text-to-text-transfer-tra
bert
text-to-text-transfer-tra | bert | |
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2 | 50 | |
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- | 19 days ago | |
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- | Apache License 2.0 |
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text-to-text-transfer-tra
- AlphaCode by DeepMind
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New text-to-image network from Google beats DALL-E
T5 was open-sourced on release (up to 11B params): https://github.com/google-research/text-to-text-transfer-tra...
It is also available via Hugging Face transformers.
Unclear if 11B is the T5-XXL mentioned in the paper, however.
bert
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Zero Shot Text Classification Under the hood
In 2019, a new language representation called BERT (Bedirectional Encoder Representation from Transformers) was introduced. The main idea behind this paradigm is to first pre-train a language model using a massive amount of unlabeled data then fine-tune all the parameters using labeled data from the downstream tasks. This allows the model to generalize well to different NLP tasks. Moreover, it has been shown that this language representation model can be used to solve downstream tasks without being explicitly trained on, e.g classify a text without training phase.
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OpenAI – Application for US trademark "GPT" has failed
task-specific parameters, and is trained on the downstream tasks by simply fine-tuning all pre-trained parameters.
[0] https://arxiv.org/abs/1810.04805
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Integrate LLM Frameworks
The release of BERT in 2018 kicked off the language model revolution. The Transformers architecture succeeded RNNs and LSTMs to become the architecture of choice. Unbelievable progress was made in a number of areas: summarization, translation, text classification, entity classification and more. 2023 tooks things to another level with the rise of large language models (LLMs). Models with billions of parameters showed an amazing ability to generate coherent dialogue.
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Embeddings: What they are and why they matter
The general idea is that you have a particular task & dataset, and you optimize these vectors to maximize that task. So the properties of these vectors - what information is retained and what is left out during the 'compression' - are effectively determined by that task.
In general, the core task for the various "LLM tools" involves prediction of a hidden word, trained on very large quantities of real text - thus also mirroring whatever structure (linguistic, syntactic, semantic, factual, social bias, etc) exists there.
If you want to see how the sausage is made and look at the actual algorithms, then the key two approaches to read up on would probably be Mikolov's word2vec (https://arxiv.org/abs/1301.3781) with the CBOW (Continuous Bag of Words) and Continuous Skip-Gram Model, which are based on relatively simple math optimization, and then on the BERT (https://arxiv.org/abs/1810.04805) structure which does a conceptually similar thing but with a large neural network that can learn more from the same data. For both of them, you can either read the original papers or look up blog posts or videos that explain them, different people have different preferences on how readable academic papers are.
- Ernie, China's ChatGPT, Cracks Under Pressure
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Ask HN: How to Break into AI Engineering
Could you post a link to "the BERT paper"? I've read some, but would be interested reading anything that anyone considered definitive :) Is it this one? "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" :https://arxiv.org/abs/1810.04805
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How to leverage the state-of-the-art NLP models in Rust
Rust crate rust_bert implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:
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Notes on training BERT from scratch on an 8GB consumer GPU
The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model.
Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations.
As for the SOPHIA optimizer, it is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases.
[1] https://arxiv.org/abs/1810.04805
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List of AI-Models
Click to Learn more...
- Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding
What are some alternatives?
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
NLTK - NLTK Source
text-to-text-transfer-transformer - Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
bert-sklearn - a sklearn wrapper for Google's BERT model
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
pysimilar - A python library for computing the similarity between two strings (text) based on cosine similarity
majesty-diffusion - Majesty Diffusion by @Dango233(@Dango233max) and @apolinario (@multimodalart)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
DeepCreamPy - deeppomf's DeepCreamPy + some updates
PURE - [NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
hent-AI - Automation of censor bar detection
NL_Parser_using_Spacy - NLP parser using NER and TDD