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
- Xet hash:
- 5451f2e0776cbb52a9d580d85d87eafb8cc255714f99de6cf9096f8d638e9ae9
- Size of remote file:
- 2.33 GB
- SHA256:
- aaa66c0f7b4dedc667cf6a6199400af863a3409435fbf87da465b2e62a77d840
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