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
Update README.md
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README.md
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license: afl-3.0
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---
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---
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license: afl-3.0
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---
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A MacBERTh model fine-tuned on SQuAD_v2. Hopefully, this will allow the model to perform well on QA tasks on historical texts.
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Finetune parameters:
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```
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training_args = TrainingArguments(
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output_dir="./results",
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evaluation_strategy="epoch",
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learning_rate=3e-5,
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per_device_train_batch_size=64,
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per_device_eval_batch_size=64,
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num_train_epochs=2,
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weight_decay=0.01,
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lr_scheduler_type=SchedulerType.LINEAR,
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warmup_ratio=0.2
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)
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```
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