biglam/autotrain-beyond-the-books
By biglam · ↓ 4 · ♥ 0 · Updated 2023-06-26
What's in this repo
This page lists the files in biglam/autotrain-beyond-the-books, including model.onnx.
- 1 ONNX models (.onnx files)
- 10 files, including 4 .json, 1 .md, 1 .onnx, 1 .safetensors, 1 .bin, 1 .txt
- Last updated 2023-06-26
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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This model is trained on a dataset of historical documents related to Jim Crow laws in the United States.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}]
Model info
Created: 2023-06-07
Updated: 2023-06-26
View on Hugging Face