| language: en | |
| license: cc-by-4.0 | |
| tags: | |
| - roberta | |
| - roberta-base | |
| - question-answering | |
| - qa | |
| datasets: | |
| - SQuAD | |
| # roberta-base + SQuAD QA | |
| Objective: | |
| This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions. | |
| ``` | |
| model_name = "thatdramebaazguy/roberta-base-squad" | |
| pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering") | |
| ``` | |
| ## Overview | |
| **Language model:** roberta-base | |
| **Language:** English | |
| **Downstream-task:** QA | |
| **Training data:** SQuADv1 | |
| **Eval data:** SQuAD | |
| **Infrastructure**: 2x Tesla v100 | |
| **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movieR_just_squadv1.sh) | |
| ## Hyperparameters | |
| ``` | |
| Num examples = 88567 | |
| Num Epochs = 10 | |
| Instantaneous batch size per device = 32 | |
| Total train batch size (w. parallel, distributed & accumulation) = 64 | |
| ``` | |
| ## Performance | |
| ### Eval on SQuADv1 | |
| - epoch = 10.0 | |
| - eval_samples = 10790 | |
| - exact_match = 83.6045 | |
| - f1 = 91.1709 | |
| ### Eval on MoviesQA | |
| - eval_samples = 5032 | |
| - exact_match = 51.6494 | |
| - f1 = 68.2615 | |
| Github Repo: | |
| - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) | |
| --- | |