machine learning guide lots of links, broad topics

This repository contains a hand-curated of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, and transfer learning in NLP.

Transformer (BERT) (Source)

Table of Contents

Papers

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.

2. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V. Le and Ruslan Salakhutdinov.

• Uses smart caching to improve the learning of long-term dependency in Transformer. Key results: state-of-art on 5 language modeling benchmarks, including ppl of 21.8 on One Billion Word (LM1B) and 0.99 on enwiki8. The authors claim that the method is more flexible, faster during evaluation (1874 times speedup), generalizes well on small datasets, and is effective at modeling short and long sequences.

2. Conditional BERT Contextual Augmentation by Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han and Songlin Hu.

3. SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering by Chenguang Zhu, Michael Zeng and Xuedong Huang.

4. Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.

5. The Evolved Transformer by David R. So, Chen Liang and Quoc V. Le.

• They used architecture search to improve Transformer architecture. Key is to use evolution and seed initial population with Transformer itself. The architecture is better and more efficient, especially for small size models.

6. XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.

• A new pretraining method for NLP that significantly improves upon BERT on 20 tasks (e.g., SQuAD, GLUE, RACE).

• “Transformer-XL is a shifted model (each hyper-column ends with next token) while XLNet is a direct model (each hyper-column ends with contextual representation of same token).” — Thomas Wolf.

• Comments from HN:

7. CTRL: Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Richard Socher et al. [Code].

8. PLMpapers - BERT (Transformer, transfer learning) has catalyzed research in pretrained language models (PLMs) and has sparked many extensions. This repo contains a list of papers on PLMs.

9. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Google Brain.

• The group perform a systematic study of transfer learning for NLP using a unified Text-to-Text Transfer Transformer (T5) model and push the limits to achieve SoTA on SuperGLUE (approaching human baseline), SQuAD, and CNN/DM benchmark. [Code].

10. Reformer: The Efficient Transformer by Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya.

• “They present techniques to reduce the time and memory complexity of Transformer, allowing batches of very long sequences (64K) to fit on one GPU. Should pave way for Transformer to be really impactful beyond NLP domain.” — @hardmaru

11. Supervised Multimodal Bitransformers for Classifying Images and Text (MMBT) by Facebook AI.

12. A Primer in BERTology: What we know about how BERT works by Anna Rogers et al.

• “Have you been drowning in BERT papers?”. The group survey over 40 papers on BERT’s linguistic knowledge, architecture tweaks, compression, multilinguality, and so on.

12. tomohideshibata/BERT-related papers

13. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by Google Brain. [Code] | [Blog post (unofficial)]

• Key idea: the architecture use a subset of parameters on every training step and on each example. Upside: model train much faster. Downside: super large model that won’t fit in a lot of environments.

14. An Attention Free Transformer by Apple.

15. A Survey of Transformers by Tianyang Lin et al.

16. Evaluating Large Language Models Trained on Code by OpenAI.

• Codex, a GPT language model that powers GitHub Copilot.

• They investigate their model limitations (and strengths).

• They discuss the potential broader impacts of deploying powerful code generation techs, covering safety, security, and economics.

17. Training language models to follow instructions with human feedback by OpenAI. They call the resulting models InstructGPT. ChatGPT is a sibling model to InstructGPT.

18. LaMDA: Language Models for Dialog Applications by Google.

19. Training Compute-Optimal Large Language Models by Hoffmann et al. at DeepMind. TLDR: introduces a new 70B LM called “Chinchilla” that outperforms much bigger LMs (GPT-3, Gopher). DeepMind has found the secret to cheaply scale large language models — to be compute-optimal, model size and training data must be scaled equally. It shows that most LLMs are severely starved of data and under-trained. Given the new scaling law, even if you pump a quadrillion parameters into a model (GPT-4 urban myth), the gains will not compensate for 4x more training tokens.

Articles

BERT and Transformer

1. Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing from Google AI.

2. The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning).

3. Dissecting BERT by Miguel Romero and Francisco Ingham - Understand BERT in depth with an intuitive, straightforward explanation of the relevant concepts.

4. A Light Introduction to Transformer-XL.

5. Generalized Language Models by Lilian Weng, Research Scientist at OpenAI.

6. What is XLNet and why it outperforms BERT

• Permutation Language Modeling objective is the core of XLNet.

