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 training_args.bin from GenzNepal/mt5-summarize-nepali: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/GenzNepal/mt5-summarize-nepali/resolve/main/training_args.bin
- Command line
-
hf download hf://GenzNepal/mt5-summarize-nepali/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/GenzNepal/mt5-summarize-nepali/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 7d7603037c93791a74e8a34ecdfeed123151d6d59c507bee9528809240b9412d
- Size of remote file:
- 4.09 kB
- SHA256:
- de91be202d631515f85911afb0b2d2fc7b35c592d7525d3d8673571ea450a9c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.