tags:
- autotrain
- text-classification
- lam
language:
- en
widget:
- text: >-
Neither this act nor any other act relating to said Cherokee Indians of
Robeson County shall be construed so as to impose on said Indians any
powers, privileges, rights or immunities, or
- text: >-
That Section one hundred and twenty-two eightythree of the General
Statutes of North Carolina is hereby amended by striking out the word
insane in the catch line and in lines two, four, nine and fifteen and
inserting in lieu thereof the words mentally disordered.
datasets:
- biglam/on_the_books
co2_eq_emissions:
emissions: 0.2641096478393395
license: mit
library_name: transformers
metrics:
- accuracy
- f1
- recall
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 64771135885
- CO2 Emissions (in grams): 0.2641
Validation Metrics
- Loss: 0.057
- Accuracy: 0.986
- Precision: 0.988
- Recall: 0.992
- AUC: 0.998
- F1: 0.990
Usage
This model is trained on a dataset of historical documents related to Jim Crow laws in the United States. The model was developed by drawing on the expertise of scholars and analyzing legal texts from various states, with the goal of identifying similarities between different states' Jim Crow laws. As such, this model may be useful for researchers or policymakers interested in understanding the history of racial discrimination in the US legal system.
The easiest way to use this model locally is via the Transformers library pipelines for inference.
Once you have installed transformers, you can run the following code. This will download and cache the model locally and allow you to make predictions on text input.
from transformers import pipeline
classifier = pipeline('text-classification', "biglam/autotrain-beyond-the-books")
classifier(text)
This will return predictions in the following format:
[{'label': 'no_jim_crow', 'score': 0.9718555212020874}]