quickai
TabFormer
| quickai | TabFormer | |
|---|---|---|
| 7 | 10 | |
| 162 | 296 | |
| - | 2.4% | |
| 3.7 | 0.0 | |
| about 1 month ago | 9 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
quickai
- Show HN: QuickAI Version 2 Released
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QuickAI version 2 released!
I originally released QuickAI here. I am very excited to announce version 2 of QuickAI
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QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
GitHub: https://github.com/geekjr/quickai
- Show HN: Quickai – Quickly experiment with state-of-the-art ML models
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quickai - A Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
Yeah, totally agree. https://github.com/geekjr/quickai/blob/main/quickai/image_classification.py does really need some reworking. Dicts are the way to go. But once that's done, I think it could actually be a practical lib!
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?
detoxify - Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
segyio - Fast Python library for SEGY files.
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
zeroshot_topics - Topic Inference with Zeroshot models
chappie.ai - Generalized AI to perform a multitude of tasks written in python3
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
falcongpt - Simple GPT app that uses the falcon-7b-instruct model with a Flask front-end.