# graph4nlp **Repository Path**: harygo2/graph4nlp ## Basic Information - **Project Name**: graph4nlp - **Description**: Graph4nlp is the library for the easy use of Graph Neural Networks for NLP - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-10-28 - **Last Updated**: 2021-10-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

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Figure: Graph4NLP Overall Architecture

## new Graph4NLP news **06/05/2021:** The **v0.4.1 release**. Try it out! ## Quick tour ***Graph4nlp*** aims to make it incredibly easy to use GNNs in NLP tasks (check out [Graph4NLP Documentation](http://saizhuo.wang/g4nlp/index.html)). Here is an example of how to use the [*Graph2seq*](http://saizhuo.wang/g4nlp/index.html) model (widely used in machine translation, question answering, semantic parsing, and various other NLP tasks that can be abstracted as graph-to-sequence problem and has shown superior performance). We also offer other high-level model APIs such as graph-to-tree models. If you are interested in DLG4NLP related research problems, you are very welcome to use our library and refer to our [graph4nlp survey](to_be_add). ```python from graph4nlp.pytorch.datasets.jobs import JobsDataset from graph4nlp.pytorch.modules.graph_construction.dependency_graph_construction import DependencyBasedGraphConstruction from graph4nlp.pytorch.modules.config import get_basic_args from graph4nlp.pytorch.models.graph2seq import Graph2Seq from graph4nlp.pytorch.modules.utils.config_utils import update_values, get_yaml_config # build dataset jobs_dataset = JobsDataset(root_dir='graph4nlp/pytorch/test/dataset/jobs', topology_builder=DependencyBasedGraphConstruction, topology_subdir='DependencyGraph') # You should run stanfordcorenlp at background vocab_model = jobs_dataset.vocab_model # build model user_args = get_yaml_config("examples/pytorch/semantic_parsing/graph2seq/config/dependency_gcn_bi_sep_demo.yaml") args = get_basic_args(graph_construction_name="node_emb", graph_embedding_name="gat", decoder_name="stdrnn") update_values(to_args=args, from_args_list=[user_args]) graph2seq = Graph2Seq.from_args(args, vocab_model) # calculation batch_data = JobsDataset.collate_fn(jobs_dataset.train[0:12]) scores = graph2seq(batch_data["graph_data"], batch_data["tgt_seq"]) # [Batch_size, seq_len, Vocab_size] ``` ## Overview Our Graph4NLP computing flow is shown as below.

