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") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -14,7 +14,7 @@
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},
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"max_len": 512,
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"model_max_length": 512,
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-
"name_or_path": "
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"special_tokens_map_file": null,
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},
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"max_len": 512,
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"model_max_length": 512,
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"name_or_path": "he_ro_tokenizer",
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"special_tokens_map_file": null,
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