| Model                                                | Downloads
| ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------
| `dbmdz/bert-base-italian-cased`                      | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/config.json)                                               • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/pytorch_model.bin)                      • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/vocab.txt)
| `dbmdz/bert-base-italian-uncased`                    | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/config.json)                                             • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/pytorch_model.bin)                    • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/vocab.txt)
| `dbmdz/bert-base-italian-xxl-cased`                  | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/config.json)                                           • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/pytorch_model.bin)                  • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/vocab.txt)
| `dbmdz/bert-base-italian-xxl-uncased`                | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/config.json)                                         • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/pytorch_model.bin)                • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/vocab.txt)
| `dbmdz/electra-base-italian-xxl-cased-discriminator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/vocab.txt)
| `dbmdz/electra-base-italian-xxl-cased-generator`     | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-generator/config.json)     • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/pytorch_model.bin)     • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/vocab.txt)


## Results


For results on downstream tasks like NER or PoS tagging, please refer to
[this repository](https://github.com/stefan-it/italian-bertelectra).


## Usage


With Transformers >= 2.3 our Italian BERT models can be loaded like:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/bert-base-italian-cased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)
```


To load the (recommended) Italian XXL BERT models, just use:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/bert-base-italian-xxl-cased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)
```


To load the Italian XXL ELECTRA model (discriminator), just use:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/electra-base-italian-xxl-cased-discriminator"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelWithLMHead.from_pretrained(model_name)
```


# Huggingface model hub


All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).


# Contact (Bugs, Feedback, Contribution and more)


For questions about our BERT/ELECTRA models just open an issue
[here](https://github.com/dbmdz/berts/issues/new) 🤗


# Acknowledgments


Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤


Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗


model name: bert-base-italian-uncased


model author: dbmdz


task: fill-mask


tags: pytorch, tf, jax, bert, fill-mask, it, dataset:wikipedia, transformers, license:mit, autonlp_compatible, infinity_compatible


language: tr
license: mit
datasets:


- allenai/c4


# 🇹🇷 Turkish ConvBERT model


We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉


Some datasets used for pretraining and evaluation are contributed from the
awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk.


Logo is provided by [Merve Noyan](https://twitter.com/mervenoyann).


# Stats


We've trained an (cased) ConvBERT model on the recently released Turkish part of the
[multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team.


After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting
in 31,240,963,926 tokens.


We used the original 32k vocab (instead of creating a new one).


# mC4 ConvBERT


In addition to the ELEC**TR**A base model, we also trained an ConvBERT model on the Turkish part of the mC4 corpus. We use a
sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU.


# Model usage


All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz)
using their model name.


Example usage with 🤗/Transformers:


```python
tokenizer = AutoTokenizer.from_pretrained("dbmdz/convbert-base-turkish-mc4-cased")

model = AutoModel.from_pretrained("dbmdz/convbert-base-turkish-mc4-cased")
```


# Citation


You can use the following BibTeX entry for citation:


