Limit use of collapsible sections; fix emissions info

README.md

@@ -20,6 +20,8 @@ model-index:
        - type: perplexity
          name: Perplexity
          value: 21.1
+         
+co2_eq_emissions: 149.2 kg
 ---
 
 # DistilGPT2
@@ -28,9 +30,6 @@ DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained w
 
 ## Model Details
 
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 - **Developed by:** Hugging Face
 - **Model type:** Transformer-based Language Model
 - **Language:** English
@@ -38,13 +37,8 @@ DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained w
 - **Model Description:** DistilGPT2 is an English-language model pre-trained with the supervision of the 124 million parameter version of GPT-2. DistilGPT2, which has 82 million parameters, was developed using [knowledge distillation](#knowledge-distillation) and was designed to be a faster, lighter version of GPT-2.
 - **Resources for more information:** See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including Distilled-GPT2), [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure, and this page for more about [GPT-2](https://openai.com/blog/better-language-models/).
 
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 ## Uses, Limitations and Risks
 
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 #### Limitations and Risks
 
 <details>
@@ -128,9 +122,6 @@ output = model(encoded_input)
 
 #### Potential Uses
 
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 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: 
@@ -141,71 +132,38 @@ The developers of GPT-2 state in their [model card](https://github.com/openai/gp
 
 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.
 
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 #### Out-of-scope Uses
 
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 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.
 
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 ## Training Data
 
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 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.
 
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 ## Training Procedure
 
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 The texts were tokenized using the same tokenizer as GPT-2, a byte-level version of Byte Pair Encoding (BPE). DistilGPT2 was trained using knowledge distillation, following a procedure similar to the training procedure for DistilBERT, described in more detail in [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108). 
 
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 ## Evaluation Results
 
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 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).
 
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 ## Carbon Emissions
 
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 *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:** 16GB V100
-- **Hours used:** 8
+- **Hardware Type:** 8 16GB V100
+- **Hours used:** 168 (1 week)
 - **Cloud Provider:** Azure
 - **Compute Region:** unavailable, assumed East US for calculations
-- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: .89 kg eq. CO2
-- **Carbon already offset by cloud provider:** .89 kg eq. CO2
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+- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 149.2 kg eq. CO2
 
 ## Citation
 
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 ```bibtex
 @inproceedings{sanh2019distilbert,
   title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
@@ -215,17 +173,10 @@ The creators of DistilGPT2 [report](https://github.com/huggingface/transformers/
 }
 ```
 
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 ## Glossary
 
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 -	<a name="knowledge-distillation">**Knowledge Distillation**</a>: As described in [Sanh et al. (2019)](https://arxiv.org/pdf/1910.01108.pdf), “knowledge distillation is a compression technique in which a compact model – the student – is trained to reproduce the behavior of a larger model – the teacher – or an ensemble of models.” Also see [Bucila et al. (2006)](https://www.cs.cornell.edu/~caruana/compression.kdd06.pdf) and [Hinton et al. (2015)](https://arxiv.org/abs/1503.02531).
 
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 <a href="https://huggingface.co/exbert/?model=distilgpt2">
 	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
 </a>