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:
- 13bdbccb1262b219c7878ddddab9573fabfc48a0b4f56e9ed3448a83b9384132
- Size of remote file:
- 3.31 kB
- SHA256:
- 6334f2b1e1294962388c0dee1ef2490a1f681d8c90c9e21d654af6555e46a0a2
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