Instructions to use JimmyPin/mt5-finetuned-summarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JimmyPin/mt5-finetuned-summarize 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="JimmyPin/mt5-finetuned-summarize")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JimmyPin/mt5-finetuned-summarize") model = AutoModelForSeq2SeqLM.from_pretrained("JimmyPin/mt5-finetuned-summarize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a37a37217227eb8fb1be63a746e5d50da26666e2170420f036ca16a781a0c5ab
- Size of remote file:
- 16.4 MB
- SHA256:
- 5deea7934e42d5e44607e1a384c997a0337a750fa1819fd4d60b02f45a2f8b37
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.