Instructions to use ARTeLab/mbart-summarization-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/mbart-summarization-mlsum 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="ARTeLab/mbart-summarization-mlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/mbart-summarization-mlsum") - Notebooks
- Google Colab
- Kaggle
File size: 343 Bytes
6424b22 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"predict_gen_len": 33.5382,
"predict_loss": 3.2747132778167725,
"predict_rouge1": 19.8687,
"predict_rouge2": 6.6948,
"predict_rougeL": 16.8896,
"predict_rougeLsum": 17.0323,
"predict_runtime": 2521.7344,
"predict_samples": 4000,
"predict_samples_per_second": 1.586,
"predict_steps_per_second": 1.586
} |