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--- |
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license: apache-2.0 |
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datasets: |
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- ML-Projects-Kiel/tweetyface |
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language: |
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- en |
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tags: |
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- gpt2 |
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inference: |
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parameters: |
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num_return_sequences: 2 |
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widget: |
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- text: "User: BarackObama\nTweet: Twitter is " |
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example_title: "Barack Obama about Twitter" |
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- text: "User: neiltyson\nTweet: Twitter is" |
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example_title: "Neil deGrasse Tyson about Twitter" |
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- text: "User: elonmusk\nTweet: Twitter is" |
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example_title: "Elon Musk about Twitter" |
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- text: "User: elonmusk\nTweet: My Opinion about space" |
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example_title: "Elon Musk deGrasse Tyson about Space" |
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- text: "User: BarackObama\nTweet: My Opinion about space" |
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example_title: "Barack Obama about Space" |
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- text: "User: neiltyson\nTweet: My Opinion about space" |
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example_title: "Neil deGrasse Tyson about Space" |
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--- |
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# Tweety Face |
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Finetuned language model based on [GPT-2](https://huggingface.co/gpt2) to generate Tweets in a users style. |
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## Model description |
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Tweety Face is a transformer model finetuned using GTP-2 and Tweets from various Twitter users. It was created to |
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generate a Twitter Tweet for a given user similar to their specific writing style. It accepts a prompt for a user |
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and completes the text. |
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This finetuned model uses the **smallest** version of GPT-2, with 124M parameters. |
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## Intended uses & limitations |
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This model was created to experiment with prompt inputs and is not intended to create real Tweets. The generated text |
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is not a real representation of the given users opinion, political affiliation, behaviour, etc. Do not use this model |
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to impersonate a user. |
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### How to use |
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You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we |
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set a seed for reproducibility: |
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```python |
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>>> from transformers import pipeline, set_seed |
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>>> generator = pipeline('text-generation', model='ML-Projects-Kiel/tweetyface') |
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>>> set_seed(42) |
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>>> generator("User: elonmusk\nTweet: Twitter is", max_length=30, num_return_sequences=5) |
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[{'generated_text': 'User: elonmusk\nTweet: Twitter is more active than ever. Even though you can’t see your entire phone list, your'}, |
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{'generated_text': 'User: elonmusk\nTweet: Twitter is just in a few hours until an announcement which has been approved by President. This should be a'}, |
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{'generated_text': 'User: elonmusk\nTweet: Twitter is currently down to a minimum of 13 tweets per day, a decline that was significantly worse than Twitter'}, |
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{'generated_text': 'User: elonmusk\nTweet: Twitter is a great investment to us. Will go above his legal fees to join Twitter in many countries,'}, |
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{'generated_text': 'User: elonmusk\nTweet: Twitter is not doing something like this – they are not using Twitter to give out their content – other than'}] |
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``` |
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## Training data |
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The training data used for this model has been released as a dataset one can browse [here](https://huggingface.co/ML-Projects-Kiel/tweetyface). |
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The raw data can be found in our [Github Repository](https://github.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers). The raw data |
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can be found in two versions. All data on the develop branch is used in a [debugging dataset](https://huggingface.co/datasets/ML-Projects-Kiel/tweetyface_debug). |
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All data in the qa branch is used in the final dataset. |
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## Training procedure |
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### Preprocessing |
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For training first all retweets (RT) have been removed. Next the newline characters "\n" have been replaced by white |
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spaces and all URLs haven been replaced with the word URL. |
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The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters). |
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