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--- |
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datasets: |
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- MIT Movie |
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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 + Task Transfer (NER) --> Domain-Specific QA |
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Objective: |
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This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with additional information coming from a different task (NER - Task Transfer). |
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https://huggingface.co/thatdramebaazguy/roberta-base-MITmovie was used as the Roberta Base + NER model. |
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``` |
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model_name = "thatdramebaazguy/roberta-base-MITmovie-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:** NER --> QA |
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**Training data:** MIT Movie, SQuADv1 |
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**Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) |
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**Infrastructure**: 4x Tesla v100 |
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**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh) |
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## Hyperparameters |
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``` |
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Num examples = 88567 |
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Num Epochs = 3 |
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Instantaneous batch size per device = 32 |
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Total train batch size (w. parallel, distributed & accumulation) = 128 |
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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 = 55.80286 |
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- f1 = 70.31451 |
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### Eval on SQuADv1 |
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- exact_match = 85.6859 |
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- f1 = 91.96064 |
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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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