Instructions to use JeremyLi96/Control_Token_Joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremyLi96/Control_Token_Joint 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="JeremyLi96/Control_Token_Joint")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JeremyLi96/Control_Token_Joint") model = AutoModelForSeq2SeqLM.from_pretrained("JeremyLi96/Control_Token_Joint") - Notebooks
- Google Colab
- Kaggle
Zihao Li commited on
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README.md
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license: mit
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license: mit
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datasets:
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- bogdancazan/wikilarge-text-simplification
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language:
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- en
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metrics:
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- sari
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- bertscore
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pipeline_tag: summarization
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A customised model based on MUSS, with added special tokens and tokenization method.
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