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 pytorch_model.bin from DaviadiAF/T5-Small_AbsSumm_XSumCNN: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/DaviadiAF/T5-Small_AbsSumm_XSumCNN/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DaviadiAF/T5-Small_AbsSumm_XSumCNN/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DaviadiAF/T5-Small_AbsSumm_XSumCNN/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- d0326de37f58f08dd29049773fd571dfb125fc48a6f8439111056fea82455311
- Size of remote file:
- 242 MB
- SHA256:
- 99adeff4ad524f849708661870a5f38148cd01375507a293a3a0b7f95ab4b84c
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