LLNL/LUAR
L
LLNL/LUAR
Overview :
LLNL/LUAR is a Transformer-based model designed for learning author representations, primarily focused on cross-domain transfer research for author verification. Introduced in an EMNLP 2021 paper, it explores whether author representations learned in one domain can be transferred to another. Key advantages of the model include its ability to handle large datasets and facilitate zero-shot transfer across diverse domains such as Amazon reviews, fanfiction short stories, and Reddit comments. Background information includes innovative research in the field of cross-domain author verification and its potential applications in natural language processing. This product is open-source and follows the Apache-2.0 license, allowing for free use.
Target Users :
The primary audience includes researchers and developers in the field of natural language processing, particularly those interested in author verification, text classification, and cross-domain transfer learning. This product is well-suited for them as it offers a powerful tool for researching and developing applications based on author representations. Additionally, its open-source nature allows for extensive customization and improvement.
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Use Cases
Researchers use the LLNL/LUAR model for author verification tasks on Amazon review datasets.
Developers experiment with hate speech detection on the Reddit comment datasets using this model.
Educational institutions leverage the LLNL/LUAR model to teach students about cross-domain transfer learning and author representation learning.
Features
Cross-domain author verification: Enables the transfer of learned author representations across different domains.
Zero-shot transfer learning: The model supports author verification without training data from the target domain.
Large-scale data processing: Capable of handling extensive datasets such as Amazon and Reddit comments.
Multiple pre-trained weights: Provides SBERT pre-trained weights for users to utilize directly or fine-tune further.
Easy result reproduction: Includes scripts for reproducing experimental results from the paper, helping researchers validate model performance.
Flexible path configuration: Users can customize storage paths for data and models by modifying the configuration file.
Multilingual support: While primarily in English, the model and code can process multilingual text.
How to Use
1. Set up the Python environment along with necessary dependencies by executing the provided script to create a virtual environment and install requirements.
2. Download and install the SBERT pre-trained weights, following the provided links and instructions.
3. Download and preprocess datasets as needed, including Reddit, Amazon, and Fanfiction datasets.
4. Modify the configuration file `file_config.ini` to set paths for data and model outputs.
5. Train and evaluate the model using provided scripts or command-line tools, for example, running `python main.py`.
6. Reproduce the results from the paper by executing the script `./scripts/reproduce/table_N.sh`.
7. Optionally, modify the code and submit a Pull Request to contribute to the project.
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