
# 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This model is trained on a dataset of historical documents related to Jim Crow laws in the United States.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](https://huggingface.co/docs/transformers/index) library [pipelines for inference](https://huggingface.co/docs/transformers/pipeline_tutorial). 

Once you have [installed transformers](https://huggingface.co/docs/transformers/installation), 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}]
```
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["autotrain", "text-classification", "lam"], "datasets": ["biglam/on_the_books"], "metrics": ["accuracy", "f1", "recall"], "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."}], "co2_eq_emissions": {"emissions": 0.2641096478393395}}
transformers pytorch onnx safetensors bert text-classification autotrain lam en dataset:biglam/on_the_books license:mit co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #safetensors #bert #text-classification #autotrain #lam #en #dataset-biglam/on_the_books #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# 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. 




This will return predictions in the following format:


# 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. 




This will return predictions in the following format:
TAGS
#transformers #pytorch #onnx #safetensors #bert #text-classification #autotrain #lam #en #dataset-biglam/on_the_books #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # 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. 




This will return predictions in the following format: