---
language: english
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
---

# Historic Language Models (HLMs)

## Languages

Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:

| Language | Training data | Size
| -------- | ------------- | ----
| German   | [Europeana](http://www.europeana-newspapers.eu/)       | 13-28GB (filtered)
| French   | [Europeana](http://www.europeana-newspapers.eu/)       | 11-31GB (filtered)
| English  | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered)
| Finnish  | [Europeana](http://www.europeana-newspapers.eu/)       | 1.2GB
| Swedish  | [Europeana](http://www.europeana-newspapers.eu/)       | 1.1GB

## Models

At the moment, the following models are available on the model hub:

| Model identifier                              | Model Hub link
| --------------------------------------------- | --------------------------------------------------------------------------
| `dbmdz/bert-base-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased)
| `dbmdz/bert-base-historic-english-cased`      | [here](https://huggingface.co/dbmdz/bert-base-historic-english-cased)
| `dbmdz/bert-base-finnish-europeana-cased`     | [here](https://huggingface.co/dbmdz/bert-base-finnish-europeana-cased)
| `dbmdz/bert-base-swedish-europeana-cased`     | [here](https://huggingface.co/dbmdz/bert-base-swedish-europeana-cased)

# Corpora Stats

## German Europeana Corpus

We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size
and use less-noisier data:

| OCR confidence | Size
| -------------- | ----
| **0.60**       | 28GB
| 0.65           | 18GB
| 0.70           | 13GB

For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:

![German Europeana Corpus Stats](stats/figures/german_europeana_corpus_stats.png)

## French Europeana Corpus

Like German, we use different ocr confidence thresholds:

| OCR confidence | Size
| -------------- | ----
| 0.60           | 31GB
| 0.65           | 27GB
| **0.70**       | 27GB
| 0.75           | 23GB
| 0.80           | 11GB

For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:

![French Europeana Corpus Stats](stats/figures/french_europeana_corpus_stats.png)

## British Library Corpus

Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). Stats incl. year filtering:

| Years             | Size
| ----------------- | ----
| ALL               | 24GB
| >= 1800 && < 1900 | 24GB

We use the year filtered variant. The following plot shows a tokens per year distribution:

![British Library Corpus Stats](stats/figures/bl_corpus_stats.png)

## Finnish Europeana Corpus

| OCR confidence | Size
| -------------- | ----
| 0.60           | 1.2GB

The following plot shows a tokens per year distribution:

![Finnish Europeana Corpus Stats](stats/figures/finnish_europeana_corpus_stats.png)

## Swedish Europeana Corpus

| OCR confidence | Size
| -------------- | ----
| 0.60           | 1.1GB

The following plot shows a tokens per year distribution:

![Swedish Europeana Corpus Stats](stats/figures/swedish_europeana_corpus_stats.png)

## All Corpora

The following plot shows a tokens per year distribution of the complete training corpus:

![All Corpora Stats](stats/figures/all_corpus_stats.png)

# Multilingual Vocab generation

For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB.
The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:

| Language | Size
| -------- | ----
| German   | 10GB
| French   | 10GB
| English  | 10GB
| Finnish  | 9.5GB
| Swedish  | 9.7GB

We then calculate the subword fertility rate and portion of `[UNK]`s over the following NER corpora:

| Language | NER corpora
| -------- | ------------------
| German   | CLEF-HIPE, NewsEye
| French   | CLEF-HIPE, NewsEye
| English  | CLEF-HIPE
| Finnish  | NewsEye
| Swedish  | NewsEye

Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:

| Language | Subword fertility  | Unknown portion
| -------- | ------------------ | ---------------
| German   | 1.43               | 0.0004
| French   | 1.25               | 0.0001
| English  | 1.25               | 0.0
| Finnish  | 1.69               | 0.0007
| Swedish  | 1.43               | 0.0

Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:

| Language | Subword fertility  | Unknown portion
| -------- | ------------------ | ---------------
| German   | 1.31               | 0.0004
| French   | 1.16               | 0.0001
| English  | 1.17               | 0.0
| Finnish  | 1.54               | 0.0007
| Swedish  | 1.32               | 0.0

# Final pretraining corpora

We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:

| Language | Size
| -------- | ----
| German   | 28GB
| French   | 27GB
| English  | 24GB
| Finnish  | 27GB
| Swedish  | 27GB

Total size is 130GB.

