ecco
adaptnlp
| ecco | adaptnlp | |
|---|---|---|
| 6 | 2 | |
| 1,905 | 414 | |
| - | 0.0% | |
| 3.6 | 0.0 | |
| 3 months ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
ecco
-
[D] Visualizing attention
I ran into this a few days ago, might be useful https://github.com/jalammar/ecco
- Show HN: Language model analysis and visualization toolkit
-
[P] Ecco - Language model analysis and visualization toolkit
GitHub: https://github.com/jalammar/ecco Paper: https://aclanthology.org/2021.acl-demo.30/
-
Finding the Words to Say: Hidden State Visualizations for Language Models
Hello HN, author here. Language models are absolutely fascinating tools. I believe it would pay for software engineers to have a sense of their capabilities and how they function. The article showcases a few views to expose the inner workings of the model, but also simple UI for interacting with a language model to get a sense for how they work and generate words.
If you prefer video, I have also recently released a video [1] with PyData to provide an intro to language models and their applications and how we're trying to make Transformer-based ones more transparent with Ecco[2].
[1] https://www.youtube.com/watch?v=rHrItfNeuh0
[2] https://www.eccox.io/ and https://github.com/jalammar/ecco
Thanks mods for merging submissions. Happy to get feedback , thoughts, or questions.
-
Show HN: Ecco – See what your NLP language model is “thinking”
https://github.com/jalammar/ecco/blob/1e957a4c1c9bd49c203993...
adaptnlp
-
Tools to use for Semantic-searching Question Answering System
Check out adaptnlp
-
Case Sensitivity using HuggingFace & Google's T5 model (base)
Yes, there are capitals in the tokenizer vocabulary of t5-base and t5-small, so both support capitalization. A few days ago I was using t5-small through adaptnlp for extractive summarization and capitalization was working fine (https://github.com/Novetta/adaptnlp). AdaptNLP is basically just a transformers wrapper, so if you can't figure out a solution, you could just dissect their source code.
What are some alternatives?
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
keytotext - Keywords to Sentences
nlp-class - A Natural Language Processing course taught by Professor Ghassemi
fastai - The fastai deep learning library
muppetshow
gector - Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
VisionTransformer-Pytorch
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
TabularSemanticParsing - Translating natural language questions to a structured query language
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.