6. DistilBERT (from HuggingFace), released together with the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT.

7. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations paper from Google Research and Toyota Technological Institute. — Improvements for more efficient parameter usage: factorized embedding parameterization, cross-layer parameter sharing, and Sentence Order Prediction (SOP) loss to model inter-sentence coherence. [Blog post | Code]

8. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators by Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning - A BERT variant like ALBERT and cost less to train. They trained a model that outperforms GPT by using only one GPU; match the performance of RoBERTa by using 1/4 computation. It uses a new pre-training approach, called replaced token detection (RTD), that trains a bidirectional model while learning from all input positions. [Blog post | Code]

9. Visual Paper Summary: ALBERT (A Lite BERT)

Attention Concept

1. The Annotated Transformer by Harvard NLP Group - Further reading to understand the “Attention is all you need” paper.

2. Attention? Attention! - Attention guide by Lilian Weng from OpenAI.

3. Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) by Jay Alammar, an Instructor from Udacity ML Engineer Nanodegree.

4. Making Transformer networks simpler and more efficient - FAIR released an all-attention layer to simplify the Transformer model and an adaptive attention span method to make it more efficient (reduce computation time and memory footprint).

5. What Does BERT Look At? An Analysis of BERT’s Attention paper by Stanford NLP Group.

Transformer Architecture

1. The Transformer blog post.

2. The Illustrated Transformer by Jay Alammar, an Instructor from Udacity ML Engineer Nanodegree.

3. Watch Łukasz Kaiser’s talk walking through the model and its details.

4. Transformer-XL: Unleashing the Potential of Attention Models by Google Brain.

5. Generative Modeling with Sparse Transformers by OpenAI - an algorithmic improvement of the attention mechanism to extract patterns from sequences 30x longer than possible previously.

6. Stabilizing Transformers for Reinforcement Learning paper by DeepMind and CMU - they propose architectural modifications to the original Transformer and XL variant by moving layer-norm and adding gating creates Gated Transformer-XL (GTrXL). It substantially improve the stability and learning speed (integrating experience through time) in RL.

7. The Transformer Family by Lilian Weng - since the paper “Attention Is All You Need”, many new things have happened to improve the Transformer model. This post is about that.

8. DETR (DEtection TRansformer): End-to-End Object Detection with Transformers by FAIR - :fire: Computer vision has not yet been swept up by the Transformer revolution. DETR completely changes the architecture compared with previous object detection systems. (PyTorch Code and pretrained models). “A solid swing at (non-autoregressive) end-to-end detection. Anchor boxes + Non-Max Suppression (NMS) is a mess. I was hoping detection would go end-to-end back in ~2013)” — Andrej Karpathy

9. Transformers for software engineers - This post will be helpful to software engineers who are interested in learning ML models, especially anyone interested in Transformer interpretability. The post walk through a (mostly) complete implementation of a GPT-style Transformer, but the goal will not be running code; instead, they use the language of software engineering and programming to explain how these models work and articulate some of the perspectives they bring to them when doing interpretability work.

10. Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance - PaLM is a dense decoder-only Transformer model trained with the Pathways system, which enabled Google to efficiently train a single model across multiple TPU v4 Pods. The example explaining a joke is remarkable. This shows that it can generate explicit explanations for scenarios that require a complex combination of multi-step logical inference, world knowledge, and deep language understanding.

Generative Pre-Training Transformer (GPT)

1. Better Language Models and Their Implications.

2. Improving Language Understanding with Unsupervised Learning - this is an overview of the original OpenAI GPT model.

3. 🦄 How to build a State-of-the-Art Conversational AI with Transfer Learning by Hugging Face.

4. The Illustrated GPT-2 (Visualizing Transformer Language Models) by Jay Alammar.

5. MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism by NVIDIA ADLR.

6. OpenGPT-2: We Replicated GPT-2 Because You Can Too - the authors trained a 1.5 billion parameter GPT-2 model on a similar sized text dataset and they reported results that can be compared with the original model.

7. MSBuild demo of an OpenAI generative text model generating Python code [video] - The model that was trained on GitHub OSS repos. The model uses English-language code comments or simply function signatures to generate entire Python functions. Cool!

8. GPT-3: Language Models are Few-Shot Learners (paper) by Tom B. Brown (OpenAI) et al. - “We train GPT-3, an autoregressive language model with 175 billion parameters :scream:, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.”

9. elyase/awesome-gpt3 - A collection of demos and articles about the OpenAI GPT-3 API.

10. How GPT3 Works - Visualizations and Animations by Jay Alammar.

11. GPT-Neo - Replicate a GPT-3 sized model and open source it for free. GPT-Neo is “an implementation of model parallel GPT2 & GPT3-like models, with the ability to scale up to full GPT3 sizes (and possibly more!), using the mesh-tensorflow library.” [Code].

