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.idea
 
 
xlnet-base-cased-finetuned-squad/runs/Jan23_16-46-24_DESKTOP-S7FLENI
xlnet-base-cased-finetuned-squad/runs/Jan23_16-46-24_DESKTOP-S7FLENI
 
 
.gitattributes
.gitattributes
 
 
.gitignore
.gitignore
 
 
README.md
README.md
 
 
distilbert.py
distilbert.py
 
 
distilbert_config.yaml
distilbert_config.yaml
 
 
distilbert_prediction.ipynb
distilbert_prediction.ipynb
 
 
electra.py
electra.py
 
 
electra_config.yaml
electra_config.yaml
 
 
electra_large.yaml
electra_large.yaml
 
 
electra_prediction.ipynb
electra_prediction.ipynb
 
 
heatmaps.ipynb
heatmaps.ipynb
 
 
mc2_report.docx
mc2_report.docx
 
 
mc2_report.pdf
mc2_report.pdf
 
 
npr_MC2_distilbert.ipynb
npr_MC2_distilbert.ipynb
 
 
npr_MC2_electra.ipynb
npr_MC2_electra.ipynb
 
 
npr_MC2_electra_big.ipynb
npr_MC2_electra_big.ipynb
 
 
npr_MC2_xlnet.ipynb
npr_MC2_xlnet.ipynb
 
 
requirements.txt
requirements.txt
 
 
utils.py
utils.py
 
 
xlnet.py
xlnet.py
 
 
xlnet_config.yaml
xlnet_config.yaml
 
 
xlnet_prediction.ipynb
xlnet_prediction.ipynb
 
 

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Question and Answering Model

For this task for Natural Language Processing at FHNW, we had to retrain selected question and answering models and evaluate them against each other.

More details

Please have a look at mc2_report.pdf.

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