```bibtex
@software{stefan_schweter_2020_3770924,
  author       = {Stefan Schweter},
  title        = {BERTurk - BERT models for Turkish},
  month        = apr,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.3770924},
  url          = {https://doi.org/10.5281/zenodo.3770924}
}
```


# Acknowledgments


Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
us the Turkish NER dataset for evaluation.


We would like to thank [Merve Noyan](https://twitter.com/mervenoyann) for the
awesome logo!


model name: convbert-base-turkish-mc4-cased


model author: dbmdz


task: fill-mask


tags: pytorch, tf, convbert, fill-mask, tr, dataset:allenai/c4, transformers, license:mit, autonlp_compatible


language: it
license: mit
datasets:


- wikipedia


# 🤗 + 📚 dbmdz BERT and ELECTRA models


In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉


# Italian BERT


The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final
training corpus has a size of 13GB and 2,050,057,573 tokens.


For sentence splitting, we use NLTK (faster compared to spacy).
Our cased and uncased models are training with an initial sequence length of 512
subwords for ~2-3M steps.


For the XXL Italian models, we use the same training data from OPUS and extend
it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/).
Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens.


Note: Unfortunately, a wrong vocab size was used when training the XXL models.
This explains the mismatch of the "real" vocab size of 31102, compared to the
vocab size specified in `config.json`. However, the model is working and all
evaluations were done under those circumstances.
See [this issue](https://github.com/dbmdz/berts/issues/7) for more information.


The Italian ELECTRA model was trained on the "XXL" corpus for 1M steps in total using a batch
size of 128. We pretty much following the ELECTRA training procedure as used for
[BERTurk](https://github.com/stefan-it/turkish-bert/tree/master/electra).


## Model weights


Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
compatible weights are available. If you need access to TensorFlow checkpoints,
please raise an issue!


| Model                                                | Downloads
| ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------
| `dbmdz/bert-base-italian-cased`                      | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/config.json)                                               • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/pytorch_model.bin)                      • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/vocab.txt)
| `dbmdz/bert-base-italian-uncased`                    | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/config.json)                                             • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/pytorch_model.bin)                    • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/vocab.txt)
| `dbmdz/bert-base-italian-xxl-cased`                  | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/config.json)                                           • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/pytorch_model.bin)                  • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/vocab.txt)
| `dbmdz/bert-base-italian-xxl-uncased`                | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/config.json)                                         • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/pytorch_model.bin)                • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/vocab.txt)
| `dbmdz/electra-base-italian-xxl-cased-discriminator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/vocab.txt)
| `dbmdz/electra-base-italian-xxl-cased-generator`     | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-generator/config.json)     • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/pytorch_model.bin)     • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/vocab.txt)


## Results


For results on downstream tasks like NER or PoS tagging, please refer to
[this repository](https://github.com/stefan-it/italian-bertelectra).


## Usage


With Transformers >= 2.3 our Italian BERT models can be loaded like:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/bert-base-italian-cased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)
```


To load the (recommended) Italian XXL BERT models, just use:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/bert-base-italian-xxl-cased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)
```


To load the Italian XXL ELECTRA model (discriminator), just use:


```python
from transformers import AutoModel, AutoTokenizer

model_name = "dbmdz/electra-base-italian-xxl-cased-discriminator"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelWithLMHead.from_pretrained(model_name)
```


# Huggingface model hub


All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).


# Contact (Bugs, Feedback, Contribution and more)


For questions about our BERT/ELECTRA models just open an issue
[here](https://github.com/dbmdz/berts/issues/new) 🤗


# Acknowledgments


Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤


Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗


model name: bert-base-italian-xxl-cased


model author: dbmdz


task: fill-mask


tags: pytorch, tf, jax, bert, fill-mask, it, dataset:wikipedia, transformers, license:mit, autonlp_compatible, infinity_compatible


# MSRoBERTa


Fine-tuned RoBERTa MLM model for [`Miscrosoft Sentence Completion Challenge`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR_SCCD.pdf). This model case-sensitive following the `Roberta-base` model.


# Model description (taken from: [here](https://huggingface.co/roberta-base))


RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.


More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.


This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.


### How to use


You can use this model directly with a pipeline for masked language modeling:


```python
from transformers import pipeline,AutoModelForMaskedLM,AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nkoh01/MSRoberta")
model = AutoModelForMaskedLM.from_pretrained("nkoh01/MSRoberta")

unmasker = pipeline(
    "fill-mask",
    model=model,
    tokenizer=tokenizer
)
unmasker("Hello, it is a <mask> to meet you.")