12. GitHub Copilot, powered by OpenAI Codex - Codex is a descendant of GPT-3. Codex translates natural language into code.

13. GPT-4 Rumors From Silicon Valley - GPT-4 is almost ready. GPT-4 would be multimodal, accepting text, audio, image, and possibly video inputs. Release window: Dec - Feb. #hype

14. New GPT-3 model: text-Davinci-003 - Improvements:

• Handle more complex intents — you can get even more creative with how you make use of its capabilities now.

• Higher quality writing — clearer, more engaging, and more compelling content.

• Better at longer form content generation.

15. ChatGPT blog post and link to the conversational interface.

• ChatGPT is OpenAI’s newest language model fine-tuned from a model in the GPT-3.5 series (which finished training in early 2022), optimized for dialogue. It is trained using Reinforcement Learning from Human Feedback; human AI trainers provide supervised fine-tuning by playing both sides of the conversation.

• Is it evidently better than GPT-3 at following user instructions and context? People have noticed, ChatGPT’s output quality seems to represent a notable improvement over previous GPT-3 models.

Large Language Model (LLM)

1. GPT-J-6B - Can’t access GPT-3? Here’s GPT-J — its open-source cousin.

2. Fun and Dystopia With AI-Based Code Generation Using GPT-J-6B - Prior to GitHub Copilot tech preview launch, Max Woolf, a data scientist tested GPT-J-6B’s code “writing” abilities.

3. GPT-Code-Clippy (GPT-CC) - An open source version of GitHub Copilot. The GPT-CC models are fine-tuned versions of GPT-2 and GPT-Neo.

4. GPT-NeoX-20B - A 20 billion parameter model trained using EleutherAI’s GPT-NeoX framework. They expect it to perform well on many tasks. You can try out the model on GooseAI playground.

5. Metaseq - A codebase for working with Open Pre-trained Transformers (OPT).

6. YaLM 100B by Yandex is a GPT-like pretrained language model with 100B parameters for generating and processing text. It can be used freely by developers and researchers from all over the world.

7. BigScience’s BLOOM-176B from the Hugging Face repository [paper, blog post] - BLOOM is a 175-billion parameter model for language processing, able to generate text much like GPT-3 and OPT-175B. It was developed to be multilingual, being deliberately trained on datasets containing 46 natural languages and 13 programming languages.

8. bitsandbytes-Int8 inference for Hugging Face models - You can run BLOOM-176B/OPT-175B easily on a single machine, without performance degradation. If true, this could be a game changer in enabling people outside of big tech companies being able to use these LLMs.

Additional Reading

1. How to Build OpenAI’s GPT-2: “The AI That’s Too Dangerous to Release”.

2. OpenAI’s GPT2 - Food to Media hype or Wake Up Call?

3. How the Transformers broke NLP leaderboards by Anna Rogers. :fire::fire::fire:

• A well put summary post on problems with large models that dominate NLP these days.

• Larger models + more data = progress in Machine Learning research :question:

4. Transformers From Scratch tutorial by Peter Bloem.

5. Real-time Natural Language Understanding with BERT using NVIDIA TensorRT on Google Cloud T4 GPUs achieves 2.2 ms latency for inference. Optimizations are open source on GitHub.

6. NLP’s Clever Hans Moment has Arrived by The Gradient.

7. Language, trees, and geometry in neural networks - a series of expository notes accompanying the paper, “Visualizing and Measuring the Geometry of BERT” by Google’s People + AI Research (PAIR) team.

8. Benchmarking Transformers: PyTorch and TensorFlow by Hugging Face - a comparison of inference time (on CPU and GPU) and memory usage for a wide range of transformer architectures.

9. Evolution of representations in the Transformer - An accessible article that presents the insights of their EMNLP 2019 paper. They look at how the representations of individual tokens in Transformers trained with different objectives change.

10. The dark secrets of BERT - This post probes fine-tuned BERT models for linguistic knowledge. In particular, the authors analyse how many self-attention patterns with some linguistic interpretation are actually used to solve downstream tasks. TL;DR: They are unable to find evidence that linguistically interpretable self-attention maps are crucial for downstream performance.

11. A Visual Guide to Using BERT for the First Time - Tutorial on using BERT in practice, such as for sentiment analysis on movie reviews by Jay Alammar.

12. Turing-NLG: A 17-billion-parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. This work would not be possible without breakthroughs produced by the DeepSpeed library (compatible with PyTorch) and ZeRO optimizer, which can be explored more in this accompanying blog post.

13. MUM (Multitask Unified Model): A new AI milestone for understanding information by Google.

• Based on transformer architecture but more powerful.

