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--- |
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language: en |
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license: mit |
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datasets: |
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- race |
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- ai2_arc |
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- openbookqa |
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metrics: |
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- accuracy |
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--- |
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# Roberta Large Fine Tuned on RACE |
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## Model description |
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This model follows the implementation by Allen AI team about [Aristo Roberta V7 Model](https://leaderboard.allenai.org/arc/submission/blcotvl7rrltlue6bsv0) given in [ARC Challenge](https://leaderboard.allenai.org/arc/submissions/public) |
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#### How to use |
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```python |
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import datasets |
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from transformers import RobertaTokenizer |
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from transformers import RobertaForMultipleChoice |
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tokenizer = RobertaTokenizer.from_pretrained( |
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"LIAMF-USP/aristo-roberta") |
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model = RobertaForMultipleChoice.from_pretrained( |
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"LIAMF-USP/aristo-roberta") |
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dataset = datasets.load_dataset( |
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"arc",, |
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split=["train", "validation", "test"], |
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) |
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training_examples = dataset[0] |
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evaluation_examples = dataset[1] |
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test_examples = dataset[2] |
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example=training_examples[0] |
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example_id = example["example_id"] |
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question = example["question"] |
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label_example = example["answer"] |
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options = example["options"] |
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if label_example in ["A", "B", "C", "D", "E"]: |
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label_map = {label: i for i, label in enumerate( |
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["A", "B", "C", "D", "E"])} |
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elif label_example in ["1", "2", "3", "4", "5"]: |
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label_map = {label: i for i, label in enumerate( |
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["1", "2", "3", "4", "5"])} |
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else: |
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print(f"{label_example} not found") |
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while len(options) < 5: |
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empty_option = {} |
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empty_option['option_context'] = '' |
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empty_option['option_text'] = '' |
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options.append(empty_option) |
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choices_inputs = [] |
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for ending_idx, option in enumerate(options): |
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ending = option["option_text"] |
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context = option["option_context"] |
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if question.find("_") != -1: |
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# fill in the banks questions |
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question_option = question.replace("_", ending) |
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else: |
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question_option = question + " " + ending |
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inputs = tokenizer( |
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context, |
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question_option, |
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add_special_tokens=True, |
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max_length=MAX_SEQ_LENGTH, |
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padding="max_length", |
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truncation=True, |
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return_overflowing_tokens=False, |
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) |
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if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0: |
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logging.warning(f"Question: {example_id} with option {ending_idx} was truncated") |
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choices_inputs.append(inputs) |
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label = label_map[label_example] |
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input_ids = [x["input_ids"] for x in choices_inputs] |
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attention_mask = ( |
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[x["attention_mask"] for x in choices_inputs] |
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# as the senteces follow the same structure, just one of them is |
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# necessary to check |
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if "attention_mask" in choices_inputs[0] |
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else None |
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) |
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example_encoded = { |
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"example_id": example_id, |
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"input_ids": input_ids, |
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"attention_mask": attention_mask, |
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"token_type_ids": token_type_ids, |
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"label": label |
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} |
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output = model(**example_encoded) |
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``` |
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## Training data |
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the Training data was the same as proposed [here](https://leaderboard.allenai.org/arc/submission/blcotvl7rrltlue6bsv0) |
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The only diferrence was the hypeparameters of RACE fine tuned model, which were reported [here](https://huggingface.co./LIAMF-USP/roberta-large-finetuned-race#eval-results) |
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## Training procedure |
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It was necessary to preprocess the data with a method that is exemplified for a single instance in the _How to use_ section. The used hyperparameters were the following: |
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| Hyperparameter | Value | |
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|:----:|:----:| |
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| adam_beta1 | 0.9 | |
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| adam_beta2 | 0.98 | |
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| adam_epsilon | 1.000e-8 | |
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| eval_batch_size | 16 | |
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| train_batch_size | 4 | |
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| fp16 | True | |
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| gradient_accumulation_steps | 4 | |
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| learning_rate | 0.00001 | |
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| warmup_steps | 0.06 | |
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| max_length | 256 | |
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| epochs | 4 | |
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The other parameters were the default ones from [Trainer](https://huggingface.co./transformers/main_classes/trainer.html) and [Trainer Arguments](https://huggingface.co./transformers/main_classes/trainer.html#trainingarguments) |
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## Eval results: |
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| Dataset Acc | Challenge Test | |
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|:----:|:----:| |
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| | 65.358 | |
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**The model was trained with a TITAN RTX** |
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