bert-base-spanish-wwm-cased

모델 소개

BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found here.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。