Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Instructions to use ARTeLab/it5-summarization-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/it5-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/it5-summarization-mlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-mlsum") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.0, | |
| "eval_gen_len": 32.7635, | |
| "eval_loss": 2.0189740657806396, | |
| "eval_rouge1": 19.2854, | |
| "eval_rouge2": 6.0392, | |
| "eval_rougeL": 16.4987, | |
| "eval_rougeLsum": 16.616, | |
| "eval_runtime": 647.9074, | |
| "eval_samples": 4000, | |
| "eval_samples_per_second": 6.174, | |
| "eval_steps_per_second": 1.029 | |
| } |