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--- |
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tags: |
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- text-generation |
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--- |
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# Model Card for gpt2-base-gedi-detoxification |
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# Model Details |
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## Model Description |
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- **Developed by:** SkolkovoInstitute |
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- **Shared by [Optional]:** SkolkovoInstitute |
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- **Model type:** Text Generation |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Related Models:** |
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- **Parent Model:** GPT-2 |
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- **Resources for more information:** |
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- [Associated GeDI Paper](https://arxiv.org/pdf/2009.06367.pdf) |
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- [Blog Post](https://blog.salesforceairesearch.com/gedi/) |
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# Uses |
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## Direct Use |
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This model can be used for the task of Text Generation or fine-tune it to a downstream task. |
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## Downstream Use [Optional] |
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More information needed |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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OpenAI note in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) |
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> 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. |
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# Bias, Risks, and Limitations |
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The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of |
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unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their |
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[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): |
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> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases |
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> that require the generated text to be true. |
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> |
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> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do |
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> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a |
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> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, |
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> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar |
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> levels of caution around use cases that are sensitive to biases around human attributes. |
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See the [GPT-2 model card](https://huggingface.co/gpt2?text=My+name+is+Merve+and+my+favorite) for examples of how the model can have biased predictions |
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*The [GeDi Blog post](https://blog.salesforceairesearch.com/gedi/) notes* |
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>We use smaller language models as generative classifiers to guide generation from larger language models. We show that this method can make generations friendlier, reduce bias and toxicity, and achieve zero-shot controllable generation of unseen topics. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web |
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pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from |
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this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights |
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40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText |
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[here](https://github.com/openai/gpt-2/blob/master/domains.txt). |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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### Metrics |
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More information needed |
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## Results |
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The [GeDi Blog post](https://blog.salesforceairesearch.com/gedi/) notes the following results |
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| Model | Toxicity | Linguistic Quality | |
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|------------------|----------|---------------------| |
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| GPT-2 | 1.45 | 3.23 | |
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| GeDi-guided GPT2 | 1.17 | 3.44 | |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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Carbon emissions can be 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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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``` |
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@article{radford2019language, |
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title={Language Models are Unsupervised Multitask Learners}, |
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author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, |
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year={2019} |
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} |
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``` |
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``` |
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@article{KrauseGeDi2020, |
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title={{GeDi: Generative Discriminator Guided Sequence Generation}}, |
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author={Krause, Ben and Gotmare, Akhilesh Deepak and McCann, Bryan and Keskar, Nitish Shirish and Joty, Shafiq and Socher, Richard and Rajani, Nazneen Fatema}, |
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journal={arXiv preprint arXiv:2009.06367}, |
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year={2020} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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SkolkovoInstitute in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("SkolkovoInstitute/gpt2-base-gedi-detoxification") |
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model = AutoModelForCausalLM.from_pretrained("SkolkovoInstitute/gpt2-base-gedi-detoxification") |
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``` |
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</details> |
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