Instructions to use nafisehNik/mt5-persian-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nafisehNik/mt5-persian-summary 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="nafisehNik/mt5-persian-summary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nafisehNik/mt5-persian-summary") model = AutoModelForSeq2SeqLM.from_pretrained("nafisehNik/mt5-persian-summary", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nafisehNik/mt5-persian-summary: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/nafisehNik/mt5-persian-summary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nafisehNik/mt5-persian-summary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nafisehNik/mt5-persian-summary/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- e8095da07fb961440d121a60020c23b757617f350110a7a5bc5c3aa254a91083
- Size of remote file:
- 1.2 GB
- SHA256:
- 68308d3bc96137d5a76d457e292400c581b74432a654dc00dfec8926539d57ee
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