Summarization
Transformers
PyTorch
Safetensors
Nepali
mt5
text2text-generation
Generated from Trainer
nepali
Instructions to use GenzNepal/mt5-summarize-nepali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GenzNepal/mt5-summarize-nepali 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="GenzNepal/mt5-summarize-nepali")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("GenzNepal/mt5-summarize-nepali") model = AutoModelForSeq2SeqLM.from_pretrained("GenzNepal/mt5-summarize-nepali", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GenzNepal/mt5-summarize-nepali: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/GenzNepal/mt5-summarize-nepali/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GenzNepal/mt5-summarize-nepali/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GenzNepal/mt5-summarize-nepali/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- dbcb1f2a5f8c603151aceccecd7fcbb48e4b712921127fb3d9b2e438000efd10
- Size of remote file:
- 1.2 GB
- SHA256:
- 125e4334e40e620da4870efd62035e4e56f6e0de8bb1fad0ac2a4064cdd5f35d
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