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''' |
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Adapted from https://github.com/lupantech/ScienceQA |
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''' |
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from dataclasses import dataclass |
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from typing import List, Optional |
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def get_question_text(problem): |
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question = problem['question'] |
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return question |
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def get_context_text(problem, use_caption): |
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txt_context = problem['hint'] |
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img_context = problem['caption'] if use_caption else "" |
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context = " ".join([txt_context, img_context]).strip() |
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if context == "": |
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context = "N/A" |
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return context |
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def get_choice_text(probelm, options): |
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choices = probelm['choices'] |
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choice_list = [] |
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for i, c in enumerate(choices): |
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choice_list.append("({}) {}".format(options[i], c)) |
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choice_txt = " ".join(choice_list) |
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return choice_txt |
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def get_origin_answer(problem, options): |
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return problem['choices'][problem['answer']] |
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def get_answer(problem, options): |
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return options[problem['answer']] |
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def get_lecture_text(problem): |
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lecture = problem['lecture'].replace("\n", "\\n") |
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return lecture |
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def get_solution_text(problem): |
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solution = problem['solution'].replace("\n", "\\n") |
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return solution |
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def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True, WithOutput = False, curr_le_data=None): |
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input_format, output_format = format.split("-") |
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if input_format == "CQM": |
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input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" |
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elif input_format == "QCM": |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" |
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elif input_format == "QM": |
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input = f"Question: {question}\nOptions: {choice}\n" |
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elif input_format == "QC": |
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input = f"Question: {question}\nContext: {context}\n" |
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elif input_format == "QCMG": |
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if curr_le_data is not None: |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n{curr_le_data}\n" |
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else: |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nSolution: {lecture} {solution}\n" |
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elif input_format == "CQMG": |
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if curr_le_data is not None: |
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input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n{curr_le_data}\n" |
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else: |
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input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\nSolution: {lecture} {solution}\n" |
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elif input_format == "QCML": |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" |
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elif input_format == "QCME": |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" |
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elif input_format == "QCMLE": |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" |
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elif input_format == "QCLM": |
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input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" |
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elif input_format == "QCEM": |
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input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" |
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elif input_format == "QCLEM": |
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input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" |
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elif input_format == "QCMA": |
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input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nAnswer: The answer is {answer}.\n" |
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elif input_format == "QCA": |
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input = f"Question: {question}\nContext: {context}\nAnswer: The answer is {answer}. \nBECAUSE:" |
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if test_example: |
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if output_format == 'A': |
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output = "Answer:" |
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elif output_format == 'E': |
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output = "Solution:" |
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else: |
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output = "Solution:" |
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elif output_format == 'A': |
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output = f"Answer: The answer is {answer}." |
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elif output_format == 'AL': |
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output = f"Answer: The answer is {answer}. BECAUSE: {solution}" |
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elif output_format == 'AE': |
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output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" |
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elif output_format == 'ALE': |
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output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" |
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elif output_format == 'AEL': |
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output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" |
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elif output_format == 'LA': |
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output = f"Answer: {lecture} The answer is {answer}." |
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elif output_format == 'EA': |
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output = f"Answer: {solution} The answer is {answer}." |
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elif output_format == 'LEA': |
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output = f"Answer: {lecture} {solution} The answer is {answer}." |
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elif output_format == 'ELA': |
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output = f"Answer: {solution} {lecture} The answer is {answer}." |
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elif output_format == 'LE': |
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output = f"Solution: {lecture} {solution}." |
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elif output_format == 'E': |
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output = f"Solution: {solution}" |
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if WithOutput: |
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if output.endswith("BECAUSE:"): |
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output = output.replace("BECAUSE:", "").strip() |
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if output_format == 'E': |
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text = input + f'Solution:' |
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elif output_format == 'A': |
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text = input + f'Answer:' |
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else: |
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text = input + f'Solution:' |
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text = text.replace(" ", " ").strip() |
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output = output.replace(" ", " ").strip() |
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return text, output |
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text = input + output |
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text = text.replace(" ", " ").strip() |
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if text.endswith("BECAUSE:"): |
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text = text.replace("BECAUSE:", "").strip() |
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return text |
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def build_prompt(problems, shot_qids, test_qid, args): |
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examples = [] |
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for qid in shot_qids: |
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question = get_question_text(problems[qid]) |
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context = get_context_text(problems[qid], args.use_caption) |
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choice = get_choice_text(problems[qid], args.options) |
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answer = get_answer(problems[qid], args.options) |
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lecture = get_lecture_text(problems[qid]) |
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solution = get_solution_text(problems[qid]) |
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train_example = create_one_example(args.prompt_format, |
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question, |
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context, |
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choice, |
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answer, |
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lecture, |
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solution, |
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test_example=False) |
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examples.append(train_example) |
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question = get_question_text(problems[test_qid]) |
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context = get_context_text(problems[test_qid], args.use_caption) |
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choice = get_choice_text(problems[test_qid], args.options) |
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answer = get_answer(problems[test_qid], args.options) |
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lecture = get_lecture_text(problems[test_qid]) |
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solution = get_solution_text(problems[test_qid]) |
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test_example = create_one_example(args.prompt_format, |
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question, |
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context, |
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choice, |
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answer, |
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lecture, |
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solution, |
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test_example=True) |
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examples.append(test_example) |
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prompt_input = '\n\n'.join(examples) |
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return prompt_input |
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def build_train_pair(problems, test_qid, args, curr_le_data=None): |
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examples = [] |
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question = get_question_text(problems[test_qid]) |
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context = get_context_text(problems[test_qid], args.use_caption) |
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choice = get_choice_text(problems[test_qid], args.options) |
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lecture = get_lecture_text(problems[test_qid]) |
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solution = get_solution_text(problems[test_qid]) |
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answer_option = get_answer(problems[test_qid], args.options) |
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answer = "(" + answer_option + ")" |
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test_example, target = create_one_example(args.prompt_format, |
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question, |
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context, |
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choice, |
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answer, |
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lecture, |
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solution, |
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test_example=False,WithOutput = True, curr_le_data=curr_le_data) |
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examples.append(test_example) |
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target = target.replace("Answer:", "").strip() |
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prompt_input = '\n\n'.join(examples) |
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return prompt_input, target |
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@dataclass(frozen=True) |
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class InputFeatures: |
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""" |
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A single set of features of data. |
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Property names are the same names as the corresponding inputs to a model. |
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""" |
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input_ids: List[List[int]] |
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attention_mask: Optional[List[List[int]]] |
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token_type_ids: Optional[List[List[int]]] |
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le_input_ids: List[List[int]] |
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le_attention_mask: Optional[List[List[int]]] |
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le_token_type_ids: Optional[List[List[int]]] |
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label: Optional[int] |