[{'score': 0.9508683085441589,
  'sequence': 'hello, it is a pleasure to meet you.',
  'token': 10483,
  'token_str': ' pleasure'},
 {'score': 0.015089659951627254,
  'sequence': 'hello, it is a privilege to meet you.',
  'token': 9951,
  'token_str': ' privilege'},
 {'score': 0.013942377641797066,
  'sequence': 'hello, it is a joy to meet you.',
  'token': 5823,
  'token_str': ' joy'},
 {'score': 0.006964420434087515,
  'sequence': 'hello, it is a delight to meet you.',
  'token': 13213,
  'token_str': ' delight'},
 {'score': 0.0024567877408117056,
  'sequence': 'hello, it is a honour to meet you.',
  'token': 6671,
  'token_str': ' honour'}]
```


## Installations


Make sure you run `!pip install transformers` command to install the transformers library before running the commands above.


## Bias and limitations


Under construction.


model name: MSRoberta


model author: nkoh01


task: fill-mask


tags: pytorch, jax, roberta, fill-mask, transformers, autonlp_compatible, infinity_compatible


tags:


- conversational


# George Costanza Model


model name: georgebot


model author: yusufmorsi


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


language: en
datasets:


- huggingartists/joni-mitchell
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Joni Mitchell

@joni-mitchell


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 Joni Mitchell.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/joni-mitchell).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/joni-mitchell")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1m5n59kk/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 Joni Mitchell's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34saoh5x) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34saoh5x/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/joni-mitchell')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/joni-mitchell")

model = AutoModelWithLMHead.from_pretrained("huggingartists/joni-mitchell")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: joni-mitchell


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/joni-mitchell, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/tom-waits
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Tom Waits

@tom-waits


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 Tom Waits.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/tom-waits).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/tom-waits")
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/tom-waits")

model = AutoModelWithLMHead.from_pretrained("huggingartists/tom-waits")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/216zw2jw/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 Tom Waits's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/16iei9vt) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/16iei9vt/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/tom-waits')
generator("I am", num_return_sequences=5)
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: tom-waits


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/tom-waits, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-the-pigs
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The ‘’Вепри’’ (The Pigs)

@the-the-pigs


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 The ‘’Вепри’’ (The Pigs).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-the-pigs).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-the-pigs")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/7yh65db9/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 The ‘’Вепри’’ (The Pigs)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/65gj1lk1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/65gj1lk1/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/the-the-pigs')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-the-pigs")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-the-pigs")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-the-pigs


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-the-pigs, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/sugar-ray
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Sugar Ray

@sugar-ray


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 Sugar Ray.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/sugar-ray).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/sugar-ray")
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/sugar-ray")

model = AutoModelWithLMHead.from_pretrained("huggingartists/sugar-ray")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/10440qj4/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 Sugar Ray's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2n3xk5nv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2n3xk5nv/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/sugar-ray')
generator("I am", num_return_sequences=5)
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: sugar-ray


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/sugar-ray, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/arctic-monkeys
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Arctic Monkeys

@arctic-monkeys


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 Arctic Monkeys.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/arctic-monkeys).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/arctic-monkeys")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1x4ii6qz/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 Arctic Monkeys's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/bmnqvn53) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/bmnqvn53/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/arctic-monkeys')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/arctic-monkeys")

model = AutoModelWithLMHead.from_pretrained("huggingartists/arctic-monkeys")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: arctic-monkeys


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/arctic-monkeys, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/aimer
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Aimer

@aimer


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 Aimer.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/aimer).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/aimer")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1rtjxc8q/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 Aimer's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2rguugmg) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2rguugmg/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/aimer')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/aimer")

model = AutoModelWithLMHead.from_pretrained("huggingartists/aimer")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: aimer


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/aimer, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/skillet
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Skillet

@skillet


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 Skillet.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/skillet).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/skillet")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1wmbkzn8/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 Skillet's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3jke6b6i) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3jke6b6i/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/skillet')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/skillet")

model = AutoModelWithLMHead.from_pretrained("huggingartists/skillet")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: skillet


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/skillet, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/snoop-dogg
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Snoop Dogg

@snoop-dogg


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 Snoop Dogg.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/snoop-dogg).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/snoop-dogg")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/255rv52h/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 Snoop Dogg's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/xlo6sfmn) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/xlo6sfmn/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/snoop-dogg')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/snoop-dogg")

model = AutoModelWithLMHead.from_pretrained("huggingartists/snoop-dogg")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: snoop-dogg


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/snoop-dogg, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/lyapis-trubetskoy
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Ляпис Трубецкой (Lyapis Trubetskoy)

@lyapis-trubetskoy


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 Ляпис Трубецкой (Lyapis Trubetskoy).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/lyapis-trubetskoy).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lyapis-trubetskoy")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ycs0usm/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 Ляпис Трубецкой (Lyapis Trubetskoy)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/uz1xtq0k) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/uz1xtq0k/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/lyapis-trubetskoy')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lyapis-trubetskoy")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lyapis-trubetskoy")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: lyapis-trubetskoy


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/lyapis-trubetskoy, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/scriptonite
