Instructions to use csebuetnlp/mT5_m2o_english_crossSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csebuetnlp/mT5_m2o_english_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_m2o_english_crossSum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_m2o_english_crossSum") model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_m2o_english_crossSum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2545d18af91c35bf18186899a341f1389fc263e5e869a96b39c3a12c656bf7c6
- Size of remote file:
- 2.33 GB
- SHA256:
- 1ae55cc88f0be78dc923a013f7bbc1a1b2e39007e70b16930edbbc52e75a2940
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