EfficientNet-PyTorch
kiri
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| EfficientNet-PyTorch | kiri | |
|---|---|---|
| 2 | 12 | |
| 7,715 | 240 | |
| - | 0.0% | |
| 0.0 | 3.2 | |
| about 2 years ago | almost 3 years ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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EfficientNet-PyTorch
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[D] MCDropout and CNNs
I used this with the popular pytorch implementation of EfficientNet. You can see what I'm talking about here https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py on line 127. Once you understand this code it is pretty straightforward to modify your forward pass to allow "stochastic depth" during inference.
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[P] Backprop: a library to easily finetune and use state-of-the-art models
I dont see you credit the author of https://github.com/lukemelas/EfficientNet-PyTorch yet you're using his implementation for efficientnet.
kiri
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[P][D] NLP question - Question Answering AI
I'm one of the authors of Backprop, a library built for transfer learning.
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Backprop: Use and finetune models in a single line of code
I'd like to share Backprop, an open source library I've been co-authoring for the last few months.
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[P] Backprop Model Hub: a curated list of state-of-the-art models
We've also got an open-source library that makes using + finetuning these models possible in a few lines of code.
- Show HN: Backprop – a simple library to use and finetune state-of-the-art models
- Show HN: Backprop – a library to easily finetune and use state-of-the-art models
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[P] Backprop: a library to easily finetune and use state-of-the-art models
I'd like to share Backprop, a Python library I've been co-authoring for the last few months. Our goal is to make finetuning and using models as easy as possible, even without extensive ML experience.
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GPT Neo: open-source GPT-3-like model with pretrained weights available
You might get some really promising results with finetuning.
If anything, you could build writing assistance that almost automates responses.
I've been co-authoring a library that lets you finetune such models in a single line of code.
https://github.com/backprop-ai/backprop
In specific the text generation finetuning example should be what you are looking for: https://github.com/backprop-ai/backprop/blob/main/examples/F...
Hope this helps, happy to chat more about it. Pretty curious about the results.
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NLP Model for extracting specific text from raw text
Here's an example Jupyter Notebook for finetuning T5. Full disclosure, I work on this library myself -- but it could be helpful.
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[D] Need help with document classifier and later prediction of text
I'm working on a library that hopefully makes working with some of these a bit easier -- here's an example notebook for running text classification with the BART checkpoint, if you're interested. If you need more task-specific finetuning for text classification, that's going to be rolled out in the near future.
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Generating notes from text
I'm working on a library that includes a few different ML tasks, including summarisation. It uses a pretrained version of Google's T5 transformer model, which we host on Hugging Face with some details on how it was trained.
What are some alternatives?
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
BIOBSS - A package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
MLclf - mini-imagenet and tiny-imagent dataset transformation for traditional classification task and also for the format for few-shot learning / meta-learning tasks
qagnn - [NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
DropoutUncertaintyExps - Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
BERTweet - BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.