Instructions to use Nadav/MacSQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadav/MacSQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Nadav/MacSQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Nadav/MacSQuAD") model = AutoModelForQuestionAnswering.from_pretrained("Nadav/MacSQuAD") - Notebooks
- Google Colab
- Kaggle
| license: afl-3.0 | |
| A MacBERTh model fine-tuned on SQuAD_v2. Hopefully, this will allow the model to perform well on QA tasks on historical texts. | |
| Finetune parameters: | |
| ``` | |
| training_args = TrainingArguments( | |
| output_dir="./results", | |
| evaluation_strategy="epoch", | |
| learning_rate=3e-5, | |
| per_device_train_batch_size=64, | |
| per_device_eval_batch_size=64, | |
| num_train_epochs=2, | |
| weight_decay=0.01, | |
| lr_scheduler_type=SchedulerType.LINEAR, | |
| warmup_ratio=0.2 | |
| ) | |
| ``` | |
| Evaluation metrics on the validation set of SQuAD_v2: | |
| ``` | |
| {'exact': 49.49886296639434, 'f1': 53.9199170778635, 'total': 11873, 'HasAns_exact': 60.08771929824562, 'HasAns_f1': 68.94250598270429, 'HasAns_total': 5928, 'NoAns_exact': 38.940285954583686, 'NoAns_f1': 38.940285954583686, 'NoAns_total': 5945, 'best_exact': 50.5095595047587, 'best_exact_thresh': 0.0, 'best_f1': 51.75825524534494, 'best_f1_thresh': 0.0} | |
| ``` |