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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.0, | |
| "eval_gen_len": 33.5945, | |
| "eval_loss": 3.3335702419281006, | |
| "eval_rouge1": 19.3489, | |
| "eval_rouge2": 6.4028, | |
| "eval_rougeL": 16.3497, | |
| "eval_rougeLsum": 16.5387, | |
| "eval_runtime": 2520.0092, | |
| "eval_samples": 4000, | |
| "eval_samples_per_second": 1.587, | |
| "eval_steps_per_second": 1.587, | |
| "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, | |
| "train_loss": 2.0082999333540426, | |
| "train_runtime": 37501.9411, | |
| "train_samples": 31997, | |
| "train_samples_per_second": 3.413, | |
| "train_steps_per_second": 3.413 | |
| } |