Instructions to use remiai3/Text_Summarization_by_sshleifer_distilbart-cnn-12-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remiai3/Text_Summarization_by_sshleifer_distilbart-cnn-12-6 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="remiai3/Text_Summarization_by_sshleifer_distilbart-cnn-12-6")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("remiai3/Text_Summarization_by_sshleifer_distilbart-cnn-12-6", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Text Summarization (CPU/GPU)
- Model:
sshleifer/distilbart-cnn-12-6(Apache-2.0) - Task: Abstractive summarization.
- Note: Here we just provide the resources for to run this models in the laptops we didn't develop this entire models we just use the open source models for the experiment this model is developed by sshleifer
Quick start (any project)
# 1) Create env
python -m venv venv && source .venv/bin/activate # Windows: ./venv/Scripts/activate
# 2) Install deps
pip install -r requirements.txt
# 3) Run
python main.py --help
Tip: If you have a GPU + CUDA, PyTorch will auto-use it. If not, everything runs on CPU (slower but works).
Model tree for remiai3/Text_Summarization_by_sshleifer_distilbart-cnn-12-6
Base model
sshleifer/distilbart-cnn-12-6