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