Summarization
Transformers
PyTorch
TensorBoard
mt5
text2text-generation
arabic
am
es
amharic
Abstractive Summarization
Generated from Trainer
Instructions to use eslamxm/mt5-base-finetuned-ar-sp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eslamxm/mt5-base-finetuned-ar-sp 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="eslamxm/mt5-base-finetuned-ar-sp")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eslamxm/mt5-base-finetuned-ar-sp") model = AutoModelForSeq2SeqLM.from_pretrained("eslamxm/mt5-base-finetuned-ar-sp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cb369f376d0734fcfc080f3465f73f813f22bd35843d910ef958941dde0c504
- Size of remote file:
- 2.33 GB
- SHA256:
- d01822663d032efc8536a83e4d3dbb574757d970df8225a7ac85dbd113ca34d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.