Instructions to use csebuetnlp/mT5_m2m_crossSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csebuetnlp/mT5_m2m_crossSum 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="csebuetnlp/mT5_m2m_crossSum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_m2m_crossSum") model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_m2m_crossSum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add metadata for dataset used to train model
#2
by davanstrien HF Staff - opened
This is a small PR to add the dataset used to train your model to the YAML metadata section. This metadata makes the link between the model and the dataset easy to see on the Hugging Face Hub.
abhik1505040 changed pull request status to merged