Summarization
Transformers
PyTorch
Arabic
mbart
text2text-generation
AraBERT
BERT
BERT2BERT
MSA
Arabic Text Summarization
Arabic News Title Generation
Arabic Paraphrasing
Summarization
Generated from Trainer
Transformers
PyTorch
Instructions to use abdalrahmanshahrour/arabartsummarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdalrahmanshahrour/arabartsummarization 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="abdalrahmanshahrour/arabartsummarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("abdalrahmanshahrour/arabartsummarization") model = AutoModelForSeq2SeqLM.from_pretrained("abdalrahmanshahrour/arabartsummarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e0280157b43cf51d66bd9f43802b2006d8ef3dfc27dc9b89d394f47bd60bd2b
- Size of remote file:
- 557 MB
- SHA256:
- 736804a6f8d47baa5b0a3271a96f105c6984f652798ea192d8a750c26713cb69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.