deepke/_sources/start.rst.txt

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Start
=====
Model Framework
---------------
.. image:: ./_static/architectures.png
DeepKE contains three modules for named entity recognition, relation extraction and attribute extraction, the three tasks respectively.
Each module has its own submodules. For example, there are standard, document-level and few-shot submodules in the attribute extraction modular.
Each submodule compose of three parts: a collection of tools, which can function as tokenizer, dataloader, preprocessor and the like, a encoder and a part for training and prediction
Dataset
-------
We use the following datasets in our experiments:
+--------------------------+-----------+------------------+----------+------------+
| Task | Settings | Corpus | Language | Model |
+==========================+===========+==================+==========+============+
| | | CoNLL-2003 | English | |
| | Standard +------------------+----------+ BERT |
| | | People's Daily | Chinese | |
| +-----------+------------------+----------+------------+
| | | CoNLL-2003 | | |
| | +------------------+ | |
| Name Entity Recognition | | MIT Movie | | |
| | Few-shot +------------------+ English | LightNER |
| | | MIT Restaurant | | |
| | +------------------+ | |
| | | ATIS | | |
+--------------------------+-----------+------------------+----------+------------+
| | | | | CNN |
| | | | +------------+
| | | | | RNN |
| | | | +------------+
| | | | | Capsule |
| | Standard | DuIE | Chinese +------------+
| | | | | GCN |
| | | | +------------+
| | | | | Transformer|
| | | | +------------+
| | | | | BERT |
| +-----------+------------------+----------+------------+
| Relation Extraction | | SEMEVAL(8-shot) | | |
| | +------------------+ | |
| | | SEMEVAL(16-shot) | | |
| | Few-shot +------------------+ English | KnowPrompt |
| | | SEMEVAL(32-shot) | | |
| | +------------------+ | |
| | | SEMEVAL(Full) | | |
| +-----------+------------------+----------+------------+
| | | DocRED | | |
| | +------------------+ | |
| | Document | CDR | English | DocuNet |
| | +------------------+ | |
| | | GDA | | |
+--------------------------+-----------+------------------+----------+------------+
| | | | | CNN |
| | | | +------------+
| | | | | RNN |
| | | | +------------+
| | |Triplet Extraction| | Capsule |
| Attribute Extraction | Standard |Dataset | Chinese +------------+
| | | | | GCN |
| | | | +------------+
| | | | | Transformer|
| | | | +------------+
| | | | | BERT |
+--------------------------+-----------+------------------+----------+------------+
Get Start
---------
If you want to use our code , you can do as follow:
.. code-block:: python
git clone https://github.com/zjunlp/DeepKE.git
cd DeepKE