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Скриптонит (Scriptonite)

@scriptonite


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 Скриптонит (Scriptonite).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/scriptonite).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/scriptonite")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/13pxeww0/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 Скриптонит (Scriptonite)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1itfp830) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1itfp830/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/scriptonite')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/scriptonite")

model = AutoModelWithLMHead.from_pretrained("huggingartists/scriptonite")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: scriptonite


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/scriptonite, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/architects
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Architects

@architects


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 Architects.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/architects).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/architects")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/licizuue/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 Architects's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1a9mrzf8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1a9mrzf8/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/architects')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/architects")

model = AutoModelWithLMHead.from_pretrained("huggingartists/architects")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: architects


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/architects, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/van-morrison
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Van Morrison

@van-morrison


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 Van Morrison.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/van-morrison).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/van-morrison")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2qbna51w/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 Van Morrison's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3c0ah11a) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3c0ah11a/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/van-morrison')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/van-morrison")

model = AutoModelWithLMHead.from_pretrained("huggingartists/van-morrison")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: van-morrison


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/van-morrison, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/upsahl
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

UPSAHL

@upsahl


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 UPSAHL.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/upsahl).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/upsahl")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2o3af3ts/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 UPSAHL's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2lr9eqkt) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2lr9eqkt/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/upsahl')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/upsahl")

model = AutoModelWithLMHead.from_pretrained("huggingartists/upsahl")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: upsahl


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/upsahl, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/sergei-letov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Сергей Летов (Sergei Letov)

@sergei-letov


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 Сергей Летов (Sergei Letov).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/sergei-letov).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/sergei-letov")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1chw67j7/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 Сергей Летов (Sergei Letov)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/my7m2jp6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/my7m2jp6/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/sergei-letov')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/sergei-letov")

model = AutoModelWithLMHead.from_pretrained("huggingartists/sergei-letov")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: sergei-letov


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/sergei-letov, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/morgenshtern
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

MORGENSHTERN

@morgenshtern


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 MORGENSHTERN.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/morgenshtern).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/morgenshtern")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/29htvpbu/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 MORGENSHTERN's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/m6tldjdu) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/m6tldjdu/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/morgenshtern')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/morgenshtern")

model = AutoModelWithLMHead.from_pretrained("huggingartists/morgenshtern")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: morgenshtern


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/morgenshtern, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/veggietales
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

VeggieTales

@veggietales


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 VeggieTales.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/veggietales).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/veggietales")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1r6205vr/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 VeggieTales's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/111uuafu) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/111uuafu/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/veggietales')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/veggietales")

model = AutoModelWithLMHead.from_pretrained("huggingartists/veggietales")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: veggietales


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/veggietales, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/grigory-leps
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Григорий Лепс (Grigory Leps)

@grigory-leps


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 Григорий Лепс (Grigory Leps).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/grigory-leps).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/grigory-leps")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/32wqexib/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 Григорий Лепс (Grigory Leps)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1j0f6nwb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1j0f6nwb/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/grigory-leps')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/grigory-leps")

model = AutoModelWithLMHead.from_pretrained("huggingartists/grigory-leps")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: grigory-leps


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/grigory-leps, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/macan
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

MACAN

@macan


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 MACAN.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/macan).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/macan")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1v1lws1p/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 MACAN's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/uechokog) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/uechokog/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/macan')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/macan")

model = AutoModelWithLMHead.from_pretrained("huggingartists/macan")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: macan


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/macan, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/abba
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

ABBA

@abba


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 ABBA.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/abba).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/abba")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3pc6wfre/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 ABBA's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3b7wqd1w) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3b7wqd1w/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/abba')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/abba")

model = AutoModelWithLMHead.from_pretrained("huggingartists/abba")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: abba


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/abba, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/xxxtentacion
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

XXXTENTACION

@xxxtentacion


