Instructions to use ConnorLin/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConnorLin/summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ConnorLin/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ConnorLin/summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload T5ForConditionalGeneration
Browse files- config.json +1 -1
- generation_config.json +6 -0
- model.safetensors +2 -2
config.json
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{
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"_name_or_path": "../flan-t5-base-samsum/checkpoint-11049",
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"architectures": [
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"
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],
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"classifier_dropout": 0.0,
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"d_ff": 2048,
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{
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"_name_or_path": "../flan-t5-base-samsum/checkpoint-11049",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"classifier_dropout": 0.0,
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"d_ff": 2048,
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generation_config.json
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{
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.35.2"
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:ec7648d626024084bc154e64cb13607f53811ce63470530a4d80cad3fba12b06
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size 990345064
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