning models. We believe that model cards have the potential to serve as *boundary objects*, a single artefact that is accessible to users who have different backgrounds and goals when interacting with model cards – including developers, students, policymakers, ethicists, those impacted by machine learning models, and other stakeholders. We recognize that developing a single artefact to serve such multifaceted purposes is difficult and requires careful consideration of potential users and use cases. Our goal as part of the Hugging Face science team over the last several months has been to help operationalize model cards towards that vision, taking into account these challenges, both at Hugging Face and in the broader ML community. 
+Model cards are an important documentation and transparency framework for machine learning models. We believe that model cards have the potential to serve as *boundary objects*, a single artifact that is accessible to users who have different backgrounds and goals when interacting with model cards – including developers, students, policymakers, ethicists, those impacted by machine learning models, and other stakeholders. We recognize that developing a single artifact to serve such multifaceted purposes is difficult and requires careful consideration of potential users and use cases. Our goal as part of the Hugging Face science team over the last several months has been to help operationalize model cards towards that vision, taking into account these challenges, both at Hugging Face and in the broader ML community. 
 
-To work towards that goal, it is important to recognize the thoughtful, dedicated efforts that have helped model cards grow into what they are today, from the adoption of model cards as a standard practice at many large organisations to the development of sophisticated tools for hosting and generating model cards. Since model cards were proposed by Mitchell et al. (2018), the landscape of machine learning documentation has expanded and evolved. A plethora of documentation tools and templates for data, models, and ML systems have been proposed and have developed – reflecting the incredible work of hundreds of researchers, impacted community members, advocates, and other stakeholders. Important discussions about the relationship between ML documentation and theories of change in responsible AI have created continued important discussions, and at times, divergence. We also recognize the challenges facing model cards, which in some ways mirror the challenges facing machine learning documentation and responsible AI efforts more generally, and we see opportunities ahead to help shape both model cards and the ecosystems in which they function positively in the months and years ahead. 
+To work towards that goal, it is important to recognize the thoughtful, dedicated efforts that have helped model cards grow into what they are today, from the adoption of model cards as a standard practice at many large organizations to the development of sophisticated tools for hosting and generating model cards. Since model cards were proposed by Mitchell et al. (2018), the landscape of machine learning documentation has expanded and evolved. A plethora of documentation tools and templates for data, models, and ML systems have been proposed and developed – reflecting the incredible work of hundreds of researchers, impacted community members, advocates, and other stakeholders. Important discussions about the relationship between ML documentation and theories of change in responsible AI have created continued important discussions, and at times, divergence. We also recognize the challenges facing model cards, which in some ways mirror the challenges facing machine learning documentation and responsible AI efforts more generally, and we see opportunities ahead to help shape both model cards and the ecosystems in which they function positively in the months and years ahead. 
 
-Our work presents a view of where we think model cards stand right now and where they could go in the future, at Hugging Face and beyond. This work is a “snapshot” of the current state of model cards, informed by a landscape analysis of the many ways ML documentation artefacts have been instantiated. It represents one perspective amongst multiple about both the current state and more aspirational visions of model cards. In this blog post, we summarise our work, including a discussion of the broader, growing landscape of ML documentation tools, the diverse audiences for and opinions about model cards, and potential new templates for model card content. We also explore and develop model cards for machine learning models in the context of the Hugging Face Hub, using the Hub’s features to collaboratively create, discuss, and disseminate model cards for ML models. 
+Our work presents a view of where we think model cards stand right now and where they could go in the future, at Hugging Face and beyond. This work is a “snapshot” of the current state of model cards, informed by a landscape analysis of the many ways ML documentation artifacts have been instantiated. It represents one perspective amongst multiple about both the current state and more aspirational visions of model cards. In this blog post, we summarise our work, including a discussion of the broader, growing landscape of ML documentation tools, the diverse audiences for and opinions about model cards, and potential new templates for model card content. We also explore and develop model cards for machine learning models in the context of the Hugging Face Hub, using the Hub’s features to collaboratively create, discuss, and disseminate model cards for ML models. 
 
-With the launch of this Guidebook, we introduce several new resources and connect together previous work on Model Cards:
+With the launch of this Guidebook, we introduce several new resources and connect previous work on Model Cards:
 
 1) An updated Model Card template, released in [the `huggingface_hub` library](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md), drawing together Model Card work in academia and throughout the industry.