BERT-pytorch
bertviz
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| BERT-pytorch | bertviz | |
|---|---|---|
| 1 | 15 | |
| 5,995 | 6,377 | |
| - | - | |
| 0.0 | 3.9 | |
| 8 months ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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BERT-pytorch
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Lack of activation in transformer feedforward layer?
I'm curious as to why the second matrix multiplication is not followed by an activation unlike the first one. Is there any particular reason why a non-linearity would be trivial or even avoided in the second operation? For reference, variations of this can be witnessed in a number of different implementations, including BERT-pytorch and attention-is-all-you-need-pytorch.
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?
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
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).
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
scibert - A BERT model for scientific text.
DeBERTa - The implementation of DeBERTa
cuad - CUAD (NeurIPS 2021)
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).