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
Browse files
README.md
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- generated_from_trainer
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model-index:
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- name: FYP_qa_final
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results:
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datasets:
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- rajpurkar/squad_v2
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- mrqa
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- generated_from_trainer
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model-index:
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- name: FYP_qa_final
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results:
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squad_v2
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type: squad_v2
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config: squad_v2
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split: validation
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metrics:
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- type: exact_match
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value: 82.3
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name: Exact Match
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- type: f1
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value: 85.7701063996245
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name: F1
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squad
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type: squad
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config: plain_text
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split: validation
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metrics:
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- type: exact_match
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value: 89.9
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name: Exact Match
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- type: f1
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value: 93.57935153408677
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name: F1
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datasets:
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- rajpurkar/squad_v2
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- mrqa
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