Instructions to use DaviadiAF/T5-Small_AbsSumm_XSumCNN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaviadiAF/T5-Small_AbsSumm_XSumCNN 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="DaviadiAF/T5-Small_AbsSumm_XSumCNN")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DaviadiAF/T5-Small_AbsSumm_XSumCNN") model = AutoModelForSeq2SeqLM.from_pretrained("DaviadiAF/T5-Small_AbsSumm_XSumCNN", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DaviadiAF/T5-Small_AbsSumm_XSumCNN: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/DaviadiAF/T5-Small_AbsSumm_XSumCNN/resolve/main/training_args.bin
- Command line
-
hf download hf://DaviadiAF/T5-Small_AbsSumm_XSumCNN/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DaviadiAF/T5-Small_AbsSumm_XSumCNN/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 3c1d52c13cbd2cea5529a04d462d740d74897db6b9e13e016da26a92f5b84941
- Size of remote file:
- 4.22 kB
- SHA256:
- 7caf23864f4516be6dde058af4d2b0d024f196e55469cb53776af30f29d9908b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.