Summarization
Transformers
TensorBoard
Safetensors
English
bart
text2text-generation
summarizer
text summarization
abstractive summarization
Instructions to use KipperDev/bart_summarizer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KipperDev/bart_summarizer_model 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="KipperDev/bart_summarizer_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/bart_summarizer_model") model = AutoModelForSeq2SeqLM.from_pretrained("KipperDev/bart_summarizer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KipperDev/bart_summarizer_model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/KipperDev/bart_summarizer_model/resolve/eeb7827634c0e2a7311ed3d00c6c447ca3a01cb2/training_args.bin
- Command line
-
hf download hf://KipperDev/bart_summarizer_model@eeb7827634c0e2a7311ed3d00c6c447ca3a01cb2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KipperDev/bart_summarizer_model/resolve/eeb7827634c0e2a7311ed3d00c6c447ca3a01cb2/training_args.bin
4.73 kB
- Xet hash:
- e24514eddb2c35ca92bab947b89360ba763c9256dd7a5e09237b9a9f5a2d62a5
- Size of remote file:
- 4.73 kB
- SHA256:
- 4dc1b0a6f82b9cd92e36b9d17cd37cb679e67a9ff7090aa2000954231e5354dc
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