zeroshot_topics
TabFormer
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| zeroshot_topics | TabFormer | |
|---|---|---|
| 3 | 10 | |
| 60 | 295 | |
| - | 3.7% | |
| 0.0 | 0.0 | |
| 11 months ago | 9 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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zeroshot_topics
TabFormer
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Time-based splitting performing significantly worse than random splitting
Hi, I am currently working on a basic binary classifier for a transaction dataset, to predict which transaction is fraudulent (Dataset: https://github.com/IBM/TabFormer). The following is a quick summary of the dataset:
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Question regarding Relational Graph Convolutional Network for a Fraud Detection problem
I am currently working on a transaction dataset (https://github.com/IBM/TabFormer/tree/main/data/credit_card) and I intend to build a fraud detection engine, but with tabular data transformed into a graph. I have used this article as my main outline for this approach: https://developer.nvidia.com/blog/optimizing-fraud-detection-in-financial-services-with-graph-neural-networks-and-nvidia-gpus/.
- TabFormer: NEW Data - star count:231.0
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[D] Neural Networks are not the only universal approximators, so why are they so uniquely effective?
When people talk about tabular data they mean something with like <100 columns where your classification might strongly depend on a handful of specific ones. There is of course a regime where data is "somewhat" tabular (some NLP problems) so it's not entirely well-defined. And there are NN architecture for tabular data like the tabformer.
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
kogpt - KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
cleanlab - The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models. [Moved to: https://github.com/cleanlab/cleanlab]
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
awesome-open-data-annotation - Open Source Data Annotation & Labeling Tools
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
cappr - Completion After Prompt Probability. Make your LLM make a choice
falcongpt - Simple GPT app that uses the falcon-7b-instruct model with a Flask front-end.