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 XXXTENTACION.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/xxxtentacion).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/xxxtentacion")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/12xi0jh5/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 XXXTENTACION's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2l2qvy4j) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2l2qvy4j/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/xxxtentacion')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/xxxtentacion")

model = AutoModelWithLMHead.from_pretrained("huggingartists/xxxtentacion")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: xxxtentacion


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/xxxtentacion, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/v-x-v-prince
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

V $ X V PRiNCE

@v-x-v-prince


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 V $ X V PRiNCE.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/v-x-v-prince).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/v-x-v-prince")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/a6qdzbfe/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 V $ X V PRiNCE's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rv03n56) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rv03n56/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/v-x-v-prince')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/v-x-v-prince")

model = AutoModelWithLMHead.from_pretrained("huggingartists/v-x-v-prince")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: v-x-v-prince


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/v-x-v-prince, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/ghost
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Ghost

@ghost


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 Ghost.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/ghost).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ghost")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1n8515nl/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 Ghost's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2qimq3aa) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2qimq3aa/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/ghost')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ghost")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ghost")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: ghost


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/ghost, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/gspd
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

GSPD

@gspd


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 GSPD.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/gspd).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/gspd")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/17su8b0l/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 GSPD's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/cnw6rr26) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/cnw6rr26/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/gspd')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/gspd")

model = AutoModelWithLMHead.from_pretrained("huggingartists/gspd")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: gspd


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/gspd, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/phish
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Phish

@phish


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 Phish.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/phish).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/phish")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/22sghxz4/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 Phish's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/340yi6e5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/340yi6e5/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/phish')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/phish")

model = AutoModelWithLMHead.from_pretrained("huggingartists/phish")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: phish


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/phish, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/aikko
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

aikko

@aikko


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 aikko.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/aikko).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/aikko")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1cfdpsrg/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 aikko's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/oesyn53g) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/oesyn53g/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/aikko')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/aikko")

model = AutoModelWithLMHead.from_pretrained("huggingartists/aikko")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: aikko


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/aikko, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/cocomelon
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Cocomelon

@cocomelon


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 Cocomelon.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/cocomelon).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/cocomelon")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1avk18yc/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 Cocomelon's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3s0b2uix) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3s0b2uix/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/cocomelon')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/cocomelon")

model = AutoModelWithLMHead.from_pretrained("huggingartists/cocomelon")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: cocomelon


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/cocomelon, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/ariya
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Ария (Ariya)

@ariya


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 Ария (Ariya).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/ariya).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ariya")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/uo73s5z1/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 Ария (Ariya)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/69c1r7ea) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/69c1r7ea/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/ariya')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ariya")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ariya")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: ariya


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/ariya, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/arash
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Arash

@arash


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 Arash.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/arash).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/arash")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/27u6df87/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 Arash's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3eav8xpf) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3eav8xpf/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/arash')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/arash")

model = AutoModelWithLMHead.from_pretrained("huggingartists/arash")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: arash


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/arash, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/denderty
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

DenDerty

@denderty


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 DenDerty.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/denderty).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/denderty")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/gu1nyrga/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 DenDerty's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2hx5b1gk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2hx5b1gk/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/denderty')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/denderty")

model = AutoModelWithLMHead.from_pretrained("huggingartists/denderty")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: denderty


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/denderty, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/oxxxymiron
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Oxxxymiron

@oxxxymiron


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 Oxxxymiron.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/oxxxymiron).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/oxxxymiron")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/if83r8sb/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 Oxxxymiron's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/s58wdkfz) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/s58wdkfz/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/oxxxymiron')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/oxxxymiron")

model = AutoModelWithLMHead.from_pretrained("huggingartists/oxxxymiron")
```


## 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*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: oxxxymiron


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/oxxxymiron, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/drake
tags:
- huggingartists
- lyrics
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Drake

@drake


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