telekinesis
bert
telekinesis | bert | |
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12 | 49 | |
16 | 37,036 | |
- | 0.6% | |
5.6 | 0.0 | |
28 days ago | 23 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
telekinesis
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Show HN: Sort and Filter Ask HN Who's Hiring by LLM-Embedding Proximity
https://payperrun.com/%3E/search?displayParams={%22q%22:%22S...
(There are quite a few, you might want to filter by date!)
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Ask HN: Who is hiring? (November 2023)
Hey everyone, I just made this thread easier to search through here:
https://payperrun.com/%3E/search?displayParams={%22q%22:%22D...
It uses LLM embeddings to sort postsby semantic proximity, but you can also filter out posts with comma separated values like this:
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Ask HN: What do you regret doing or not doing in your 30s?
https://news.ycombinator.com/item?id=33118584
[Shameless plug: I found all these on my llm-embedding based search engine I launched today: https://payperrun.com/%3E/search?displayParams={%22q%22:%22A...
It's much better than HN's default search: https://hn.algolia.com/?q=Ask+HN%3A+What+do+you+regret+doing... ]
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My thoughts on starting an online business as someone who's never done it before
https://payperrun.com/%3E/search?displayParams={%22q%22:%22A...
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We should promote more personal indexing, rather than algorhythmic indexing
There have been a few attempts at a crowdsourced-rank search engine (which is similar to what you're suggesting - people indexing the content), but it seems to be a hard cookie, most of the examples of similar ideas I could find on ProductHunt or ShowHN seem dead:
https://payperrun.com/%3E/search?displayParams={%22q%22:%22c...
(btw, I just launched this llm-embedding based search service that lets you check if a startup idea has already been tried/failed).
I don't know if this idea has a higher death rate than the baseline, but my guess is Google/PageRank is good enough for most use-cases, and then if you want quality sources, you can just follow them on YouTube, Twitter, Instagram, etc. Wait, maybe I shouldn't try to compete with Google?
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Show HN: An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More
I like the section on how it works: https://payperrun.com/%3E/search?display=How%20this%20servic...
The vector search is using https://lancedb.com/ and OpenAI embeddings.
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Embeddings: What they are and why they matter
Behaves as I expected now!
I went here looking for more info about payperrun https://payperrun.com/%3E/welcome and clicked on the "Spotlight" section and saw 4 popups blocked - I never see popups anywhere these days and have to admit that sends me away pretty quickly.
- Show HN: Payperrun.com – A New Way to Monetize Your Code
- telekinesis: Just-in-time SDKs
- Show HN: Just-in-Time SDKs
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
What are some alternatives?
chasr-server - End-To-End Encrypted GPS Tracking Service
NLTK - NLTK Source
terra.py - Python SDK for Terra
bert-sklearn - a sklearn wrapper for Google's BERT model
pyxet - Python SDK for XetHub
pysimilar - A python library for computing the similarity between two strings (text) based on cosine similarity
DBoW2 - Enhanced hierarchical bag-of-word library for C++
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
PURE - [NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
vectordb - A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.
NL_Parser_using_Spacy - NLP parser using NER and TDD