Summarization
Transformers
PyTorch
TensorBoard
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use sunsvrv/bart-base-facebook with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunsvrv/bart-base-facebook 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="sunsvrv/bart-base-facebook")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sunsvrv/bart-base-facebook") model = AutoModelForSeq2SeqLM.from_pretrained("sunsvrv/bart-base-facebook", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sunsvrv/bart-base-facebook: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/sunsvrv/bart-base-facebook/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sunsvrv/bart-base-facebook/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sunsvrv/bart-base-facebook/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- b174bc83c25ae24c918c1aef992f657beed39a7549992e1140098ee1c4f1b4df
- Size of remote file:
- 558 MB
- SHA256:
- 2572b8513cfbc62d0dfd4204d0d27bf6966708a9f41a240b0402519d3f984668
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.