bertviz
ecco
| bertviz | ecco | |
|---|---|---|
| 15 | 6 | |
| 6,398 | 1,908 | |
| - | - | |
| 3.9 | 3.6 | |
| 9 months ago | 3 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
bertviz
-
StreamingLLM: tiny tweak to KV LRU improves long conversations
This seems only to work cause large GPTs have redundant, undercomplex attentions. See this issue in BertViz about attention in Llama: https://github.com/jessevig/bertviz/issues/128
-
[D] Is there a tool that indicates which parts of the input prompt impact the LLM's output the most?
https://github.com/jessevig/bertviz this could be helpful .. I was playing around with it a while ago to see how the attention weights are distributed across prompts
-
Show HN: Fully client-side GPT2 prediction visualizer
It would be interesting to have attention visualized as well, similar to how it's done in BertViz:
https://github.com/jessevig/bertviz
-
How to visualise LLMs ?
link for lazy: https://github.com/jessevig/bertviz
-
Ask HN: Can someone ELI5 Transformers and the “Attention is all we need” paper
The Illustrated Transfomer ( https://jalammar.github.io/illustrated-transformer/ ) and Visualizing attention ( https://towardsdatascience.com/deconstructing-bert-part-2-vi... ), are both really good resources. For a more ELI5 approach this non-technical explainer ( https://www.parand.com/a-non-technical-explanation-of-chatgp... ) covers it at a high level.
- Perplexity.ai Prompt Leakage
-
[Discussion] is attention an explanation?
You can get some information this way, but not everything you would want to know. You can try it yourself with BertViz.
-
using bert for relation extraction
2) BERT learns a lot in its embeddings: the BERTOLOGY paper (https://arxiv.org/abs/2002.12327) provides a great in-depth look at some of the broader linguistic traits that BERT learns. Different layers often learn different patterns, so the embeddings aren't really interpretable, but you can use something like bertviz (https://github.com/jessevig/bertviz) to explore attention weights across layers for predetermined examples
-
Maintaining context vs. overloading your Replika
I messed up a few things and mixed a couple others, anyways this site has a lot of decent information about it. https://towardsdatascience.com/deconstructing-bert-part-2-visualizing-the-inner-workings-of-attention-60a16d86b5c1
-
[D] code to visualize attention heads
Big fan of BertViz for this, widely used in research for this very purpose: https://github.com/jessevig/bertviz
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...
What are some alternatives?
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
BERT-pytorch - Google AI 2018 BERT pytorch implementation
nlp-class - A Natural Language Processing course taught by Professor Ghassemi
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
muppetshow
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
VisionTransformer-Pytorch
DeBERTa - The implementation of DeBERTa
TabularSemanticParsing - Translating natural language questions to a structured query language
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.