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metadata
language: en
thumbnail: >-
  http://www.huggingtweets.com/jeremyphoward-lmoroney-ylecun/1684662288397/predictions.png
tags:
  - huggingtweets
widget:
  - text: My dream is
๐Ÿค– AI CYBORG ๐Ÿค–
Jeremy Howard & Yann LeCun & Laurence Moroney ๐Ÿ‡ฎ๐Ÿ‡ช ๐Ÿด๓ ง๓ ข๓ ท๓ ฌ๓ ณ๓ ฟ
@jeremyphoward-lmoroney-ylecun

I was made with huggingtweets.

Create your own bot based on your favorite user with the demo!

How does it work?

The model uses the following pipeline.

pipeline

To understand how the model was developed, check the W&B report.

Training data

The model was trained on tweets from Jeremy Howard & Yann LeCun & Laurence Moroney ๐Ÿ‡ฎ๐Ÿ‡ช ๐Ÿด๓ ง๓ ข๓ ท๓ ฌ๓ ณ๓ ฟ.

Data Jeremy Howard Yann LeCun Laurence Moroney ๐Ÿ‡ฎ๐Ÿ‡ช ๐Ÿด๓ ง๓ ข๓ ท๓ ฌ๓ ณ๓ ฟ
Tweets downloaded 3221 3246 3233
Retweets 1468 495 342
Short tweets 143 215 252
Tweets kept 1610 2536 2639

Explore the data, which is tracked with W&B artifacts at every step of the pipeline.

Training procedure

The model is based on a pre-trained GPT-2 which is fine-tuned on @jeremyphoward-lmoroney-ylecun's tweets.

Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.

At the end of training, the final model is logged and versioned.

How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/jeremyphoward-lmoroney-ylecun')
generator("My dream is", num_return_sequences=5)

Limitations and bias

The model suffers from the same limitations and bias as GPT-2.

In addition, the data present in the user's tweets further affects the text generated by the model.

About

Built by Boris Dayma

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For more details, visit the project repository.

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