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Notebooks for fine-tuning a BERT model and training a LSTM model for financial QA

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bert bert-model colab deep-learning finance finbert finbert-qa information-retrieval lstm-model natural-language-processing notebooks qa-lstm question-answering

finbert-qa-notebooks's Introduction

Hi there, I am Bithiah (BITH-ee-ah) ๐Ÿ‘‹

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finbert-qa-notebooks's Issues

Cannot reproduce the numbers on the paper with this Notebook

Hey! Thanks for open-sourcing this amazing project!
Just a quick question, I folllowed strictly with this notebook FinBERT_QA.ipynb and cannot reproduce the numbers reported on the paper.
More specificly, with using bert-qa as the starting point, after

config = {'bert_model_name': bert-qa,
      'max_seq_len': 512,
      'batch_size': 16,
      'learning_rate': 3e-6,
      'weight_decay': 0.01,
      'n_epochs': 3,
      'num_warmup_steps': 10000}

The results I had was
Epoch 2:

Train Loss: 0.069 | Train Accuracy: 98.39%
Validation Loss: 0.089 | Validation Accuracy: 98.09%
Average nDCG@10 for 333 queries: 0.476
MRR@10 for 333 queries: 0.442
Average Precision@1 for 333 queries: 0.381

Epoch 3:

Train Loss: 0.055 | Train Accuracy: 98.75%
Validation Loss: 0.097 | Validation Accuracy: 98.1%

Average nDCG@10 for 333 queries: 0.471
MRR@10 for 333 queries: 0.427
Average Precision@1 for 333 queries: 0.357

but the reported nDCG@10 should be 0.481.

Any ideas/suggestions? Thank you!

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