Summarization
Transformers
PyTorch
mt5
text2text-generation
arabic
ar
Abstractive Summarization
Generated from Trainer
Instructions to use eslamxm/mt5-base-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eslamxm/mt5-base-arabic 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-arabic")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eslamxm/mt5-base-arabic") model = AutoModelForSeq2SeqLM.from_pretrained("eslamxm/mt5-base-arabic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff0ecfe21520ba4dcabd7ca6f80a0fd231e1bcf0f267d3418ff200fb80719808
- Size of remote file:
- 2.33 GB
- SHA256:
- db2992bd63726e3d775b7476cafc85311cbe874271011a30fd9fe3dac44df6e7
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