Bert Base Uncased Finetuned Clause Type

🚀 Unlocking the Power of Deep Learning for Clause Classification: Revolutionizing Commercial Applications

In the dynamic commercial operation landscape, efficiency and accuracy in document processing are of utmost importance. Traditional methods for analyzing legal clauses and contracts are often time - consuming and prone to human error. However, with the emergence of deep - learning technologies, especially in clause classification, a new era of automation and precision has dawned.

This is a fine - tuned version of "google - bert/bert - base - cased" for classification, using over 3200 clause examples extracted from contracts annotated by the Atticus Project [https://www.atticusprojectai.org/].

Through initiatives like the ATTICUS project and continuous advancements in AI, the future of commercial document analysis is promising. In this future, deep learning will play a crucial role in unlocking efficiency, insights, and value from the vast amount of textual information that drives the global economy.

✨ Features

Real - World Applications

In practice, the integration of deep learning for clause classification spans various industries:

• Legal Services: Law firms and legal departments use deep learning to streamline contract review processes and efficiently extract key information.
• Finance and Insurance: Deep - learning models help analyze complex financial agreements, identify clauses related to risk factors, liabilities, and compliance.
• Healthcare and Pharmaceuticals: Companies in highly regulated sectors employ deep learning to analyze patient contracts, supplier agreements, and regulatory documents.

Test Accuracy

The test accuracy of this model is 88%.

Labels

"0": "Anti - Assignment",
"1": "Audit_Rights",
"2": "Cap_On_Liability",
"3": "Covenant_Not_To_Sue",
"4": "Effective_Date",
"5": "Expiration_Date",
"6": "Governing_Law",
"7": "Insurance",
"8": "License_Grant",
"9": "Non - Transferable_License",
"10": "Notice_ Period_To_Terminate_Renewal",
"11": "Parties",
"12": "Post - Termination_Services",
"13": "Renewal_Term",
"14": "Revenue/Profit_Sharing",
"15": "Uncapped_Liability",
"16": "Warranty_Duration"

📦 Installation

To load the model, first install the transformer library in your environment:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("text - classification", model="mauro/bert - base - uncased - finetuned - clause - type")

clause = """ The foregoing license shall be transferable or sublicensable by Parent Group solely 
to a Permitted Party  and  subject to the restrictions herein  with any sale or transfer of a 
Parent business that utilizes the Licensed SpinCo IP If Parent enters an agreement to transfer 
the License_Granted to it under this Section 3 1 in connection with any sale or transfer of a 
Parent business  then SpinCo and members of the SpinCo Group shall be made third party 
beneficiaries under such transfer agreement to enforce breaches of the license 
3 If SpinCo enters an agreement to transfer the License_Granted to it under this  
Section 3 2  in connection with any sale or transfer of a SpinCo business  then Parent 
and members of the Parent Group shall be made third party beneficiaries under such transfer 
agreement to enforce breaches of the license     Such agreement shall prohibit any further 
sublicensing or transfer of rights by the Permitted Party  or  in the case of a sale or 
transfer of a Parent business  the transferee  or any use of the Licensed SpinCo IP outside 
the scope of the License_Granted to Parent herein     Such agreement shall prohibit any further 
transfer of rights by such party or any use of the transferred Intellectual Property outside the 
scope of the License_Granted to SpinCo herein"""

result = classifier(clause, return_all_scores=False)
print(result)

The result will be:

[{'label': 'Non - Transferable_License', 'score': 0.989809513092041}]

Advanced Usage

For visualization, you need to install Matplotlib and Pandas:

pip install matplotlib pandas

# all probabilities
preds = classifier(clause, return_all_scores=True)

# create a df with the result
import pandas as pd
df = pd.DataFrame([[x['label'], x['score']] for x in preds[0]], columns=['label', 'score'])

import matplotlib.pyplot as plt

# probability distribution
plt.bar(df['label'], df['score'])
plt.xlabel('label')
plt.ylabel('score')  
plt.title('Probability distribution for all clauses type')
plt.xticks(rotation=90) 
plt.show()

You will get the probability distribution of all classes: 

📄 License

This project is licensed under the Apache - 2.0 license.