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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: roberta-base-fiqa-flm-sq-flit |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-base-fiqa-flm-sq-flit |
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This model is a fine-tuned version of roberta-base on a custom dataset create for question answering in |
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financial domain. |
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## Model description |
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RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. |
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The model was further processed as below for the specific downstream QA task. |
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1. Pretrained for domain adaptation with Masked language modeling (MLM) objective with |
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the FIQA challenge Opinion-based QA task is available here - https://drive.google.com/file/d/1BlWaV-qVPfpGyJoWQJU9bXQgWCATgxEP/view |
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2. Pretrained with MLM objective with custom generated dataset for Banking and Finance. |
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3. Fine Tuned with SQuAD V2 dataset for QA task adaptation. |
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4. Fine Tuned with custom labeled dataset in SQuAD format for domain and task adaptation. |
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## Intended uses & limitations |
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The model is intended to be used for a custom Questions Answering system in the BFSI domain. |
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## Training and evaluation data |
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More information needed |
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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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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 2.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.15.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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