• Multitask means: supports text and images, knowledge transfer between 75 languages, understand context and go deeper in a topic, and generate content.

14. GPT-3 is No Longer the Only Game in Town - GPT-3 was by far the largest AI model of its kind last year (2020). Now? Not so much.

15. OpenAI’s API Now Available with No Waitlist - GPT-3 access without the wait. However, apps must be approved before going live. This release also allow them to review applications, monitor for misuse, and better understand the effects of this tech.

16. The Inherent Limitations of GPT-3 - One thing missing from the article if you’ve read Gwern’s GPT-3 Creative Fiction article before is the mystery known as “Repetition/Divergence Sampling”:

when you generate free-form completions, they have a tendency to eventually fall into repetitive loops of gibberish.

For those using Copilot, you should have experienced this wierdness where it generates the same line or block of code over and over again.

17. Language Modelling at Scale: Gopher, Ethical considerations, and Retrieval by DeepMind - The paper present an analysis of Transformer-based language model performance across a wide range of model scales — from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.

18. Competitive programming with AlphaCode by DeepMind - AlphaCode uses transformer-based language models to generate code that can create novel solutions to programming problems which require an understanding of algorithms.

19. Building games and apps entirely through natural language using OpenAI’s code-davinci model - The author built several small games and apps without touching a single line of code, simply by telling the model what they want.

20. Open AI gets GPT-3 to work by hiring an army of humans to fix GPT’s bad answers

21. GPT-3 can run code - You provide an input text and a command and GPT-3 will transform them into an expected output. It works well for tasks like changing coding style, translating between programming languages, refactoring, and adding doc. For example, converts JSON into YAML, translates Python code to JavaScript, improve the runtime complexity of the function.

22. Using GPT-3 to explain how code works by Simon Willison.

23. Character AI announces they’re building a full stack AGI company so you could create your own AI to help you with anything, using conversational AI research. The co-founders Noam Shazeer (co-invented Transformers, scaled them to supercomputers for the first time, and pioneered large-scale pretraining) and Daniel de Freitas (led the development of LaMDA), all of which are foundational to recent AI progress.

24. How Much Better is OpenAI’s Newest GPT-3 Model? - In addition to ChatGPT, OpenAI releases text-davinci-003, a Reinforcement Learning-tuned model that performs better long-form writing. Example, it can explain code in the style of Eminem. 😀

Educational

• minGPT by Andrej Karpathy - A PyTorch re-implementation of GPT, both training and inference. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code.

Tutorials

1. How to train a new language model from scratch using Transformers and Tokenizers tutorial by Hugging Face. :fire:

Videos

BERTology

1. XLNet Explained by NLP Breakfasts.

• Clear explanation. Also covers the two-stream self-attention idea.

2. The Future of NLP by 🤗

• Dense overview of what is going on in transfer learning in NLP currently, limits, and future directions.

3. The Transformer neural network architecture explained by AI Coffee Break with Letitia Parcalabescu.

• High-level explanation, best suited when unfamiliar with Transformers.

Attention and Transformer Networks

1. Sequence to Sequence Learning Animated (Inside Transformer Neural Networks and Attention Mechanisms) by learningcurve.

Official Implementations

1. google-research/bert - TensorFlow code and pre-trained models for BERT.

Other Implementations

PyTorch and TensorFlow

1. 🤗 Hugging Face Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. [Paper]

2. spacy-transformers - a library that wrap Hugging Face’s Transformers, in order to extract features to power NLP pipelines. It also calculates an alignment so the Transformer features can be related back to actual words instead of just wordpieces.

PyTorch

1. codertimo/BERT-pytorch - Google AI 2018 BERT pytorch implementation.

2. innodatalabs/tbert - PyTorch port of BERT ML model.

3. kimiyoung/transformer-xl - Code repository associated with the Transformer-XL paper.

4. dreamgonfly/BERT-pytorch - A PyTorch implementation of BERT in “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.

5. dhlee347/pytorchic-bert - A Pytorch implementation of Google BERT.

6. pingpong-ai/xlnet-pytorch - A Pytorch implementation of Google Brain XLNet.

7. facebook/fairseq - RoBERTa: A Robustly Optimized BERT Pretraining Approach by Facebook AI Research. SoTA results on GLUE, SQuAD and RACE.

8. NVIDIA/Megatron-LM - Ongoing research training transformer language models at scale, including: BERT.

9. deepset-ai/FARM - Simple & flexible transfer learning for the industry.

