Instructions to use taskload/bart-cause-effect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taskload/bart-cause-effect with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("taskload/bart-cause-effect") model = AutoModelForSeq2SeqLM.from_pretrained("taskload/bart-cause-effect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from taskload/bart-cause-effect: direct link, hf CLI and curl.
- Browser
- Download file 379 Bytes
-
https://huggingface.co/taskload/bart-cause-effect/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://taskload/bart-cause-effect@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/taskload/bart-cause-effect/resolve/refs%2Fpr%2F1/README.md
379 Bytes
Bart for Cause-Effect Extraction
This model was created using by Taskload, a group led by Henry Leonardi for automating information extraction. To get involved contact me at leonardi.henry@gmail.com
cause_effect = pipeline("summarization", model="taskload/bart-cause-effect")
output = cause_effect("text: "+event, max_length=300, min_length=30, do_sample=False)