Summarization
Transformers
PyTorch
Safetensors
Hebrew
t5
text2text-generation
text-generation-inference
Instructions to use imvladikon/het5_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imvladikon/het5_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="imvladikon/het5_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("imvladikon/het5_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("imvladikon/het5_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d667c9f53d0dc5821351b40be719f42404882c4988cc1002053b6d3ebb5a2c4
- Size of remote file:
- 977 MB
- SHA256:
- fccaf2561c6c5ee505fedbddb09ca8a94263ca44cfa12afe7096c7e9eee4c337
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