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								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '"json encoded string"' \
https://api-inference.huggingface.co/models/huggingtweets/julien_c
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huggingtweets/julien_c huggingtweets/julien_c
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pytorch

tf

Contributed by

HuggingTweets
2 team members · 4 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingtweets/julien_c") model = AutoModelWithLMHead.from_pretrained("huggingtweets/julien_c")
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Julien Chaumond 🤖 AI Bot
@julien_c bot

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 @julien_c's tweets.

Data Quantity
Tweets downloaded 3219
Retweets 874
Short tweets 386
Tweets kept 1959

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 @julien_c's tweets.

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

Intended uses & limitations

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/julien_c')
generator("My dream is", max_length=50, 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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