Instructions to use rahular/varta-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahular/varta-t5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rahular/varta-t5") model = AutoModelForSeq2SeqLM.from_pretrained("rahular/varta-t5") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -2,5 +2,5 @@
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"model_max_length": 1000000000000000019884624838656,
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"name_or_path": "varta/t5-base",
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"special_tokens_map_file": "/home/sumanth/.cache/huggingface/hub/models--varta--t5-base/snapshots/4e276a3c9e7689b03dadc443b048464f034db144/special_tokens_map.json",
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-
"tokenizer_class": "
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}
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"model_max_length": 1000000000000000019884624838656,
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"name_or_path": "varta/t5-base",
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"special_tokens_map_file": "/home/sumanth/.cache/huggingface/hub/models--varta--t5-base/snapshots/4e276a3c9e7689b03dadc443b048464f034db144/special_tokens_map.json",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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