BERT-Relation-Extraction
bertviz
| BERT-Relation-Extraction | bertviz | |
|---|---|---|
| 1 | 15 | |
| 555 | 6,413 | |
| - | - | |
| 4.4 | 3.9 | |
| 8 months ago | 9 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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BERT-Relation-Extraction
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How to enable GPU with using github BERT model?
I'm currently following this github repo-https://github.com/plkmo/BERT-Relation-Extraction - in attempt to parse my own data later. When i'm trying to pre-train this model with cnn.txt the compute goes entirely to cpu/ram or so I assume. It has taken over 7h doing this so far with no sign of completion.
bertviz
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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
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[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
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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
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How to visualise LLMs ?
link for lazy: https://github.com/jessevig/bertviz
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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
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[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.
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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
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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
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[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
What are some alternatives?
ARElight - Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts, powered by AREkit
ecco - Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
BERT-pytorch - Google AI 2018 BERT pytorch implementation
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
DeBERTa - The implementation of DeBERTa
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
ManimML - ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.
scibert - A BERT model for scientific text.
Towards-A-Deep-and-Unified-Understanding-of-Deep-Neural-Models-in-NLP - Code implementation of paper Towards A Deep and Unified Understanding of Deep Neural Models in NLP
torchscale - Foundation Architecture for (M)LLMs