# Pretraining

## Multilingual model

We train a multilingual BERT model using the 32k vocab with the official BERT implementation
on a v3-32 TPU using the following parameters:

```bash
python3 run_pretraining.py --input_file gs://histolectra/historic-multilingual-tfrecords/*.tfrecord \
--output_dir gs://histolectra/bert-base-historic-multilingual-cased \
--bert_config_file ./config.json \
--max_seq_length=512 \
--max_predictions_per_seq=75 \
--do_train=True \
--train_batch_size=128 \
--num_train_steps=3000000 \
--learning_rate=1e-4 \
--save_checkpoints_steps=100000 \
--keep_checkpoint_max=20 \
--use_tpu=True \
--tpu_name=electra-2 \
--num_tpu_cores=32
```

The following plot shows the pretraining loss curve:

![Training loss curve](stats/figures/pretraining_loss_historic-multilingual.png)

## English model

The English BERT model - with texts from British Library corpus - was trained with the Hugging Face
JAX/FLAX implementation for 10 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:

```bash
python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-historic-english-cased/ \
--tokenizer_name /mnt/datasets/bert-base-historic-english-cased/ \
--train_file /mnt/datasets/bl-corpus/bl_1800-1900_extracted.txt \
--validation_file /mnt/datasets/bl-corpus/english_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 10 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-historic-english-cased-512-noadafactor-10e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length
```

The following plot shows the pretraining loss curve:

![Training loss curve](stats/figures/pretraining_loss_historic_english.png)

## Finnish model

The BERT model - with texts from Finnish part of Europeana - was trained with the Hugging Face
JAX/FLAX implementation for 40 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:

```bash
python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
--tokenizer_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
--train_file /mnt/datasets/hlms/extracted_content_Finnish_0.6.txt \
--validation_file /mnt/datasets/hlms/finnish_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 40 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-finnish-europeana-cased-512-dupe1-noadafactor-40e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length
```

The following plot shows the pretraining loss curve:

![Training loss curve](stats/figures/pretraining_loss_finnish_europeana.png)

## Swedish model

The BERT model - with texts from Swedish part of Europeana - was trained with the Hugging Face
JAX/FLAX implementation for 40 epochs (approx. 660K steps) on a v3-8 TPU, using the following command:

```bash
python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
--tokenizer_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
--train_file /mnt/datasets/hlms/extracted_content_Swedish_0.6.txt \
--validation_file /mnt/datasets/hlms/swedish_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 40 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-swedish-europeana-cased-512-dupe1-noadafactor-40e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length
```

The following plot shows the pretraining loss curve:

![Training loss curve](stats/figures/pretraining_loss_swedish_europeana.png)

# Acknowledgments

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as
TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤

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 🤗


---
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 🤗


---
language: de
license: mit
tags:
  - "historic german"
---

# 🤗 + 📚 dbmdz BERT models

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉

# German Europeana BERT

We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
that were provided by *The European Library*. The final
training corpus has a size of 51GB and consists of 8,035,986,369 tokens.

Detailed information about the data and pretraining steps can be found in
[this repository](https://github.com/stefan-it/europeana-bert).

## 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-german-europeana-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/vocab.txt)

## Results

For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert).

## Usage

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

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-europeana-uncased")
model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-uncased")
```

# 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 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 🤗


---
language: fr
license: mit
tags:
  - "historic french"
---
# 🤗 + 📚 dbmdz ELECTRA models

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models 🎉

# French Europeana ELECTRA

We extracted all French texts using the `language` metadata attribute from the Europeana corpus.

The resulting corpus has a size of 63GB and consists of 11,052,528,456 tokens.

Based on the metadata information, texts from the 18th - 20th century are mainly included in the
training corpus.

Detailed information about the data and pretraining steps can be found in
[this repository](https://github.com/stefan-it/europeana-bert).

## Model weights

ELECTRA model weights for PyTorch and TensorFlow are available.

* French Europeana ELECTRA (discriminator): `dbmdz/electra-base-french-europeana-cased-discriminator` - [model hub page](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-discriminator/tree/main)
* French Europeana ELECTRA (generator): `dbmdz/electra-base-french-europeana-cased-generator` - [model hub page](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-generator/tree/main)

## Results

For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert).

## Usage

With Transformers >= 2.3 our French Europeana ELECTRA model can be loaded like:

