Small section rearranging

README.md

@@ -21,7 +21,7 @@ model-index:
          name: Perplexity
          value: 21.1
          
-co2_eq_emissions: 149.2 kg
+co2_eq_emissions: 149200 g
 ---
 
 # DistilGPT2
@@ -74,7 +74,27 @@ The impact of model compression techniques – such as knowledge distillation
 
 </details>
 
-#### How to Get Started with the Model 
+#### Potential Uses
+
+Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model. 
+
+The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including: 
+
+> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
+> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
+> - *Entertainment: Creation of games, chat bots, and amusing generations.*
+
+Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
+
+#### Out-of-scope Uses
+
+OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md): 
+
+> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
+>
+> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
+
+### How to Get Started with the Model 
 
 <details>
 <summary>Click to expand</summary>
@@ -120,26 +140,6 @@ output = model(encoded_input)
 
 </details>
 
-#### Potential Uses
-
-Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model. 
-
-The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including: 
-
-> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
-> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
-> - *Entertainment: Creation of games, chat bots, and amusing generations.*
-
-Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
-
-#### Out-of-scope Uses
-
-OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md): 
-
-> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
->
-> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
-
 ## Training Data
 
 DistilGPT2 was trained using [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), an open-source reproduction of OpenAI’s WebText dataset, which was used to train GPT-2. See the [OpenWebTextCorpus Dataset Card](https://huggingface.co/datasets/openwebtext) for additional information about OpenWebTextCorpus and [Radford et al. (2019)](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) for additional information about WebText.
@@ -152,9 +152,9 @@ The texts were tokenized using the same tokenizer as GPT-2, a byte-level version
 
 The creators of DistilGPT2 [report](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) that, on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGPT2 (after fine-tuning on the train set).
 
-## Carbon Emissions
+## Environmental Impact
 
-*Emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
+*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
 
 - **Hardware Type:** 8 16GB V100
 - **Hours used:** 168 (1 week)