Instructions to use HeNLP/HeRo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HeNLP/HeRo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HeNLP/HeRo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HeNLP/HeRo") model = AutoModelForMaskedLM.from_pretrained("HeNLP/HeRo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('HeNLP/HeRo')
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model = AutoModelForMaskedLM.from_pretrained('HeNLP/HeRo'
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```
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### Citing
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('HeNLP/HeRo')
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model = AutoModelForMaskedLM.from_pretrained('HeNLP/HeRo'
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# Tokenization Example:
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# Encoding and tokenizing
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encoded_string = tokenizer('שלום לכולם')
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# Decoding
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decoded_string = tokenizer.decode(encoded_string['input_ids'], skip_special_tokens=True)
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```
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### Citing
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