Git Product home page Git Product logo

ilm's People

Contributors

chrisdonahue avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

ilm's Issues

Infill a sentence as a continuation of a conditioning token?

I'd like to be able to do a version of sentence infilling that allows for conditioning the generation on a leading token—i.e., semantically prompting the generated infill sentence. In your estimation, would it be a big job to enable this kind of generation? I'm thinking of the way that initial words like "however", "therefore", "further", and so on can have a strong semantic effect on the kind of sentence infill generated.

The issue during training.

I have set up the dataset according to your instructions. However, when I tried to train using your code, I encountered the following error. I have spent some time debugging it without a solution. Can you please advise on how to resolve this?

image

Early stopping

Where did you implement the early stopping based on PPL on the validation set in the training script? Thanks

How to get top k spans for a mask

Hi,

Thanks for releasing this! I've only just started to play with it but managed to get the example from the Jupyter Notebook working without any problems.

My questions:

  1. Is it possible to generate a phrase of a variable number of tokens (e.g. <= 2) in a mask?
    E.g. I like to _ on Tuesdays.
    sing, eat cake, eat bread, dance, ...
    I tried using the <|startofinfill|> and <|endofinfill|> tokens but the output didn't make sense:
I am looking forward to<|startofinfill|><|endofinfill|> in this year summer camps. 
--------------------------------------------------------------------------------
I am looking forward to We have my entire gym membership. My mom's husband has taken my sister and I to their house. I can not wait to go. in this year summer camps.
  1. Can we also get a top k list of candidates and probabilities for each mask?

Thanks!

How to Run Juypter Notebook?

It continually says I am missing certain things. I am new, and would really like to try it. Could I please have some guidance?

How to use custom tokenizer?

I have a custom tokenizer that's just a BertTokenizer with a custom vocab, which was used when pretraining my GPT-2 model. I'm trying to specify it to the train_ilm.py script, but I hit a NotImplemented error that I'm not sure how to solve. Any thoughts?

Infill for fine tuned model

I have a fine-tuned GPT-2 model, trained on a specific text domain, on english language. The model input has been tokenized with SentencePiece. How to adapt that model to ILM if possible?

Thank you.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.