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--- |
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language: en |
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license: cc-by-4.0 |
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tags: |
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- roberta |
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- roberta-base |
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- masked-language-modeling |
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datasets: |
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- wikimovies |
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--- |
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# roberta-base for MLM |
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``` |
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model_name = "thatdramebaazguy/roberta-base-wikimovies" |
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pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask") |
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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:** Fill-Mask |
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**Training data:** wikimovies |
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**Eval data:** wikimovies |
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**Infrastructure**: 2x Tesla v100 |
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**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/shell_scripts/train_movie_roberta.sh) |
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## Hyperparameters |
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``` |
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num_examples = 4346 |
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batch_size = 16 |
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n_epochs = 3 |
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base_LM_model = "roberta-base" |
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learning_rate = 5e-05 |
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max_query_length=64 |
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Gradient Accumulation steps = 1 |
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Total optimization steps = 816 |
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evaluation_strategy=IntervalStrategy.NO |
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prediction_loss_only=False |
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per_device_train_batch_size=8 |
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per_device_eval_batch_size=8 |
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adam_beta1=0.9 |
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adam_beta2=0.999 |
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adam_epsilon=1e-08, |
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max_grad_norm=1.0 |
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lr_scheduler_type=SchedulerType.LINEAR |
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warmup_ratio=0.0 |
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seed=42 |
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eval_steps=500 |
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metric_for_best_model=None |
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greater_is_better=None |
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label_smoothing_factor=0.0 |
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``` |
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## Performance |
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perplexity = 4.3808 |
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Some of my work: |
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- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) |
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--- |
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