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"datasets[2]" with value "MIT Movie" is not valid. If possible, use a dataset id from https://huggingface.co/datasets.
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"language[0]" must only contain lowercase characters
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"language[0]" with value "English" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
roberta-base + DAPT + Task Transfer for Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> 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).
https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.
model_name = "thatdramebaazguy/movie-roberta-MITmovie-squad"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
Overview
Language model: roberta-base
Language: English
Downstream-task: NER --> QA
Training data: imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1
Eval data: MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA)
Infrastructure: 4x Tesla v100
Code: See example
Hyperparameters
Num examples = 88567
Num Epochs = 3
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 128
Performance
Eval on SQuADv1
- eval_samples = 10790
- exact_match = 83.0274
- f1 = 90.1615
Eval on MoviesQA
- eval_samples = 5032
- exact_match = 51.64944
- f1 = 65.53983
Github Repo:
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