Instructions to use longborn/attr2desc-bart-kw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use longborn/attr2desc-bart-kw 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="longborn/attr2desc-bart-kw")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("longborn/attr2desc-bart-kw") model = AutoModelForSeq2SeqLM.from_pretrained("longborn/attr2desc-bart-kw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from longborn/attr2desc-bart-kw: direct link, hf CLI and curl.
- Browser
- Download file 561 MB
-
https://huggingface.co/longborn/attr2desc-bart-kw/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://longborn/attr2desc-bart-kw/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/longborn/attr2desc-bart-kw/resolve/main/pytorch_model.bin
561 MB
- Xet hash:
- 9941c139fa9c9df2a71b4e830a58ff97aa9811bc7dfd7b9d770a3beca8d2bd97
- Size of remote file:
- 561 MB
- SHA256:
- 3c3a501fe2c06e6e968b4cca668f16c088d95769b00d6d1f935e517f833398e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.