Summarization
Transformers
PyTorch
TensorBoard
mt5
text2text-generation
arabic
ar
fa
persian
Abstractive Summarization
Generated from Trainer
Instructions to use eslamxm/mt5-base-finetuned-arfa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eslamxm/mt5-base-finetuned-arfa 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-arfa")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eslamxm/mt5-base-finetuned-arfa") model = AutoModelForSeq2SeqLM.from_pretrained("eslamxm/mt5-base-finetuned-arfa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bfd116e59e509016ef6929ca5da0eae8de54358beaddd1aff19b94f3f4494e9
- Size of remote file:
- 3.31 kB
- SHA256:
- a77818cdfb3f81487d23944a3f8a7e9ddfef2996b8eac3c1438c078902cd70fc
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