Instructions to use acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted") model = AutoModelForSeq2SeqLM.from_pretrained("acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted/resolve/main/training_args.bin
- Command line
-
hf download hf://acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/acmc/summarizer_google_long-t5-local-base_tf_idf_unfaceted/resolve/main/training_args.bin
4.28 kB
- Xet hash:
- 83e8be034ed9391d1efab797b8ea3d146b414675ba41e64e3ce1d670c3e312a7
- Size of remote file:
- 4.28 kB
- SHA256:
- 654cf0a749821ed8f49cef2c6214296040c072f7c65be5edcef08ca9c9619ae0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.