text-to-text-transfer-transformer
nlu
text-to-text-transfer-transformer | nlu | |
---|---|---|
29 | 25 | |
5,909 | 809 | |
1.1% | 0.9% | |
5.0 | 8.6 | |
3 months ago | 9 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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text-to-text-transfer-transformer
- T5: Text-to-Text-Transfer-Transformer
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Gemma: New Open Models
Google released the T5 paper about 5 years ago:
https://arxiv.org/abs/1910.10683
This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.
Following GPT-3, it became much more common for labs to not release full details or model weights. Prior to that, it was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights.
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[P] Free and Fast LLM Finetuning
[2] - https://arxiv.org/abs/1910.10683
- Free and Fast LLM Finetuning
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[Discussion] Is there a better way than positional encodings in self attention?
T5-style relative encodings https://arxiv.org/abs/1910.10683
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What were the 40 research papers on the list Ilya Sutskever gave John Carmack?
11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)
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[P] T5 Implementation in PyTorch
You can find a link to the paper here: https://arxiv.org/abs/1910.10683
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Text-to-Text Transformer (T5-Base Model) Testing For Summarization, Sentiment Classification, and Translation Using Pytorch and Torchtext
The Text-to-Text Transformer is a type of neural network architecture that is particularly well-suited for natural language processing tasks involving the generation of text. It was introduced in the paper "Attention is All You Need" by Vaswani et al. and has since become a popular choice for many NLP tasks, including language translation, summarization, and text generation
- AlphaCode by DeepMind
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[R] LiBai: a large-scale open-source model training toolbox
Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here
nlu
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1 line to visualizations for dependency trees, entity relationships, resolution, assertion, NER and new models for Afrikaans, Welsh, Maltese, Tamil, and Vietnamese - John Snow Labs NLU 3.0.1 for Python
See the visualization tutorial notebook and visualization docs for more info.
All of the 140+ NLU tutorial Notebooks have been updated and reworked to reflect the latest changes in NLU 3.0.0+
- 200+SOTA Medical NLP Models NER, Resolution, Relations – John Snow Labs NLU3.0.0
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200+ State of the Art Medical Models for NER, Entity Resolution, Relation Extraction, Assertion, Spark 3 and Python 3.8 support - John Snow Labs NLU 3.0.0
Graph NLU tutorial
Entity Resolution overview notebook
Relation extraction notebook
Medical Named Entity Extraction (NER) notebook
NLU on Github
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
spark-nlp - State of the Art Natural Language Processing
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
spark-nlp-display - A library for the simple visualization of different types of Spark NLP annotations.
DeepCreamPy - Decensoring Hentai with Deep Neural Networks
streamlit-webrtc-example - Real time video and audio processing examples with Streamlit and streamlit-webrtc
dalle-mini - DALL·E Mini - Generate images from a text prompt
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
BERTweet - BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
majesty-diffusion - Majesty Diffusion by @Dango233(@Dango233max) and @apolinario (@multimodalart)
PromCSE - Code for "Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (EMNLP 2022)"