Instructions to use MM98/mt5-small-finetuned-pnsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MM98/mt5-small-finetuned-pnsum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MM98/mt5-small-finetuned-pnsum") model = AutoModelForSeq2SeqLM.from_pretrained("MM98/mt5-small-finetuned-pnsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MM98/mt5-small-finetuned-pnsum: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/MM98/mt5-small-finetuned-pnsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MM98/mt5-small-finetuned-pnsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MM98/mt5-small-finetuned-pnsum/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- e195c25adb1de4cb555ab1cc2682fdab1c83bdb879594d535f545ab971534c15
- Size of remote file:
- 1.2 GB
- SHA256:
- d4c88f0a7be12e7aa97eb2ed09467798b5504e49fc351e903e004cdb5456b990
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