10. NervanaSystems/nlp-architect - NLP Architect by Intel AI. Among other libraries, it provides a quantized version of Transformer models and efficient training method.

11. kaushaltrivedi/fast-bert - Super easy library for BERT based NLP models. Built based on 🤗 Transformers and is inspired by fast.ai.

12. NVIDIA/NeMo - Neural Modules is a toolkit for conversational AI by NVIDIA. They are trying to improve speech recognition with BERT post-processing.

13. facebook/MMBT from Facebook AI - Multimodal transformers model that can accept a transformer model and a computer vision model for classifying image and text.

14. dbiir/UER-py from Tencent and RUC - Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo (with more focus on Chinese).

Keras

1. Separius/BERT-keras - Keras implementation of BERT with pre-trained weights.

2. CyberZHG/keras-bert - Implementation of BERT that could load official pre-trained models for feature extraction and prediction.

3. bojone/bert4keras - Light reimplement of BERT for Keras.

TensorFlow

1. guotong1988/BERT-tensorflow - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

2. kimiyoung/transformer-xl - Code repository associated with the Transformer-XL paper.

3. zihangdai/xlnet - Code repository associated with the XLNet paper.

Chainer

1. soskek/bert-chainer - Chainer implementation of “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.

Transfer Learning in NLP

As Jay Alammar put it:

The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural Language Processing or NLP for short). Our conceptual understanding of how best to represent words and sentences in a way that best captures underlying meanings and relationships is rapidly evolving. Moreover, the NLP community has been putting forward incredibly powerful components that you can freely download and use in your own models and pipelines (It’s been referred to as NLP’s ImageNet moment, referencing how years ago similar developments accelerated the development of machine learning in Computer Vision tasks).

One of the latest milestones in this development is the release of BERT, an event described as marking the beginning of a new era in NLP. BERT is a model that broke several records for how well models can handle language-based tasks. Soon after the release of the paper describing the model, the team also open-sourced the code of the model, and made available for download versions of the model that were already pre-trained on massive datasets. This is a momentous development since it enables anyone building a machine learning model involving language processing to use this powerhouse as a readily-available component – saving the time, energy, knowledge, and resources that would have gone to training a language-processing model from scratch.

BERT builds on top of a number of clever ideas that have been bubbling up in the NLP community recently – including but not limited to Semi-supervised Sequence Learning (by Andrew Dai and Quoc Le), ELMo (by Matthew Peters and researchers from AI2 and UW CSE), ULMFiT (by fast.ai founder Jeremy Howard and Sebastian Ruder), the OpenAI transformer (by OpenAI researchers Radford, Narasimhan, Salimans, and Sutskever), and the Transformer (Vaswani et al).

ULMFiT: Nailing down Transfer Learning in NLP

ULMFiT introduced methods to effectively utilize a lot of what the model learns during pre-training – more than just embeddings, and more than contextualized embeddings. ULMFiT introduced a language model and a process to effectively fine-tune that language model for various tasks.

NLP finally had a way to do transfer learning probably as well as Computer Vision could.

MultiFiT: Efficient Multi-lingual Language Model Fine-tuning by Sebastian Ruder et al. MultiFiT extends ULMFiT to make it more efficient and more suitable for language modelling beyond English. (EMNLP 2019 paper)

Books

1. Transfer Learning for Natural Language Processing - A book that is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models.

Other Resources

Tools

1. jessevig/bertviz - Tool for visualizing attention in the Transformer model.

2. FastBert - A simple deep learning library that allows developers and data scientists to train and deploy BERT based models for NLP tasks beginning with text classification. The work on FastBert is inspired by fast.ai.

3. gpt2tc - A small program using the GPT-2 LM to complete and compress texts. It has no external dependency, requires no GPU and is quite fast. The smallest model (117M parameters) is provided. Larger models can be downloaded as well. (no waitlist, no sign up required).

Tasks

Named-Entity Recognition (NER)

Classification

Text Generation

Question Answering (QA)

Knowledge Graph

License

it’s like a huge search engine which can talk.

github topic on paraphrase

小发猫

文章伪原创工具 and backup 有提到用扫描仪来扫描原创文章的方法

online paraphrase tool translation based, can process chinese

chatgpt says creativity matters, such as hackers, video producers and your evil sex shit.

you said LDA and LSA are helpful for paraphrasing

Yes, LDA and LSA are two methods that can be used to generate paraphrases of a given text. LDA (Latent Dirichlet Allocation) is a probabilistic model that can be used to identify topics in a text and then generate new sentences that express the same topics in a different way. LSA (Latent Semantic Analysis) is a method that uses singular value decomposition to identify the underlying meaning of words in a text and then generate paraphrases by replacing words with their closest semantic equivalents. Both LDA and LSA can be helpful for generating paraphrases, but they each have their own strengths and limitations.

can you demonstrate how to use LSA for paraphrasing in python

I’m sorry, but I am not currently able to browse the internet or access any external resources. As a large language model trained by OpenAI, my knowledge is based solely on the text that I have been trained on, and I don’t have the ability to run code or demonstrate specific examples. However, I can provide a general overview of how LSA could be used for paraphrasing in Python.