```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-french-europeana-cased-discriminator")
model = AutoModel.from_pretrained("dbmdz/electra-base-french-europeana-cased-discriminator")
```

# 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 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 our models from their S3 storage 🤗


---
language: dutch
license: mit
widget:
- text: "de [MASK] vau Financien, in hec vorige jaar, da inkomswi"
---

# Language Model for Historic Dutch

In this repository we open source a language model for Historic Dutch, trained on the
[Delpher Corpus](https://www.delpher.nl/over-delpher/delpher-open-krantenarchief/download-teksten-kranten-1618-1879\),
that include digitized texts from Dutch newspapers, ranging from 1618 to 1879.

# Changelog

* 13.12.2021: Initial version of this repository.

# Model Zoo

The following models for Historic Dutch are available on the Hugging Face Model Hub:

| Model identifier                       | Model Hub link
| -------------------------------------- | -------------------------------------------------------------------
| `dbmdz/bert-base-historic-dutch-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-dutch-cased)

# Stats

The download urls for all archives can be found [here](delpher-corpus.urls).

We then used the awesome `alto-tools` from [this](https://github.com/cneud/alto-tools)
repository to extract plain text. The following table shows the size overview per year range:

| Period    | Extracted plain text size
| --------- | -------------------------:
| 1618-1699 | 170MB
| 1700-1709 | 103MB
| 1710-1719 |  65MB
| 1720-1729 | 137MB
| 1730-1739 | 144MB
| 1740-1749 | 188MB
| 1750-1759 | 171MB
| 1760-1769 | 235MB
| 1770-1779 | 271MB
| 1780-1789 | 414MB
| 1790-1799 | 614MB
| 1800-1809 | 734MB
| 1810-1819 | 807MB
| 1820-1829 | 987MB
| 1830-1839 | 1.7GB
| 1840-1849 | 2.2GB
| 1850-1854 | 1.3GB
| 1855-1859 | 1.7GB
| 1860-1864 | 2.0GB
| 1865-1869 | 2.3GB
| 1870-1874 | 1.9GB
| 1875-1876 | 867MB
| 1877-1879 | 1.9GB

The total training corpus consists of 427,181,269 sentences and 3,509,581,683 tokens (counted via `wc`),
resulting in a total corpus size of 21GB.

The following figure shows an overview of the number of chars per year distribution:

![Delpher Corpus Stats](figures/delpher_corpus_stats.png)

# Language Model Pretraining

We use the official [BERT](https://github.com/google-research/bert) implementation using the following command
to train the model:

```bash
python3 run_pretraining.py --input_file gs://delpher-bert/tfrecords/*.tfrecord \
--output_dir gs://delpher-bert/bert-base-historic-dutch-cased \
--bert_config_file ./config.json \
--max_seq_length=512 \
--max_predictions_per_seq=75 \
--do_train=True \
--train_batch_size=128 \
--num_train_steps=3000000 \
--learning_rate=1e-4 \
--save_checkpoints_steps=100000 \
--keep_checkpoint_max=20 \
--use_tpu=True \
--tpu_name=electra-2 \
--num_tpu_cores=32
```

We train the model for 3M steps using a total batch size of 128 on a v3-32 TPU. The pretraining loss curve can be seen
in the next figure:

![Delpher Pretraining Loss Curve](figures/training_loss.png)

# Evaluation

We evaluate our model on the preprocessed Europeana NER dataset for Dutch, that was presented in the
["Data Centric Domain Adaptation for Historical Text with OCR Errors"](https://github.com/stefan-it/historic-domain-adaptation-icdar) paper.

The data is available in their repository. We perform a hyper-parameter search for:

* Batch sizes: `[4, 8]`
* Learning rates: `[3e-5, 5e-5]`
* Number of epochs: `[5, 10]`

and report averaged F1-Score over 5 runs with different seeds. We also include [hmBERT](https://github.com/stefan-it/clef-hipe/blob/main/hlms.md) as baseline model.

Results:

| Model               | F1-Score (Dev / Test)
| ------------------- | ---------------------
| hmBERT              | (82.73) / 81.34
| Maerz et al. (2021) | - / 84.2
| Ours                | (89.73) / 87.45

# License

All models are licensed under [MIT](LICENSE).

# Acknowledgments

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as
TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤

We thank [Clemens Neudecker](https://github.com/cneud) for maintaining the amazing
[ALTO tools](https://github.com/cneud/alto-tools) that were used for parsing the Delpher Corpus XML files.

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 🤗


---
language: de
license: mit
---

# 🤗 + 📚 dbmdz German BERT models

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models 🎉

# German BERT

## Stats

In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [deepset](https://deepset.ai/) we provide another German-language model.

The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus,
Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with
a size of 16GB and 2,350,234,427 tokens.

For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps
(sentence piece model for vocab generation) follow those used for training
[SciBERT](https://github.com/allenai/scibert). The model is trained with an initial
sequence length of 512 subwords and was performed for 1.5M steps.

This release includes both cased and uncased models.

## 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
| -------------------------------- | ---------------------------------------------------------------------------------------------------------------
| `bert-base-german-dbmdz-cased`   | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt)
| `bert-base-german-dbmdz-uncased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt)

## Usage

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

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased")
```

## Results

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

# 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 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 🤗


---
language: tr
license: mit
datasets:
- allenai/c4
---

# 🇹🇷 Turkish ConvBERT model

[![DOI](https://zenodo.org/badge/237817454.svg)](https://zenodo.org/badge/latestdoi/237817454)

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!

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


# 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.


---
tags:
- conversational
---

# George Costanza Model


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

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---
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*

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