Instructions to use songhieng/khmer-mt5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use songhieng/khmer-mt5-summarization 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="songhieng/khmer-mt5-summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("songhieng/khmer-mt5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("songhieng/khmer-mt5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload quantized-khmer-mt5.pth with huggingface_hub
Verified signature5c0bdfb -
Update README.md
Verified signaturef91ee24 -
Update README.md
Verified signaturec2668f6 -
Upload tokenizer
Verified signature242152b -
Upload MT5ForConditionalGeneration
Verified signatureaf86eec -
initial commit
Verified signature0d8a8b4