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Model Card for alias-gpt2-small-x21

Model Details

Model Description

More information needed

  • Developed by: Stanford CRFM

  • Shared by [Optional]: Stanford CRFM

  • Model type: Text Generation

  • Language(s) (NLP): More information needed

  • License: Apache 2.0

  • Parent Model: GPT-2

  • Resources for more information:

Uses

Direct Use

This model can be used for the task of Text Generation.

Downstream Use [Optional]

More information needed.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

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Training Procedure

Preprocessing

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Speeds, Sizes, Times

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Model Examination

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: More information needed
  • Hours used: More information needed
  • Cloud Provider: More information needed
  • Compute Region: More information needed
  • Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Stanford CRFM in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/alias-gpt2-small-x21")

model = AutoModelForCausalLM.from_pretrained("stanford-crfm/alias-gpt2-small-x21")
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