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## Graph4NLP Models and Applications ### Graph4NLP models - [Graph2Seq](https://github.com/graph4ai/graph4nlp/blob/master/graph4nlp/pytorch/models/graph2seq.py): a general end-to-end neural encoder-decoder model that maps an input graph to a sequence of tokens. - [Graph2Tree](https://github.com/graph4ai/graph4nlp/blob/master/graph4nlp/pytorch/models/graph2tree.py): a general end-to-end neural encoder-decoder model that maps an input graph to a tree structure. ### Graph4NLP applications We provide a comprehensive collection of NLP applications, together with detailed examples as follows: - [Text classification](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/text_classification): to give the sentence or document an appropriate label. - [Semantic parsing](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/semantic_parsing): to translate natural language into a machine-interpretable formal meaning representation. - [Neural machine translation](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/nmt): to translate a sentence in a source language to a different target language. - [summarization](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/summarization): to generate a shorter version of input texts which could preserve major meaning. - [KG completion](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/kg_completion): to predict missing relations between two existing entities in konwledge graphs. - [Math word problem solving](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/math_word_problem): to automatically solve mathematical exercises that provide background information about a problem in easy-to-understand language. - [Name entity recognition](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/name_entity_recognition): to tag entities in input texts with their corresponding type. - [Question generation](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/question_generation): to generate an valid and fluent question based on the given passage and target answer (optional). ## Performance | Task | Dataset | GNN Model | Graph construction | Evaluation | Performance | |----------------------------|:--------------------------------:|:-------------------:|----------------------------------------------|--------------------|:-----------------------------:| | Text classification | TRECT
CAirline
CNSST
| GAT | Dependency | Accuracy | 0.948
0.769
0.538
| | Semantic Parsing | JOBS | SAGE | Constituency | Execution accuracy | 0.936 | | Question generation | SQuAD | GGNN | Dependency | BLEU-4 | 0.15175 | | Machine translation | IWSLT14 | GCN | Dynamic | BLEU-4 | 0.3212 | | Summarization | CNN(30k) | GCN | Dependency | ROUGE-1 | 26.4 | | Knowledge graph completion | Kinship | GCN | Dependency | MRR | 82.4 | | Math word problem | MAWPS
MATHQA | SAGE | Dynamic | Solution accuracy
Exact match | 76.4
61.07 | ## Installation Currently, users can install Graph4NLP via **pip** or **source code**. Graph4NLP supports the following OSes: - Linux-based systems (tested on Ubuntu 18.04 and later) - macOS (only CPU version) - Windows 10 (only support pytorch >= 1.8) ### Installation via pip (binaries) We provide pip wheels for all major OS/PyTorch/CUDA combinations. Note that we highly recommend `Windows` users refer to `Installation via source code` due to compatibility. #### Ensure that at least PyTorch (>=1.6.0) is installed: Note that `>=1.6.0` is ok. ``` bash $ python -c "import torch; print(torch.__version__)" >>> 1.6.0 ``` #### Find the CUDA version PyTorch was installed with (for GPU users): ```bash $ python -c "import torch; print(torch.version.cuda)" >>> 10.2 ``` #### Install the relevant dependencies: `torchtext` is needed since Graph4NLP relies on it to implement embeddings. Please pay attention to the PyTorch requirements before installing `torchtext` with the following script! For detailed version matching please refer [here](https://pypi.org/project/torchtext/). ``` bash pip install torchtext # >=0.7.0 ``` #### Install Graph4NLP ```bash pip install graph4nlp${CUDA} ``` where `${CUDA}` should be replaced by the specific CUDA version (`none` (CPU version), `"-cu92"`, `"-cu101"`, `"-cu102"`, `"-cu110"`). The following table shows the concrete command lines. For CUDA 11.1 users, please refer to `Installation via source code`. | Platform | Command | | --------- | ----------------------------- | | CPU | `pip install graph4nlp` | | CUDA 9.2 | `pip install graph4nlp-cu92` | | CUDA 10.1 | `pip install graph4nlp-cu101` | | CUDA 10.2 | `pip install graph4nlp-cu102` | | CUDA 11.0 | `pip install graph4nlp-cu110` | ### Installation via source code #### Ensure that at least PyTorch (>=1.6.0) is installed: Note that `>=1.6.0` is ok. ``` bash $ python -c "import torch; print(torch.__version__)" >>> 1.6.0 ``` #### Find the CUDA version PyTorch was installed with (for GPU users): ```bash $ python -c "import torch; print(torch.version.cuda)" >>> 10.2 ``` #### Install the relevant dependencies: `torchtext` is needed since Graph4NLP relies on it to implement embeddings. Please pay attention to the PyTorch requirements before installing `torchtext` with the following script! For detailed version matching please refer [here](https://pypi.org/project/torchtext/). ``` bash pip install torchtext # >=0.7.0 ``` #### Download the source code of `Graph4NLP` from Github: ```bash git clone https://github.com/graph4ai/graph4nlp.git cd graph4nlp ``` #### Configure the CUDA version Then run `./configure` (or `./configure.bat` if you are using Windows 10) to config your installation. The configuration program will ask you to specify your CUDA version. If you do not have a GPU, please type 'cpu'. ```bash ./configure ``` #### Install the relevant packages: Finally, install the package: ```shell python setup.py install ``` ## Major Releases | Releases | Date | Features | | -------- | ---------- | ------------------------------------------------------------ | | v0.4.1 | 2021-06-05 | - Support the whole pipeline of Graph4NLP
- GraphData and Dataset support | ## New to Deep Learning on Graphs for NLP? If you want to learn more on applying Deep Learning on Graphs techniques to NLP tasks, you can refer to our survey paper which provides an overview of this existing research direction. If you want detailed reference to our library, please refer to our docs. - Documentation: [Docs](http://saizhuo.wang/g4nlp/index.html) - Graph4NLP Survey: [Graph4nlp survey]() - Graph4NLP Tutorials: [Graph4NLP-NAACL'21](https://www.aclweb.org/anthology/2021.naacl-tutorials.3.pdf)(Slides: [google drive](https://drive.google.com/file/d/1_7cPySt9Pzfd6MaqNihD4FkKI0qzf-s4/view?usp=sharing), [baidu netdisk](https://pan.baidu.com/s/1QeWedhMgIBjBpK0EcgXYvQ)(drs1)) - Graph4NLP Literature Review: [Literature Lists](https://github.com/graph4ai/graph4nlp_literature ) - Graph4NLP Workshops : [Workshops](https://deep-learning-graphs.bitbucket.io/dlg-kdd21/index.html) ## Contributing Please let us know if you encounter a bug or have any suggestions by filing an issue. We welcome all contributions from bug fixes to new features and extensions. We expect all contributions discussed in the issue tracker and going through PRs. ## Citation If you found this code useful, please consider citing the following paper (please stay tuned!). ## Team Graph4AI Team. Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Saizhuo Wang ## License Graph4NLP uses Apache License 2.0.