pytorch jax gpt2 text-generation de transformers
---
language: de

widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
---

# BPT2

See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("tursunali/bpt2")
model = AutoModelForCausalLM.from_pretrained("tursunali/bpt2")

prompt = "<your prompt>"

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(pipe(prompt)[0]["generated_text"])
```

Also, two tricks might improve the generated text:

```python
output = model.generate(
    # during training an EOS token was used to mark the beginning of each text
    # so it can help to insert it at the start
    torch.tensor(
        [tokenizer.eos_token_id] + tokenizer.encode(prompt)
    ).unsqueeze(0),
    do_sample=True,
    # try setting bad_words_ids=[[0]] to disallow generating an EOS token, without this the model is
    # prone to ending generation early because a significant number of texts from the training corpus
    # is quite short
    bad_words_ids=[[0]],
    max_length=max_length,
)[0]
print(tokenizer.decode(output))
```

## Citing

Please cite BPT2 as follows:

```
@misc{Backpacker_Trail_German_large_2022,
author = {BackpackerTrail, Tursunali Kholdorov},
title = {{BPT2: Backpacker Trail German versions of BPT2}},
url = {https://github.com/Tursunali-Kholdorov/bptTrainer},
year = {2022}
}
```
