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ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned) This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.

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We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the ParsBERT repo for the latest information about previous and current models. ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than `3.9M` documents, `73M` sentences, and `1.3B` words. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.

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bert-fa-zwnj-base

HooshvareLab

The new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary. ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news).

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gpt2-fa

HooshvareLab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation
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