allennlp
attention-is-all-you-need-pytorch
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| allennlp | attention-is-all-you-need-pytorch | |
|---|---|---|
| 13 | 3 | |
| 11,337 | 8,456 | |
| - | - | |
| 8.4 | 0.0 | |
| over 1 year ago | 15 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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allennlp
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How to solve ConfigurationError using HuggingFace Token Classifier
No clue. So what I did was google the error. Here's what I found: https://github.com/allenai/allennlp/issues/4319
- AllenNLP will be unmaintained in December
- AllenNLP Is EOL
- Any recommendation for the replacement of the toolkit jiant? [Research] [Discussion]
- Cedille, the largest French language model, open source with a freely accessible playground
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[P] Cedille, the largest French language model (6b), released in open source
Another aspect we had fun with is dataset filtering. We have run the whole C4 French dataset through the Detoxify classifier to clean it up 🤬
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Any allennlp users in this sub?
https://github.com/allenai/allennlp/discussions looks active
- Multilingual C4 (mC4) Dataset now released
- C4 dataset released (800GB Common Crawl-derived text; T5 training data)
attention-is-all-you-need-pytorch
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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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Question: LLMs
I did implement an "LLM" proof of concept from scratch in a course for my masters, pretty much doing a small implementation of a transformer from the Attention is all you Need paper (plus other resources). It was useless, but was a great experience to understand how it works. There are a few implementation like this out there, like this one: https://github.com/jadore801120/attention-is-all-you-need-pytorch (first google result). I think it is a fun exercise (the amount of fun depends on how much of a masochist you are :) ).
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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.
What are some alternatives?
cedille-ai - ✒️ Cedille is a large French language model (6B), released under an open-source license
LFattNet - Attention-based View Selection Networks for Light-field Disparity Estimation
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
BERT-pytorch - Google AI 2018 BERT pytorch implementation
lm-evaluation-harness - A framework for few-shot evaluation of language models.
OpenPrompt - An Open-Source Framework for Prompt-Learning.
python-sutime - Python wrapper for Stanford CoreNLP's SUTime
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
PaddleHub - Awesome pre-trained models toolkit based on PaddlePaddle. (400+ models including Image, Text, Audio, Video and Cross-Modal with Easy Inference & Serving)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.