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
File size: 329 Bytes
5b3e33d | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |