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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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---
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datasets:
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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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license: cc-by-4.0
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---
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# roberta-base + SQuAD QA
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Objective:
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This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.
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```
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model_name = "thatdramebaazguy/roberta-base-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:** SQuADv1
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**Eval data:** SQuAD
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**Infrastructure**: 2x 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) = 64
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```
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## Performance
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### Eval on SQuADv1
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- epoch = 10.0
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- eval_samples = 10790
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- exact_match = 83.6045
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- f1 = 91.1709
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### Eval on MoviesQA
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- eval_samples = 5032
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- exact_match = 51.6494
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- f1 = 68.2615
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Github Repo:
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- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
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