Reproduce The Results

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Reproducing the results of a research paper can be a challenging task, especially when it comes to complex models and datasets. In this article, we will walk you through the steps to reproduce the results reported in a paper, using a specific model and dataset.

Introduction

The paper in question uses a model called Mistral-7B-Instruct-v0.2, which is a large language model trained on a dataset of text prompts and responses. The model is used to classify papers into different categories based on their content and co-citation network. However, when we tried to reproduce the results, we encountered some issues.

What We Did

To reproduce the results, we followed these steps:

Step 1: Download the Model

We downloaded the model mistralai/Mistral-7B-Instruct-v0.2 to our local machine using the following command:

git clone https://github.com/mistralai/Mistral-7B-Instruct-v0.2.git

We then modified the model_name_or_path parameter in gofa.py to the corresponding path:

model_name_or_path = "/path/to/Mistral-7B-Instruct-v0.2"

Step 2: Run the Script

We ran the script python chat_gofa.py, which downloads the pretrained checkpoint into ./cache_data/model:

python chat_gofa.py

Step 3: Modify the Load Directory

We modified the load_dir parameter in inference_config.yaml to ./cache_data/model/mistral_qamag03_best_ckpt.pth:

load_dir: ./cache_data/model/mistral_qamag03_best_ckpt.pth

Step 4: Run the Inference Script

We ran the inference script via bash run_inference.sh:

bash run_inference.sh

The Results

After running the code, we got the following result:

cora_node_test/text_accuracy:0.000000±0.000000

However, when we checked the output, we found that some results were true, such as:

question: This is a co-citation network focusing on artificial intelligence, nodes represent academic papers and edges represent two papers that are co-cited by other papers. You are an expert in computer science. You need to choose the correct paper category based on the paper content and its co-citation network. For example, if the paper [NODEID.FK] focuses on extracting interpretable rules from data, used in classification and pattern discovery, choose Rule Learning; if the paper [NODEID.FK] involves the development of artificial neural networks for tasks like image recognition, speech processing, and natural language understanding, choose Neural Networks; if the paper [NODEID.FK] studies case-based reasoning systems where historical cases are used to solve new problems, applicable in diagnostics and recommendation systems, choose Case-Based; if the paper [NODEID.FK] covers optimization and search strategies inspired by natural selection, including crossover, mutation, and selection methods, choose Genetic Algorithms; if the paper [NODE.FK] encompasses fundamental theoretical studies in algorithms, computational complexity, and graph theory, choose Theory; if the paper [NODEID.FK] deals with learning optimal decision-making through rewards and penalties, useful in robotics and game ai, choose Reinforcement Learning; if the paper [NODEID.FK] focuses on models and algorithms for handling uncertainty, including bayesian networks and stochastic processes, used in predictive modeling and statistical analysis, choose Probabilistic Methods. What is the most likely paper category for the paper? Choose from the following: Rule Learning; Neural Networks; Case-Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.

target: Genetic Algorithms.

gen:  Based on the paper content and its co-citation network, the most likely paper category for the paper is Genetic Algorithms.</s>

Issues with the Results

We also tried modifying eval_task_names into cora_link, and the result was not zero, but it seemed unsatisfactory.

Conclusion

Reproducing the results of a research paper can be a challenging task, especially when it comes to complex models and datasets. In this article, we walked you through the steps to reproduce the results reported in a paper, using a specific model and dataset. However, we encountered some issues with the results, which we hope to resolve in future work.

Future Work

In future work, we plan to investigate the following:

  • Model modifications: We plan to modify the model to improve its performance on the task of paper classification.
  • Dataset modifications: We plan to modify the dataset to improve its quality and relevance to the task of paper classification.
  • Evaluation metrics: We plan to use different evaluation metrics to evaluate the performance of the model.

References

  • [1] Mistral-7B-Instruct-v0.2: A large language model trained on a dataset of text prompts and responses.
  • [2] Cora dataset: A dataset of academic papers and their co-citation networks.
  • [3] GOFa: A framework for evaluating the performance of models on the task of paper classification.

Code

The code used in this article is available on GitHub at https://github.com/username/reproduce-results.

Acknowledgments

We would like to thank the authors of the paper for their work on the Mistral-7B-Instruct-v0.2 model and the Cora dataset. We would also like to thank the reviewers for their feedback on this article.

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In our previous article, we walked you through the steps to reproduce the results reported in a paper, using a specific model and dataset. However, we encountered some issues with the results, which we hope to resolve in future work. In this article, we will answer some frequently asked questions (FAQs) related to reproducing the results.

Q: What is the Mistral-7B-Instruct-v0.2 model?

A: The Mistral-7B-Instruct-v0.2 model is a large language model trained on a dataset of text prompts and responses. It is a type of transformer-based model that uses self-attention mechanisms to process input sequences.

Q: What is the Cora dataset?

A: The Cora dataset is a dataset of academic papers and their co-citation networks. It is a widely used dataset in the field of natural language processing and machine learning.

Q: What is the task of paper classification?

A: The task of paper classification is to classify academic papers into different categories based on their content and co-citation network. In this task, the model is trained to predict the most likely category for a given paper.

Q: What are the challenges in reproducing the results?

A: There are several challenges in reproducing the results, including:

  • Model modifications: The model may need to be modified to improve its performance on the task of paper classification.
  • Dataset modifications: The dataset may need to be modified to improve its quality and relevance to the task of paper classification.
  • Evaluation metrics: Different evaluation metrics may be used to evaluate the performance of the model.

Q: How can I modify the model to improve its performance?

A: There are several ways to modify the model to improve its performance, including:

  • Increasing the model size: Increasing the size of the model can improve its performance on the task of paper classification.
  • Using different architectures: Using different architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), can improve the performance of the model.
  • Using different training objectives: Using different training objectives, such as masked language modeling or next sentence prediction, can improve the performance of the model.

Q: How can I modify the dataset to improve its quality and relevance?

A: There are several ways to modify the dataset to improve its quality and relevance, including:

  • Adding more data: Adding more data to the dataset can improve its quality and relevance to the task of paper classification.
  • Removing noisy data: Removing noisy data from the dataset can improve its quality and relevance to the task of paper classification.
  • Using different preprocessing techniques: Using different preprocessing techniques, such as tokenization or stemming, can improve the quality and relevance of the dataset.

Q: What are the evaluation metrics used to evaluate the performance of the model?

A: The evaluation metrics used to evaluate the performance of the model include:

  • Accuracy: The accuracy of the model is the proportion of correctly classified papers.
  • Precision: The precision of the model is the of correctly classified papers among all papers classified as belonging to a particular category.
  • Recall: The recall of the model is the proportion of correctly classified papers among all papers that belong to a particular category.

Q: How can I use the code to reproduce the results?

A: The code used in this article is available on GitHub at https://github.com/username/reproduce-results. You can use the code to reproduce the results by following the steps outlined in the previous article.

Q: What are the future work directions?

A: The future work directions include:

  • Model modifications: We plan to modify the model to improve its performance on the task of paper classification.
  • Dataset modifications: We plan to modify the dataset to improve its quality and relevance to the task of paper classification.
  • Evaluation metrics: We plan to use different evaluation metrics to evaluate the performance of the model.

Q: How can I contribute to the project?

A: You can contribute to the project by:

  • Forking the repository: Forking the repository on GitHub allows you to create a copy of the repository that you can modify and push changes to.
  • Submitting pull requests: Submitting pull requests allows you to propose changes to the repository and have them reviewed and merged by the project maintainers.
  • Reporting issues: Reporting issues allows you to report bugs or other problems with the project and have them fixed by the project maintainers.