Transformers
PyTorch
English
Chinese
bart
text2text-generation
GENIUS
conditional text generation
sketch-based text generation
data augmentation
Instructions to use beyond/genius-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beyond/genius-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("beyond/genius-large") model = AutoModelForSeq2SeqLM.from_pretrained("beyond/genius-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -45,6 +45,6 @@ You can use this model directly with a pipeline for masked language modeling:
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```python
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from transformers import pipeline
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genius = pipeline("text2text-generation", model="beyond/genius-large", device=0)
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sketch = "
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generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]["generated_text"]
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```
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```python
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from transformers import pipeline
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genius = pipeline("text2text-generation", model="beyond/genius-large", device=0)
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sketch = "asdfasdfasdfasdfadsf"
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generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]["generated_text"]
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```
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