convert github code to colab or kaggle notebook with minor modifications -- 2
Budget: $14 – $15 AUD
BUDGET IS FIXED. DONT WASTE YOUR TIME QUOTING IF YOU GONNA BID MORE.
I am in search of a freelancer with the capability to transform my existing code, available on GitHub at the following link: https://github.com/ramber1836/TASD, into either a Colab or Kaggle notebook. The existing code is authored in Python and makes use of PyTorch. It is imperative that the selected freelancer not only replicates the code but also executes the associated model. Furthermore, the candidate should provide a demonstrative example of "Case C" as depicted in Figure 6 of the document available at: https://arxiv.org/pdf/2301.02071.pdf.
In particular, the notebook should possess the capability to take the "FL_input.txt" file as input and generate output similar to the following:
"Table 4 lists the results of different approaches on various measures, including consistency, novelty, diversity, and coherence. We can observe that both the 'w/o adversarial' and 'w/o memory' strategies yield identical consistency results, thus validating the effectiveness of these strategies. Additionally, the 'dynamic strategy' produces higher novelty, confirming its effectiveness."
The ideal candidate for this project should be well-versed in Python programming and possess a proficiency in converting code to Colab or Kaggle notebooks.
I am in search of a freelancer with the capability to transform my existing code, available on GitHub at the following link: https://github.com/ramber1836/TASD, into either a Colab or Kaggle notebook. The existing code is authored in Python and makes use of PyTorch. It is imperative that the selected freelancer not only replicates the code but also executes the associated model. Furthermore, the candidate should provide a demonstrative example of "Case C" as depicted in Figure 6 of the document available at: https://arxiv.org/pdf/2301.02071.pdf.
In particular, the notebook should possess the capability to take the "FL_input.txt" file as input and generate output similar to the following:
"Table 4 lists the results of different approaches on various measures, including consistency, novelty, diversity, and coherence. We can observe that both the 'w/o adversarial' and 'w/o memory' strategies yield identical consistency results, thus validating the effectiveness of these strategies. Additionally, the 'dynamic strategy' produces higher novelty, confirming its effectiveness."
The ideal candidate for this project should be well-versed in Python programming and possess a proficiency in converting code to Colab or Kaggle notebooks.