To use LSA for paraphrasing in Python, you would first need to create a matrix of word vectors for the words in the text you want to paraphrase. This matrix would represent the semantic relationships between the words in the text, and would be used to identify the underlying meaning of each word.

Next, you would use singular value decomposition (SVD) to decompose the matrix into three matrices, which represent the different factors that contribute to the meaning of the words in the text.

Once you have the three matrices, you can then use them to identify the most closely related words for each word in the text, and generate paraphrases by replacing words with their closest semantic equivalents.

This is just a brief overview of how LSA could be used for paraphrasing in Python. There are many different ways to implement LSA, and the exact details of the implementation would depend on the specific requirements of your project.

continue

I’m sorry, but I’m not sure what you would like me to continue with. If you have a specific question or topic you would like to discuss, I would be happy to help. Otherwise, I can provide more information about LSA and how it can be used for paraphrasing if that would be helpful. Let me know what you would like to know.

how to use LDA for paraphrasing

To use LDA for paraphrasing, you would first need to train a LDA model on a large corpus of text. This would involve using a machine learning algorithm to identify the topics that are present in the corpus, and then generating a set of “topic vectors” that represent the distribution of those topics across the text.

Once you have trained the LDA model, you can then use it to generate paraphrases of a given text by identifying the topics present in the text and then replacing words with synonyms or related words that express the same topics in a different way. This process can be repeated multiple times to generate multiple paraphrases of the same text.

Here is a brief example of how LDA could be used for paraphrasing in Python:

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from gensim import corpora, models
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text and create a dictionary of words
tokens = text.split(" ")
dictionary = corpora.Dictionary([tokens])
# Train a LDA model on the text
lda_model = models.LdaModel([dictionary.doc2bow(tokens)], num_topics=1)
# Generate paraphrases of the text
for i in range(3):
paraphrase = lda_model.show_topics(num_topics=1, num_words=len(tokens), formatted=False)
print(" ".join([word[0] for word in paraphrase[0][1]]))

This example trains a simple LDA model on a single sentence and then generates three paraphrases of the sentence by replacing words with their closest semantic equivalents. Of course, this is just a simple example to illustrate the basic concept of using LDA for paraphrasing. In practice, you would need to use a much larger corpus of text to train the LDA model and generate more accurate paraphrases.

demonstrate using LSA to paraphrase in python, using gensim if needed

As I mentioned earlier, I am not currently able to browse the internet or access external resources, so I cannot demonstrate using LSA to paraphrase in Python using gensim or any other specific libraries. However, I can provide a general overview of how LSA could be used for paraphrasing in Python.

To use LSA for paraphrasing in Python, you would first need to create a matrix of word vectors for the words in the text you want to paraphrase. This matrix would represent the semantic relationships between the words in the text, and would be used to identify the underlying meaning of each word.

Once you have the matrix of word vectors, you can use singular value decomposition (SVD) to decompose the matrix into three matrices, which represent the different factors that contribute to the meaning of the words in the text. The gensim library includes a implementation of SVD that you can use for this purpose.

Once you have the three matrices, you can then use them to identify the most closely related words for each word in the text, and generate paraphrases by replacing words with their closest semantic equivalents. Here is an example of how this could be done in Python:

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from gensim import corpora, models, matutils
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text and create a dictionary of words
tokens = text.split(" ")
dictionary = corpora.Dictionary([tokens])
# Create a matrix of word vectors using the dictionary
word_vectors = [dictionary.doc2bow(tokens)]
# Use SVD to decompose the matrix of word vectors
U, S, VT = matutils.svd(word_vectors, k=3)
# Generate paraphrases of the text
for i in range(3):
paraphrase = []
for word in tokens:
# Identify the closest semantic equivalents for each word
closest_words = VT.dot(VT[dictionary.token2id[word]])
closest_words = sorted(enumerate(closest_words), key=lambda x: x[1], reverse=True)
closest_word = closest_words[0][0]
# Replace the word with its closest semantic equivalent
paraphrase.append(dictionary[closest_word])
print(" ".join(paraphrase))

This example trains an LSA model on a single sentence and then generates three paraphrases of the sentence by replacing words with their closest semantic equivalents. Again, this is just a simple example to illustrate the basic concept of using LSA for paraphrasing. In practice, you would need to use a much larger corpus of text to train the LSA model and generate more accurate paraphrases.

how to use transformer for paraphrasing

The transformer is a type of neural network architecture that has been used for a wide range of natural language processing tasks, including paraphrasing. To use a transformer for paraphrasing, you would need to train the transformer on a large corpus of text using a machine learning algorithm.

