Instructions to use pgajo/mdeberta-xlwa-en-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pgajo/mdeberta-xlwa-en-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="pgajo/mdeberta-xlwa-en-it")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("pgajo/mdeberta-xlwa-en-it") model = AutoModelForQuestionAnswering.from_pretrained("pgajo/mdeberta-xlwa-en-it") - Notebooks
- Google Colab
- Kaggle
Model description:
Model: mdeberta
Dataset: TASTEset
Unshuffled ratio: []
Shuffled ratio: []
Best exact match epoch: 3
Best exact match: 82.32
Best epoch: 3
Drop duplicates: []
Max epochs = 10
Optimizer lr = 3e-05
Optimizer eps = 1e-08
Batch size = 32
Dataset path = pgajo/mdeberta_xlwa_en-it
Results
| epoch | train_loss | train_f1 | train_exact | dev_loss | dev_f1 | dev_exact | test_loss | test_f1 | test_exact |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.32 | 91.77 | 90.75 | 1.26 | 81.86 | 80.66 | 0 | 0 | 0 |
| 2 | 0.04 | 98.92 | 98.74 | 1.67 | 82.05 | 81.26 | 0 | 0 | 0 |
| 3 | 0.02 | 99.4 | 99.29 | 1.46 | 82.58 | 82.32 | 0 | 0 | 0 |
| 4 | 0.02 | 99.53 | 99.45 | 1.73 | 82.6 | 81.97 | 0 | 0 | 0 |
| 5 | 0.01 | 99.71 | 99.65 | 1.63 | 82.05 | 81.52 | 0 | 0 | 0 |
| 6 | 0.01 | 99.66 | 99.59 | 1.86 | 82.16 | 82.02 | 0 | 0 | 0 |
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