Update README.md
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README.md
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@@ -37,4 +37,166 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("x2bee/PLOAR-7B-DPO-v1.0")
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model = AutoModelForCausalLM.from_pretrained("x2bee/PLOAR-7B-DPO-v1.0")
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tokenizer = AutoTokenizer.from_pretrained("x2bee/PLOAR-7B-DPO-v1.0")
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model = AutoModelForCausalLM.from_pretrained("x2bee/PLOAR-7B-DPO-v1.0")
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```
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## Downstream Use [Optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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## Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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# Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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## Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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# Training Details
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## Training Data
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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More information on training data needed
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## Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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More information needed
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# Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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## Testing Data, Factors & Metrics
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### Testing Data
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<!-- This should link to a Data Card if possible. -->
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More information needed
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### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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More information needed
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### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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More information needed
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## Results
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More information needed
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# Model Examination
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More information needed
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# Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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More information needed
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**APA:**
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More information needed
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# Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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More information needed
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# More Information [optional]
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If you would like more information about our company, please visit the link below.
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[tech.x2bee.com](https://tech.x2bee.com/)
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# Model Card Authors [optional]
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<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
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Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son
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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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More information needed
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</details>
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