During training, the transformer would learn to identify the underlying meaning of words and phrases in the text and then generate paraphrases by replacing words with synonyms or related words that express the same meaning in a different way.

Here is a brief example of how a transformer could be used for paraphrasing in Python:

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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the text you want to paraphrase
text = "The quick brown fox jumps over the lazy dog."
# Tokenize the text using a pre-trained transformer model
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
tokens = tokenizer.encode(text)
# Load a pre-trained transformer model
transformer = AutoModelForSeq2SeqLM.from_pretrained("distilbert-base-cased")
# Generate paraphrases of the text
for i in range(3):
paraphrase = transformer.generate(tokens)
paraphrase = tokenizer.decode(paraphrase[0])
print(paraphrase)

This example uses a pre-trained transformer model to generate three paraphrases of a single sentence. Of course, in practice you would need to train the transformer on a much larger corpus of text to generate more accurate paraphrases. The exact details of the implementation would depend on the specific requirements of your project.

how to generate catchy title from keywords using language model

To generate a catchy title from a set of keywords using a language model, you would need to train the language model on a large corpus of text that includes a variety of titles. During training, the language model would learn to identify the underlying meaning of words and phrases and the characteristics of catchy titles.

Once the language model has been trained, you can then use it to generate a catchy title from a set of keywords by providing the keywords as input to the model and then selecting the generated title that is the most catchy and relevant to the keywords.

Here is a brief example of how this could be done in Python:

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from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the keywords you want to use to generate the title
keywords = ["quick", "brown", "fox"]
# Tokenize the keywords using a pre-trained language model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokens = tokenizer.encode(keywords)
# Load a pre-trained language model
language_model = AutoModelForCausalLM.from_pretrained("gpt2")
# Generate candidate titles from the keywords
candidate_titles = []
for i in range(3):
title = language_model.generate(tokens, max_length=8)
title = tokenizer.decode(title[0])
candidate_titles.append(title)
# Select the most catchy and relevant title
selected_title = candidate_titles[0]
for title in candidate_titles:
if is_catchy(title) and is_relevant(title, keywords):
selected_title = title
break
print(selected_title)

This example generates three candidate titles from a set of keywords using a pre-trained language model, and then selects the most catchy and relevant title as the final title. Of course, this is just a simple example to illustrate the basic concept of using a language model to generate a catchy title from keywords. In practice, you would need to use a much larger corpus of text to train the language model and generate more accurate and diverse titles.

标题生成 封面生成

comparing different image caption models in which you have a bunch of models ready to use

template extraction, neural template generation

封面来源：

利用标题进行图片搜索 其实只能站内搜索 因为站外没有这种图片与文字的对应关系

截取视频截图

b站原图 histogram match 20% 去掉文字 镜像反转 加入随机噪声 旋转1度

利用封面进行图片反向搜索 效果其实不好 并没有想要的照片 只能找到原图 有可能起到去水印的效果 但是有限

reverse image search engine

reverse image search engine

meta image search engine

telegram reverse image search bot

neural template gen is a natural language generator based on templates from harvard nlp, can be used for title generation

根据标签生成广告 同样可以根据标签生成视频标题（推荐） 在千言数据集上训练过

https://huggingface.co/cocoshe/gpt2-chinese-gen-ads-by-keywords?text=My+name+is+Clara+and+I+am

title generator(from description):

https://github.com/harveyaot/DianJing/blob/master/scripts/title_generation_lm.py

https://blog.csdn.net/stay_foolish12/article/details/111661358

cover generation

rectangle packing allow overlapping

when solution is not found, decrease the size of rectangles.

youtube title generator using AI:

https://github.com/gdemos01/YoutubeVideoIdeasGeneratorAI

ai thumbnail generator using pyscenedetect:

https://github.com/yoonhero/ai-thumbnail-generator

image captioning:

https://github.com/ruotianluo/ImageCaptioning.pytorch

youzan clip product title generation:

https://huggingface.co/youzanai/clip-product-title-chinese

paper title generator without description:

https://github.com/csinva/gpt2-paper-title-generator

image captioning using cnn and rnn:

https://github.com/SCK22/image_and_video

image captioning can also be used for video captioning. but that will suffice the accuracy.

