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--- |
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datasets: |
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- imdb |
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- cornell_movie_dialogue |
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- SQuAD |
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language: |
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- English |
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thumbnail: |
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tags: |
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- roberta |
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- roberta-base |
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- question-answering |
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- qa |
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- movies |
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license: cc-by-4.0 |
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--- |
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# roberta-base + DAPT + Domain-Specific QA |
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Objective: |
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This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain. |
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https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta. |
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``` |
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model_name = "thatdramebaazguy/movie-roberta-squad" |
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pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering") |
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``` |
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## Overview |
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**Language model:** roberta-base |
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**Language:** English |
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**Downstream-task:** QA |
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**Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, SQuADv1 |
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**Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) |
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**Infrastructure**: 1x Tesla v100 |
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**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movieR_just_squadv1.sh) |
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## Hyperparameters |
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``` |
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Num examples = 88567 |
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Num Epochs = 10 |
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Instantaneous batch size per device = 32 |
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Total train batch size (w. parallel, distributed & accumulation) = 32 |
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``` |
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## Performance |
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### Eval on MoviesQA |
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- eval_samples = 5032 |
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- exact_match = 51.64944 |
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- f1 = 65.53983 |
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### Eval on SQuADv1 |
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- exact_match = 81.23936 |
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- f1 = 89.27827 |
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Github Repo: |
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- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) |
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--- |
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