Instructions to use ozcangundes/mt5-small-turkish-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ozcangundes/mt5-small-turkish-summarization 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="ozcangundes/mt5-small-turkish-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ozcangundes/mt5-small-turkish-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ozcangundes/mt5-small-turkish-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f341655afe210377b43bb13367aee54df321bf1af1455b061ed27a83bbb308a
- Size of remote file:
- 1.2 GB
- SHA256:
- 4582a7bbd296b9b186014925c4639bfbca68913e4d17938cddfb0c654079a510
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.