Instructions to use csebuetnlp/mT5_m2m_crossSum_enhanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csebuetnlp/mT5_m2m_crossSum_enhanced 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_enhanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_m2m_crossSum_enhanced") model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_m2m_crossSum_enhanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from csebuetnlp/mT5_m2m_crossSum_enhanced: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/csebuetnlp/mT5_m2m_crossSum_enhanced/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://csebuetnlp/mT5_m2m_crossSum_enhanced/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/csebuetnlp/mT5_m2m_crossSum_enhanced/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 5590fe273d4cf71a803a1e397901ca0fa8fd68bcf363c13979a2b95c4c9b9f23
- Size of remote file:
- 2.33 GB
- SHA256:
- 3acf5b79dbb88238c554c713af691cd0e7355c1db19a3b168462e318fb186a7b
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