metadata

license: apache-2.0
datasets:
  - oscar-corpus/OSCAR-2109
language:
  - pl
  - en
pipeline_tag: text-generation
library_name: transformers

B-GPT_pl_en_sequential

The B-GPT Models are bilingual GPT-2 style models. For the first half of training, this model was trained only on Polish data. In the second half of training, the model was trained on only {language_2} data.. At the end of training, 50 % of training data seen by the model is Polish and 50 % is English. The tokenizer was trained on the same proportions of Polish and English data.

Model details:

All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
Details for this model specifically:

* Architecture: gpt2
* Parameters: 124770816
* Maximum sequence length: 512 tokens
* Training text data (raw): [XXXX]
* Training tokens: 12B
* Vocabulary size: 50000
* Compute cost: ~9 NVIDIA A6000 GPU hours
* CO2 Emission: 1.17 kg

Training datasets (percentages prior to deduplication):
* 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)

Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.

## Use This Model

Load the model:

```
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("B-GPT_pl_en_sequential")
model = AutoModel.from_pretrained("B-GPT_pl_en_sequential")


````

Text Generation:

```
from transformers import pipeline

pipe = pipeline("text-generation", model="B-GPT_pl_en_sequential")

pipe("I am a")

```

## Citation

If you use this model, please cite:

```


```