L3Cube-MahaCorpus和MahaBERT：马拉地语单语语料库、马拉地语BERT语言模型和资源
L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources
摘要：
我们提出了L3立方体MahaCorpus马拉地语单语数据集从不同的互联网来源刮。我们扩展现有的马拉地语单语语料库与24.8M句子和289M标记。我们进一步提出，MahaBERT，MahaAlBERT和MahaRoBerta所有基于BERT的掩蔽语言模型，以及MahaFT，快速文本词嵌入，都是在具有752 M标记的完整马拉地语语料库上训练的。我们展示了这些资源在下游马拉地语情感分析、文本分类和命名实体识别（NER）任务上的有效性。我们还发布了MahaGPT，一个在马拉地语语料库上训练的生成马拉地语GPT模型。马拉地语是印度的一种流行语言，但仍然缺乏这些资源。这项工作是在为马拉地语建立开放资源方面向前迈出的一步。数据和模型可在https://github.com/l3cube-pune/MarathiNLP上获得。
英文摘要：
We present L3Cube-MahaCorpus a Marathi monolingual data set scraped from different internet sources. We expand the existing Marathi monolingual corpus with 24.8M sentences and 289M tokens. We further present, MahaBERT, MahaAlBERT, and MahaRoBerta all BERT-based masked language models, and MahaFT, the fast text word embeddings both trained on full Marathi corpus with 752M tokens. We show the effectiveness of these resources on downstream Marathi sentiment analysis, text classification, and named entity recognition (NER) tasks. We also release MahaGPT, a generative Marathi GPT model trained on Marathi corpus. Marathi is a popular language in India but still lacks these resources. This work is a step forward in building open resources for the Marathi language. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
