Instructions to use mbshr/urt5-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbshr/urt5-base-finetuned with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="mbshr/urt5-base-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mbshr/urt5-base-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("mbshr/urt5-base-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add tokenizer
Browse files- special_tokens_map.json +5 -0
- spiece.model +3 -0
- tokenizer_config.json +12 -0
special_tokens_map.json
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{
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:2dabb5302a55dfe096bdb96cee79e2aa5676a9d158ff6ae8e034f064191eac0a
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size 916494
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tokenizer_config.json
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{
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"additional_special_tokens": null,
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"eos_token": "</s>",
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"extra_ids": 0,
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"name_or_path": "/content/drive/MyDrive/Finetune/urt5-base-news",
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"pad_token": "<pad>",
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"padding_side": "left",
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"sp_model_kwargs": {},
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"special_tokens_map_file": "/content/drive/MyDrive/urt5-base/special_tokens_map.json",
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"tokenizer_class": "T5Tokenizer",
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"unk_token": "<unk>"
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}
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