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metadata
language: en
thumbnail: >-
  http://www.huggingtweets.com/apesahoy-daftlimmy-women4wes/1662819834805/predictions.png
tags:
  - huggingtweets
widget:
  - text: My dream is
🤖 AI CYBORG 🤖
twitch.tv/Limmy & Humongous Ape MP & Women for Wes
@apesahoy-daftlimmy-women4wes

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 twitch.tv/Limmy & Humongous Ape MP & Women for Wes.

Data twitch.tv/Limmy Humongous Ape MP Women for Wes
Tweets downloaded 3246 3247 1807
Retweets 411 191 53
Short tweets 715 607 275
Tweets kept 2120 2449 1479

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 @apesahoy-daftlimmy-women4wes'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/apesahoy-daftlimmy-women4wes')
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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