Instructions to use imvladikon/het5_small_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imvladikon/het5_small_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_small_summarization", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("imvladikon/het5_small_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("imvladikon/het5_small_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41511e06aff86409abfc336092e17a786f8aee594e768c6c19ad55fdfd4a830e
- Size of remote file:
- 16.3 MB
- SHA256:
- b750506e8d37d847db67c0541ebbedbfd426381d112543c653afbb85f65e51e4
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