Instructions to use RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096 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="RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096") model = AutoModelForMultimodalLM.from_pretrained("RMWeerasinghe/long-t5-tglobal-base-boardpapers-4096") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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- name: long-t5-tglobal-base-boardpapers-4096
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.37.0
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- Pytorch 2.1.2
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- Datasets 2.17.0
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- Tokenizers 0.15.1
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model-index:
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- name: long-t5-tglobal-base-boardpapers-4096
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results: []
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pipeline_tag: summarization
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.37.0
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- Pytorch 2.1.2
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- Tokenizers 0.15.1
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