Question Answering
Transformers
TensorBoard
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
Instructions to use kxx-kkk/FYP_qa_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kxx-kkk/FYP_qa_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kxx-kkk/FYP_qa_final")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("kxx-kkk/FYP_qa_final") model = AutoModelForQuestionAnswering.from_pretrained("kxx-kkk/FYP_qa_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -39,10 +39,14 @@ The dataset used for training is listed below according to training sequences:
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1. [MRQA(train split)](https://huggingface.co/datasets/mrqa)
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2. [UCLNLP/adversarial_qa](https://huggingface.co/datasets/UCLNLP/adversarial_qa)
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3. [mbartolo/synQA](https://huggingface.co/datasets/mbartolo/synQA)
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4. [MRQA(test split)](https://huggingface.co/datasets/mrqa)
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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1. [MRQA(train split)](https://huggingface.co/datasets/mrqa)
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2. [UCLNLP/adversarial_qa](https://huggingface.co/datasets/UCLNLP/adversarial_qa)
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3. [mbartolo/synQA](https://huggingface.co/datasets/mbartolo/synQA)
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4. [MRQA(test split)](https://huggingface.co/datasets/mrqa)*This model
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## Training procedure
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The training approach uses the fine-tuning approach of transfer learning on the pre-trained model to perform NLP QA tasks.
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Each time a model was trained with one dataset only and saved as the PTMs for the next training.
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This model is the last model that trained with [MRQA(test split)](https://huggingface.co/datasets/mrqa).
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### Training hyperparameters
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The following hyperparameters were used during training:
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