roberta_qa_japanese

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roberta_qa_japanese

(Japanese caption : 日本語の (抽出型) 質問応答のモデル)

This model is a fine-tuned version of rinna/japanese-roberta-base (pre-trained RoBERTa model provided by rinna Co., Ltd.) trained for extractive question answering.

The model is fine-tuned on JaQuAD dataset provided by Skelter Labs, in which data is collected from Japanese Wikipedia articles and annotated by a human.

Intended uses

When running with a dedicated pipeline :

from transformers import pipeline

model_name = "tsmatz/roberta_qa_japanese"
qa_pipeline = pipeline(
    "question-answering",
    model=model_name,
    tokenizer=model_name)
result = qa_pipeline(
    question = "決勝トーナメントで日本に勝ったのはどこでしたか。",
    context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。",
    align_to_words = False,
)
print(result)

When manually running through forward pass :

import torch
import numpy as np
from transformers import AutoModelForQuestionAnswering, AutoTokenizer

model_name = "tsmatz/roberta_qa_japanese"
model = (AutoModelForQuestionAnswering
         .from_pretrained(model_name))
tokenizer = AutoTokenizer.from_pretrained(model_name)

def inference_answer(question, context):
    question = question
    context = context
    test_feature = tokenizer(
        question,
        context,
        max_length=318,
    )
    with torch.no_grad():
        outputs = model(torch.tensor([test_feature["input_ids"]]))
    start_logits = outputs.start_logits.cpu().numpy()
    end_logits = outputs.end_logits.cpu().numpy()
    answer_ids = test_feature["input_ids"][np.argmax(start_logits):np.argmax(end_logits)+1]
    return "".join(tokenizer.batch_decode(answer_ids))

question = "決勝トーナメントで日本に勝ったのはどこでしたか。"
context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。"
answer_pred = inference_answer(question, context)
print(answer_pred)

Training procedure

You can download the source code for fine-tuning from here.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 7e-05
• train_batch_size: 2
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 3

Training results

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu102
• Datasets 2.6.1
• Tokenizers 0.13.1