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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