- attention-is-all-you-need-pytorch VS LFattNet
- attention-is-all-you-need-pytorch VS long-range-arena
- attention-is-all-you-need-pytorch VS BERT-pytorch
- attention-is-all-you-need-pytorch VS OpenPrompt
- attention-is-all-you-need-pytorch VS allennlp
- attention-is-all-you-need-pytorch VS transformer-pytorch
- attention-is-all-you-need-pytorch VS transformers
- attention-is-all-you-need-pytorch VS sru
- attention-is-all-you-need-pytorch VS attention-is-all-you-need-py
Attention-is-all-you-need-pytorch Alternatives
Similar projects and alternatives to attention-is-all-you-need-pytorch
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LFattNet
Attention-based View Selection Networks for Light-field Disparity Estimation
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BERT-pytorch
Google AI 2018 BERT pytorch implementation
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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long-range-arena
Long Range Arena for Benchmarking Efficient Transformers
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OpenPrompt
An Open-Source Framework for Prompt-Learning.
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allennlp
Discontinued An open-source NLP research library, built on PyTorch.
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transformer-pytorch
Transformer: PyTorch Implementation of "Attention Is All You Need"
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transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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sru
Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)
attention-is-all-you-need-pytorch reviews and mentions
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ElevenLabs Launches Voice Translation Tool to Break Down Language Barriers
The transformer model was invented to attend to context over the entire sequence length. Look at how the original authors used the Transformer for NMT in the original Vaswani et al publication. https://github.com/jadore801120/attention-is-all-you-need-py...
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Lack of activation in transformer feedforward layer?
I'm curious as to why the second matrix multiplication is not followed by an activation unlike the first one. Is there any particular reason why a non-linearity would be trivial or even avoided in the second operation? For reference, variations of this can be witnessed in a number of different implementations, including BERT-pytorch and attention-is-all-you-need-pytorch.
Stats
jadore801120/attention-is-all-you-need-pytorch is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of attention-is-all-you-need-pytorch is Python.