Summarization
Transformers
PyTorch
TensorBoard
ONNX
Safetensors
German
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use Shahm/bart-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shahm/bart-german 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="Shahm/bart-german")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shahm/bart-german") model = AutoModelForSeq2SeqLM.from_pretrained("Shahm/bart-german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shahm/bart-german: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/Shahm/bart-german/resolve/refs%2Fpr%2F13/pytorch_model.bin
- Command line
-
hf download hf://Shahm/bart-german@refs/pr/13/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shahm/bart-german/resolve/refs%2Fpr%2F13/pytorch_model.bin
558 MB
- Xet hash:
- 9d14ae8052addfeb9f4145041068d82e23eff0e305a4235db09f3a96d801a6ce
- Size of remote file:
- 558 MB
- SHA256:
- 60664a4c271c80b0cc8e7fec6b817425de79f9bbc8bae6369ccfb8eb0dda59bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.