Default BERT Textual Entailment Service Documentation

Introduction

This application provides an implementation of BART fine-tuned on NLI dataset for indetifying argument relations. It serves as a default AMF component designed for detecting argument relations between propositions. Entailemtnt relation is mapped to support relation, conflicts, and non-relations between propositions.

• It can be integrated into the argument mining pipeline alongside other AMF components for further analysis and processing.

Brief Overview of the Architecture/Method

Brief overview of the architecture/method used.

• Dataset: Link to datasets
• Model ID: facebook/bart-large-mnli
• Repository: GitHub repository
• Paper: Link to published paper

Endpoints

/bert-te

Description

• Methods: GET, POST
  • GET: Returns information about the BERT Textual Entailment Service and its usage.
  • POST: Expects a file upload (file parameter) in the xAIF format. The Flask route processes the uploaded file identify argument relation between I-nodes and update the xAIF node to represent the argument relations and returns the updated xAIF as a json file.

Details

• URL: /bert-te
• Methods: GET, POST
• Input:
  • GET: No parameters.
  • POST: Expects a file upload (file parameter) in the xAIF format.
• Output:
  • Response: The inferred argument structure in xAIF json file format, containing nodes, edges, locutions, and other relevant information.
  • Example Response: Example JSON response.

Input and Output Formats

Input Format

• Text File: xAIF format input (xAIF format details).

Output Format

The inferred argument structure is returned in the xAIF format, containing nodes, edges, locutions, and other relevant information. In the xAIF:

• Argument units are specified as type "I" nodes.
• Argument relations are represented as "RA" type for support and "CA" type for attack relations.
• The relations between the "I" nodes and argument relation nodes are presented as edges.

Installation

Requirements for Installation

torch numpy transformers xaif_eval==0.0.9 amf-fast-inference==0.0.3 markdown2

Installation Setup

Using Docker Container

To set up the BERT Textual Entailment Service using Docker, follow these steps:

1. Clone the Repository:

   git clone https://github.com/arg-tech/bert-te.git

2. Navigate to the Project Root Directory:

   cd bert-te

3. Make Required Changes:

   • Edit the Dockerfile, main.py, and docker-compose.yml files to specify the container name, port number, and other settings as needed.

4. Build and Run the Docker Container:

   docker-compose up

From Source Without Docker

If you prefer to install without Docker:

1. Install Dependencies:

   • Ensure Python and necessary libraries are installed.

2. Configure and Run:

   • Configure the environment variables and settings in main.py.
   • Run the application using Python:

     python main.py

Usage

Using Programming Interface

Example Python Code Snippet

import requests
import json

url = 'http://your-server-url/bert-te'
input_file_path = 'example_xAIF.json'

with open(input_file_path, 'r', encoding='utf-8') as file:
    files = {'file': (input_file_path, file, 'application/json')}

response = requests.post(url, files=files)

if response.status_code == 200:
    output_file_path = 'output_xAIF.json'
    with open(output_file_path, 'w', encoding='utf-8') as output_file:
        json.dump(response.json(), output_file, ensure_ascii=False, indent=4)
    print(f'Response saved to {output_file_path}')
else:
    print(f'Failed to make a POST request. Status code: {response.status_code}')
    print(response.text)

Using cURL

• Example Request:

curl -X POST \
  -F "file=@example_xAIF.json" \
  http://your-server-url/bert-te

Using Web Interface

The service can also be used to create a pipeline on our n8n interface. Simply create an HTTP node, configure the node including the URL of the service and the parameter name of the file (file).