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:
- 68e79b1e60976cea7bbee4110851c669cfc6d23df424a2c6030b21bb921ca0f6
- Size of remote file:
- 2.33 GB
- SHA256:
- c0c7d2de0d314232e9d97d516a196c0704a23e92fa058545a631763200a5f8ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.