# mindnlp **Repository Path**: wangshiyu2024/mindnlp ## Basic Information - **Project Name**: mindnlp - **Description**: MindNLP is an open source NLP library based on MindSpore. - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 19 - **Created**: 2024-05-15 - **Last Updated**: 2024-05-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README #
MindNLP

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### News 📢 * 🔥 **Latest Features** * 🤗 Hugging *huggingface* ecosystem, we use **datasets** lib as default dataset loader to support mounts of useful datasets. * 📝 MindNLP supports NLP tasks such as *language model*, *machine translation*, *question answering*, *sentiment analysis*, *sequence labeling*, *summarization*, etc. You can access them through [examples](https://github.com/mindspore-lab/mindnlp/examples/). * 🚀 MindNLP currently supports industry-leading Large Language Models (LLMs), including **Llama**, **GLM**, **RWKV**, etc. For support related to large language models, including ***pre-training***, ***fine-tuning***, and **inference** demo examples, you can find them in the ["llm" directory](https://github.com/mindspore-lab/mindnlp/llm/). * 🤗 Pretrained models support ***huggingface transformers-like apis***, including **60+** models like **[BERT](https://github.com/mindspore-lab/mindnlp/mindnlp/transformers/models/bert)**, **[Roberta](https://github.com/mindspore-lab/mindnlp/mindnlp/transformers/models/roberta)**, **[GPT2](https://github.com/mindspore-lab/mindnlp/mindnlp/transformers/models/gpt2)**, **[T5](https://github.com/mindspore-lab/mindnlp/mindnlp/transformers/models/t5)**, etc. You can use them easily by following code snippet: ```python from mindnlp.transformers import AutoModel model = AutoModel.from_pretrained('bert-base-cased') ``` ### Installation #### Install from Pypi You can install the official version of MindNLP which uploaded to pypi. ```bash pip install mindnlp ``` #### Daily build You can download MindNLP daily wheel from [here](https://repo.mindspore.cn/mindspore-lab/mindnlp/newest/any/). #### Install from source To install MindNLP from source, please run: ```bash pip install git+https://github.com/mindspore-lab/mindnlp.git # or git clone https://github.com/mindspore-lab/mindnlp.git cd mindnlp bash scripts/build_and_reinstall.sh ``` #### Version Compatibility | MindNLP version | MindSpore version | Supported Python version | |-----------------|-------------------|--------------------------| | master | daily build | >=3.7.5, <=3.9 | | 0.1.1 | >=1.8.1, <=2.0.0 | >=3.7.5, <=3.9 | | 0.2.x | >=2.1.0 | >=3.8, <=3.9 | ### Introduction MindNLP is an open source NLP library based on MindSpore. It supports a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. The master branch works with **MindSpore master**. #### Major Features - **Comprehensive data processing**: Several classical NLP datasets are packaged into friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. - **Friendly NLP model toolset**: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. - **Easy-to-use engine**: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. ### Supported models Since there are too many supported models, please check [here](https://mindnlp.cqu.ai/supported_models) ### License This project is released under the [Apache 2.0 license](LICENSE). ### Feedbacks and Contact The dynamic version is still under development, if you find any issue or have an idea on new features, please don't hesitate to contact us via [Github Issues](https://github.com/mindspore-lab/mindnlp/issues). ### Acknowledgement MindSpore is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new semantic segmentation methods. ### Citation If you find this project useful in your research, please consider citing: ```latex @misc{mindnlp2022, title={{MindNLP}: Easy-to-use and high-performance NLP and LLM framework based on MindSpore}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} } ```