Instructions to use remi/bertabs-finetuned-extractive-abstractive-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remi/bertabs-finetuned-extractive-abstractive-summarization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="remi/bertabs-finetuned-extractive-abstractive-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization") model = AutoModelForMaskedLM.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a0e32833954b4600e95919176302e754599be8e2c2bd726b1edb94e684cd59d
- Size of remote file:
- 438 MB
- SHA256:
- a3bb5f9d3e52b351b80abbf2f59fea4c2f666c1d6bd5bb0cc9f4d952c73b8889
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.