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:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("beyond/genius-large") model = AutoModelForSeq2SeqLM.from_pretrained("beyond/genius-large") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -52,4 +52,14 @@ sketch = "your_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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print(generated_text)
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```
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generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
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print(generated_text)
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```
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```
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from transformers import pipeline
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# 1. load the model with the huggingface `pipeline`
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genius = pipeline("text2text-generation", model='beyond/genius-large', device=0)
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# 2. provide a sketch (joint by <mask> tokens)
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sketch = "<mask> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>"
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# 3. here we go!
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generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
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print(generated_text)
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```
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