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:
- 1ef4007719203edc31941d81f691fdf9d86a70697fe5a92ec35f6f68def98715
- Size of remote file:
- 2.19 MB
- SHA256:
- 521fae3db2f93d0238fced2c91ff62bcb78781d58a0692602a5b5ecdf2a897e6
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