bert
ojg
bert | ojg | |
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49 | 17 | |
37,036 | 796 | |
0.6% | - | |
0.0 | 7.0 | |
24 days ago | 4 days ago | |
Python | Go | |
Apache License 2.0 | MIT License |
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bert
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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
- Google internally developed chatbots like ChatGPT years ago
ojg
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Interactive Examples for Learning Jq
I found Jq to be difficult to use which is why Oj, https://github.com/ohler55/ojg is based on JSONPath. There still are a lot of options but it only takes a couple of help screens to figure out what the options are.
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Building a high performance JSON parser
You might want to take a look at https://github.com/ohler55/ojg. It takes a different approach with a single pass parser. There are some performance benchmarks included on the README.md landing page.
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A Journey building a fast JSON parser and full JSONPath
I like the "Simple Encoding Notation" (SEN) of the underlying library: https://github.com/ohler55/ojg/blob/develop/sen.md
- Oj Is on Tap
- SEN: Simple Encoding Notation
- The fastest tool for querying large JSON files is written in Python (benchmark)
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FX: An interactive alternative to jq to process JSON
Another alternative is the oj app (ojg/cmd/oj) which is part of https://github.com/ohler55/ojg. It relies on JSONPath for extraction and manipulation of JSON.
- Go 1.17 Release Notes
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OjG now has a tokenizer that is almost 10 times faster than json.Decode
I promise to add more examples but in the mean time there are the test files. The one for Unmarshal is https://github.com/ohler55/ojg/blob/develop/oj/unmashall_test.go
- The Pretty JSON Revolution
What are some alternatives?
NLTK - NLTK Source
jsonparser - One of the fastest alternative JSON parser for Go that does not require schema
bert-sklearn - a sklearn wrapper for Google's BERT model
jsonic - All you need with JSON
pysimilar - A python library for computing the similarity between two strings (text) based on cosine similarity
fastjson - Fast JSON parser and validator for Go. No custom structs, no code generation, no reflection
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
ask - A Go package that provides a simple way of accessing nested properties in maps and slices.
PURE - [NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
jettison - Highly configurable, fast JSON encoder for Go
NL_Parser_using_Spacy - NLP parser using NER and TDD
json2go - Create go type representation from json