library_name: transformers
pipeline_tag: text-generation
license: mit
tags: []
Model Card for Perspectival Language Model
This model is associated with the paper "Pretraining Language Models for Diachronic Linguistic Change Discovery" and is designed for text generation, particularly in the context of historical linguistics.
Model Details
Model Description
This 🤗 transformers model was trained to study diachronic linguistic change by pretraining language models on historical text corpora.
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: Llama (Please verify and specify the exact architecture)
- Language(s) (NLP): English (Please specify all languages if applicable)
- License: MIT (Please verify and correct if needed)
- Finetuned from model [optional]: [Please specify base model if applicable]
Model Sources
- Repository: https://github.com/comp-int-hum/historical-perspectival-lm
- Paper [optional]: https://huggingface.co/papers/2504.05523
- Demo [optional]: [More Information Needed]
Uses
Direct Use
The model can be used directly for generating text, especially when exploring historical language patterns.
Downstream Use [optional]
This model can be fine-tuned for tasks like language change detection or stylistic analysis across time periods.
Out-of-Scope Use
The model may not perform well on tasks requiring contemporary language understanding.
Bias, Risks, and Limitations
The model's training data reflects biases in historical texts, which could appear in the model's outputs.
Recommendations
Users should be aware of potential biases and the model's limitations with modern language.
How to Get Started with the Model
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
[More Information Needed]
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
[More Information Needed]
Model Examination [optional]
[More Information Needed]
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
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]