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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-mlsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from ARTeLab/it5-summarization-mlsum: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/ARTeLab/it5-summarization-mlsum/resolve/refs%2Fpr%2F2/train_results.json
- Command line
-
hf download hf://ARTeLab/it5-summarization-mlsum@refs/pr/2/train_results.json
-
curl -L -o train_results.json https://huggingface.co/ARTeLab/it5-summarization-mlsum/resolve/refs%2Fpr%2F2/train_results.json
194 Bytes
| { | |
| "epoch": 4.0, | |
| "train_loss": 2.132151625336569, | |
| "train_runtime": 19550.5305, | |
| "train_samples": 31997, | |
| "train_samples_per_second": 6.547, | |
| "train_steps_per_second": 1.091 | |
| } |