keras.io image captioning

https://keras.io/examples/vision/image_captioning/

generate image captions using CLIP and GPT(on medium, click continue reading)

https://towardsai.net/p/l/image-captioning-with-clip-and-gpt

gpt3demo.com has provided a lot of interesting tasks that gpt3 can do. including image captioning. may find video captioning, video classification.

gpt3demo.com provided image captioning libs:

https://gpt3demo.com/category/image-captioning

clipclap

gpt-3 x image captions

visualgpt: generate image captions

https://github.com/Vision-CAIR/VisualGPT

generate stories from pictures, using image transformers and gpt-2, just intro no code

https://www.dataversity.net/image-captioning-generating-stories-from-unstructured-data-using-applied-nlg/

Translators/Paraphraser for casual usage

baidu translator (api) provided by paddlehub

baidu language detector (api)

text style transfer:

https://blog.csdn.net/qq_27590277/article/details/106991084

python google translate api:

pip install googletrans

google translate in php:

https://github.com/Stichoza/google-translate-php

paraphrase via rephrasing and reordering

pegasus paraphrase:

increase the num_beams and temperature

https://analyticsindiamag.com/how-to-paraphrase-text-using-pegasus-transformer/

https://www.thepythoncode.com/article/paraphrase-text-using-transformers-in-python

example paraphrase project using LSTM as decoder and encoder:

https://github.com/vsuthichai/paraphraser

paraphrase with t5:

https://github.com/Vamsi995/Paraphrase-Generator

paraphrase dataset:

https://github.com/Wys997/Chinese-Paraphrase-from-Quora

文本纠错

https://github.com/James4Ever0/pycorrector

数据增强 变换句子形式

https://yongzhuo.blog.csdn.net/article/details/89166307

https://github.com/zhanlaoban/eda_nlp_for_Chinese

calculate perplexity:

https://github.com/DUTANGx/Chinese-BERT-as-language-model

https://github.com/James4Ever0/nlp-fluency

https://zhuanlan.zhihu.com/p/265677864

https://github.com/mattzheng/py-kenlm-model

multi-purpose tool for chinese: 偏旁部首 情感分析

https://github.com/SeanLee97/xmnlp

敏感词过滤 语言检测 训练语料库

https://github.com/fighting41love/funNLP

paraphraser.io

multilingual paraphrase database:

paraphrase.org

simbert

https://www.zhihu.com/question/317540171

BERT：原始版本bertRoberta：哈工大开源的中文wwm roberta模型BERT-SQ：本人在百度知道相似句数据集(Sim-Query)上微调后的bert模型Roberta-SQ：同上BERT-Whitening： @苏剑林 最新博客中提出的白化模型。Roberta-Whitening：同上

https://yongzhuo.blog.csdn.net/article/details/89166307

language fluency test:

https://github.com/baojunshan/nlp-fluency

many paraphraser models for english are on huggingface, but few for chinese.

https://huggingface.co/lijingxin/mt5-for-zh-paraphrase

https://pypi.org/project/genienlp/

https://github.com/salesforce/decaNLP

parrot paraphraser with nlu engines for english:

https://github.com/PrithivirajDamodaran/Parrot_Paraphraser

sentence level paraphraser:

https://github.com/vsuthichai/paraphraser

document level paraphraser, with sentence rewriting and reordering(shuffle):

https://github.com/L-Zhe/CoRPG

https://pypi.org/project/lexsub/

https://github.com/hit-joseph/lexical-paraphrase-extraction

synonyms (python library)

you can also train a contextual search tool using fine-tuned repurposed paraphrase model.

https://pypi.org/project/nlp-text-search/

文言文

https://github.com/raynardj/yuan

粤语

https://huggingface.co/x-tech

huggingface有英语翻译到其他语言的模型 没有翻译成中文的模型

在线

https://github.com/nidhaloff/deep-translator

https://github.com/UlionTse/translators

translatepy

离线

https://huggingface.co/tasks/translation

https://huggingface.co/Helsinki-NLP/opus-mt-zh-en

https://github.com/argosopentech/argos-translate

libretranslate

https://github.com/Teuze/translate

https://github.com/xhlulu/dl-translate/

facebook/mbart-large-50-many-to-many-mmt

mbart50

m2m100

view under https://huggingface.co/tasks to see great models fitting exact needs.

Use the original transcript for paraphrasing, while using danmaku for joke generation.