Instructions to use jhanvidesai/SummarizationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhanvidesai/SummarizationModel 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="jhanvidesai/SummarizationModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jhanvidesai/SummarizationModel") model = AutoModelForSeq2SeqLM.from_pretrained("jhanvidesai/SummarizationModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jhanvidesai/SummarizationModel: direct link, hf CLI and curl.
- Browser
- Download file 111 Bytes
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https://huggingface.co/jhanvidesai/SummarizationModel/resolve/main/README.md
- Command line
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hf download hf://jhanvidesai/SummarizationModel/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/jhanvidesai/SummarizationModel/resolve/main/README.md
111 Bytes
metadata
license: apache-2.0
library_name: transformers
tags:
- text2text-generation
pipeline_tag: summarization