| ---
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| title: HTK
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| app_file: app1.py
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| sdk: gradio
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| sdk_version: 4.36.1
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| ---
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| # MRC-RetroReader
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|
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| ## Introduction
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| MRC-RetroReader is a machine reading comprehension (MRC) model designed for reading comprehension tasks. The model leverages advanced neural network architectures to provide high accuracy in understanding and responding to textual queries.
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|
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| ## Table of Contents
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|
|
| - [Introduction](#introduction)
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| - [Table of Contents](#table-of-contents)
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| - [Installation](#installation)
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| - [Usage](#usage)
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| - [Features](#features)
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| - [Dependencies](#dependencies)
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| - [Configuration](#configuration)
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| - [Documentation](#documentation)
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| - [Examples](#examples)
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| - [Troubleshooting](#troubleshooting)
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| - [Contributors](#contributors)
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| - [License](#license)
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|
|
| ## Installation
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|
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| 1. Clone the repository:
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| ```
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| git clone https://github.com/phanhoang1803/MRC-RetroReader.git
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| cd MRC-RetroReader
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| ```
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| 2. Install the required dependencies:
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| ```
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| pip install -r requirements.txt
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| ```
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|
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| ## Usage
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|
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| - For notebooks: to running automatically, turn off wandb, warning if necessary:
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| ```
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| wandb off
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| import warnings
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| warnings.filterwarnings('ignore')
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| ```
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| - To train the model using the SQuAD v2 dataset:
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| ```
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| python train_squad_v2.py --config path-to-yaml-file --module intensive --batch_size batch_size
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| ```
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|
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| ## Features
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|
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| - High accuracy MRC model
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| - Easy to train on custom datasets
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| - Configurable parameters for model tuning
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|
|
| ## Dependencies
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|
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| - Python 3.x
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| - PyTorch
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| - Transformers
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| - Tokenizers
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| For a full list of dependencies, see `requirements.txt`.
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|
|
| ## Configuration
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| Configuration files can be found in the `configs` directory. Adjust the parameters in these files to customize the model training and evaluation.
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|
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| ## Documentation
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| For detailed documentation, refer to the `documentation` directory. This includes:
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| - Model architecture
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| - Training procedures
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| - Evaluation metrics
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|
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| ## Examples
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| Example training and evaluation scripts are provided in the repository. To train on the SQuAD v2 dataset:
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| ## Troubleshooting
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| For common issues and their solutions, refer to the `troubleshooting guide`.
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|
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| ## Contributors
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| - phanhoang1803
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|
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| ## License
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| This project is licensed under the MIT License. See the `LICENSE` file for details.
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|
