roberta-base-fiqa-flm-sq-flit
This model is a fine-tuned version of roberta-base on a custom dataset create for question answering in
financial domain.
Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion.
The model was further processed as below for the specific downstream QA task.
- Pretrained for domain adaptation with Masked language modeling (MLM) objective with
the FIQA challenge Opinion-based QA task is available here - https://drive.google.com/file/d/1BlWaV-qVPfpGyJoWQJU9bXQgWCATgxEP/view
- Pretrained with MLM objective with custom generated dataset for Banking and Finance.
- Fine Tuned with SQuAD V2 dataset for QA task adaptation.
- Fine Tuned with custom labeled dataset in SQuAD format for domain and task adaptation.
Intended uses & limitations
The model is intended to be used for a custom Questions Answering system in the BFSI domain.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
