readme: ---
  language: en
  datasets:
  - huggingartists/big-russian-boss
  tags:
  - huggingartists
  - lyrics
  - lm-head
  - causal-lm
  widget:
  - text: "I am"
  ---
  
  <div class="inline-flex flex-col" style="line-height: 1.5;">
      <div class="flex">
          <div
  			style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/d66eeeef006738708df1e52b84c34c14.403x403x1.jpg&#39;)">
          </div>
      </div>
      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
      <div style="text-align: center; font-size: 16px; font-weight: 800">Big Russian Boss</div>
      <a href="https://genius.com/artists/big-russian-boss">
      	<div style="text-align: center; font-size: 14px;">@big-russian-boss</div>
      </a>
  </div>
  
  I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
  
  Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
  
  ## How does it work?
  
  To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
  
  ## Training data
  
  The model was trained on lyrics from Big Russian Boss.
  
  Dataset is available [here](https://huggingface.co/datasets/huggingartists/big-russian-boss).
  And can be used with:
  
  ```python
  from datasets import load_dataset
  
  dataset = load_dataset("huggingartists/big-russian-boss")
  ```
  
  [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ju9bqqi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
  
  ## Training procedure
  
  The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Big Russian Boss's lyrics.
  
  Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx) for full transparency and reproducibility.
  
  At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx/artifacts) is logged and versioned.
  
  ## How to use
  
  You can use this model directly with a pipeline for text generation:
  
  ```python
  from transformers import pipeline
  generator = pipeline('text-generation',
                       model='huggingartists/big-russian-boss')
  generator("I am", num_return_sequences=5)
  ```
  
  Or with Transformers library:
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead
    
  tokenizer = AutoTokenizer.from_pretrained("huggingartists/big-russian-boss")
  
  model = AutoModelWithLMHead.from_pretrained("huggingartists/big-russian-boss")
  ```
  
  ## Limitations and bias
  
  The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
  
  In addition, the data present in the user's tweets further affects the text generated by the model.
  
  ## About
  
  *Built by Aleksey Korshuk*
  
  [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
  
  [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
  
  [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
  
  For more details, visit the project repository.
  
  [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
  