Instructions to use ashaba1in/t5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashaba1in/t5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ashaba1in/t5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ashaba1in/t5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload MT5ForConditionalGeneration
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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{
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"_name_or_path": "karpov/vebinar/summarization_t5/checkpoint-
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"architectures": [
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"MT5ForConditionalGeneration"
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],
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{
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"_name_or_path": "karpov/vebinar/summarization_t5/checkpoint-40000",
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"architectures": [
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"MT5ForConditionalGeneration"
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],
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1200729512
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version https://git-lfs.github.com/spec/v1
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oid sha256:dbee97be7486ef718dadd3cbc2b6792ed488c59a5ef281931859ece7665dd371
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size 1200729512
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