jiant
jiant is an nlp toolkit (by nyu-mll)
bertviz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.) (by jessevig)
jiant | bertviz | |
---|---|---|
2 | 15 | |
1,608 | 6,398 | |
0.5% | - | |
0.0 | 3.9 | |
10 months ago | 9 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
jiant
Posts with mentions or reviews of jiant.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-11.
-
Any recommendation for the replacement of the toolkit jiant? [Research] [Discussion]
I am doing research in NLP with the toolkit jiant (https://github.com/nyu-mll/jiant). It is a quite nice and easy-to-use tool. Unfortunately, it stopped being maintained. I wonder is there any other recommendation that I can use to replace it?
-
Looking for a code base to implement multi-task learning in NLP
Jiant should fulfill 1, 2, 4 and 5.
bertviz
Posts with mentions or reviews of bertviz.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-17.
-
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