Summarization
Transformers
PyTorch
Enawené-Nawé
mt5
text2text-generation
Trained with AutoTrain
Eval Results (legacy)
Instructions to use ell-hol/mT5-OrangeSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ell-hol/mT5-OrangeSum 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="ell-hol/mT5-OrangeSum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ell-hol/mT5-OrangeSum") model = AutoModelForSeq2SeqLM.from_pretrained("ell-hol/mT5-OrangeSum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ell-hol/mT5-OrangeSum: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/ell-hol/mT5-OrangeSum/resolve/main/tokenizer.json
- Command line
-
hf download hf://ell-hol/mT5-OrangeSum/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ell-hol/mT5-OrangeSum/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 754e6a77bf7ec2a3ca81dcd6f2bd033a042bef5eab69dab5c1f552d875b9b7d5
- Size of remote file:
- 16.3 MB
- SHA256:
- 9ea1e7ff2e76290d0ef2a7fbfad581d3c7aaf82f0c247c443b012c7bd6b8a885
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.