--- title: bert-fa-base-uncased canonical_url: "https://www.modelscope.cn/models/SingGuo/bert-fa-base-uncased" md_url: "https://www.modelscope.cn/models/SingGuo/bert-fa-base-uncased.md" repository: SingGuo/bert-fa-base-uncased chinese_name: "波斯語bert" last_updated: 2026-01-18 license: apache-2.0 pipeline_tag: text-error-correction tasks: - text-error-correction model_type: - bert architectures: - BertForMaskedLM library_name: - transformer - tensorflow - pytorch frameworks: - Pytorch language: - fa downloads: 2 stars: 0 tags: - bert-fa - bert-persian - persian-lm --- # bert-fa-base-uncased > bert-fa-base-uncased - SingGuo 在 ModelScope 开源的模型。https://huggingface.co/HooshvareLab/bert-fa-base-uncased SingGuo/bert-fa-base-uncased 是 ModelScope 魔搭社区上的text-error-correction模型，采用 apache-2.0 许可。 - **Repository**: SingGuo/bert-fa-base-uncased - **License**: apache-2.0 - **Tasks**: text-error-correction - **Tags**: bert-fa, bert-persian, persian-lm - **Downloads**: 2 - **Stars**: 0 - **Last updated**: 2026-01-18 Source: https://www.modelscope.cn/models/SingGuo/bert-fa-base-uncased --- <<<<<<< HEAD --- language: fa tags: - bert-fa - bert-persian - persian-lm license: apache-2.0 --- # ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding 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](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. ## Introduction 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. Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515) ## Intended uses & limitations 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](https://huggingface.co/models?search=bert-fa) to look for fine-tuned versions on a task that interests you. ### How to use #### TensorFlow 2.0 ```python from transformers import AutoConfig, AutoTokenizer, TFAutoModel config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased") tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased") model = TFAutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased") text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد میتوانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است." tokenizer.tokenize(text) >>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.'] ``` #### Pytorch ```python from transformers import AutoConfig, AutoTokenizer, AutoModel config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased") tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased") model = AutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased") ``` ## Training ParsBERT trained on a massive amount of public corpora ([Persian Wikidumps](https://dumps.wikimedia.org/fawiki/), [MirasText](https://github.com/miras-tech/MirasText)) and six other manually crawled text data from a various type of websites ([BigBang Page](https://bigbangpage.com/) `scientific`, [Chetor](https://www.chetor.com/) `lifestyle`, [Eligasht](https://www.eligasht.com/Blog/) `itinerary`, [Digikala](https://www.digikala.com/mag/) `digital magazine`, [Ted Talks](https://www.ted.com/talks) `general conversational`, Books `novels, storybooks, short stories from old to the contemporary era`). As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format. ## Goals Objective goals during training are as below (after 300k steps). ``` bash ***** Eval results ***** global_step = 300000 loss = 1.4392426 masked_lm_accuracy = 0.6865794 masked_lm_loss = 1.4469004 next_sentence_accuracy = 1.0 next_sentence_loss = 6.534152e-05 ``` ## Derivative models ### Base Config #### ParsBERT v2.0 Model - [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) #### ParsBERT v2.0 Sentiment Analysis - [HooshvareLab/bert-fa-base-uncased-sentiment-digikala](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala) - [HooshvareLab/bert-fa-base-uncased-sentiment-snappfood](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood) - [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary) - [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi) #### ParsBERT v2.0 Text Classification - [HooshvareLab/bert-fa-base-uncased-clf-digimag](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-digimag) - [HooshvareLab/bert-fa-base-uncased-clf-persiannews](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-persiannews) #### ParsBERT v2.0 NER - [HooshvareLab/bert-fa-base-uncased-ner-peyma](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma) - [HooshvareLab/bert-fa-base-uncased-ner-arman](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-arman) ## Eval results ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling. ### Sentiment Analysis (SA) Task | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------:|:-----------:|:-----:|:-------------:| | Digikala User Comments | 81.72 | 81.74* | 80.74 | - | | SnappFood User Comments | 87.98 | 88.12* | 87.87 | - | | SentiPers (Multi Class) | 71.31* | 71.11 | - | 69.33 | | SentiPers (Binary Class) | 92.42* | 92.13 | - | 91.98 | ### Text Classification (TC) Task | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | |:-----------------:|:-----------:|:-----------:|:-----:| | Digikala Magazine | 93.65* | 93.59 | 90.72 | | Persian News | 97.44* | 97.19 | 95.79 | ### Named Entity Recognition (NER) Task | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |:-------:|:-----------:|:-----------:|:-----:|:----------:|:------------:|:--------:|:--------------:|:----------:| | PEYMA | 93.40* | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - | | ARMAN | 99.84* | 98.79 | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 | ### BibTeX entry and citation info Please cite in publications as the following: ```bibtex @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` ## Questions? Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. ======= # bert-fa-base-uncased #### 介绍 {**以下是 Gitee 平台说明，您可以替换此简介** Gitee 是 OSCHINA 推出的基于 Git 的代码托管平台（同时支持 SVN）。专为开发者提供稳定、高效、安全的云端软件开发协作平台 无论是个人、团队、或是企业，都能够用 Gitee 实现代码托管、项目管理、协作开发。企业项目请看 [https://gitee.com/enterprises](https://gitee.com/enterprises)} #### 软件架构 软件架构说明 #### 安装教程 1. xxxx 2. xxxx 3. xxxx #### 使用说明 1. xxxx 2. xxxx 3. xxxx #### 参与贡献 1. Fork 本仓库 2. 新建 Feat_xxx 分支 3. 提交代码 4. 新建 Pull Request #### 特技 1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md 2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) 3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目 5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) 6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) >>>>>>> 0209e3a607e5b6f34560217c003bed2e80eac5ce