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README.md
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thumbnail: "https://huggingface.co/Tanhim/gpt2-model-de" <br />
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datasets: Ten Thousand German News Articles Dataset <br />
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from transformers import AutoTokenizer, AutoModelWithLMHead <br />
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tokenizer = AutoTokenizer.from_pretrained("Tanhim/gpt2-model-de") <br />
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model = AutoModelWithLMHead.from_pretrained("Tanhim/gpt2-model-de") <br />
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text-generation = pipeline("text-generation", model="Tanhim/gpt2-model-de", tokenizer="anonymous-german-nlp/german-gpt2") <br />
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thumbnail: "https://huggingface.co/Tanhim/gpt2-model-de" <br />
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datasets: Ten Thousand German News Articles Dataset <br />
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### How to use
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You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
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set a seed for reproducibility:
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```python
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>>> from transformers import pipeline, set_seed
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>>> generation= pipeline('text-generation', model='Tanhim/gpt2-model-de')
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>>> set_seed(42)
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>>> generation("Hallo, ich bin ein Sprachmodell,", max_length=30, num_return_sequences=5)
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import AutoTokenizer, AutoModelWithLMHead <br />
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tokenizer = AutoTokenizer.from_pretrained("Tanhim/gpt2-model-de") <br />
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model = AutoModelWithLMHead.from_pretrained("Tanhim/gpt2-model-de") <br />
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text = "Ersetzen Sie mich durch einen beliebigen Text, den Sie wünschen."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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