Create README.md

README.md

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+---
+license: mit
+language:
+- en
+library_name: fasttext
+tags:
+- schema
+- word-embeddings
+- embeddings
+- fasttext
+- unsupervised-learning
+- tables
+- web-table
+- schema-data
+---
+# Pre-trained Web Table Embeddings
+
+The models here represent schema terms and instance data terms in a semantic vector space making them especially useful for representing schema and class information as well as for ML tasks on tabular text data.
+
+The code for executing and evaluating the models is located in the [table-embeddings Github repository](https://github.com/guenthermi/table-embeddings)
+
+## Quick Start
+
+You can install the table_embeddings package to encode text from tables by running the following commands:
+
+
+```bash
+pip install cython
+pip install pip install git+https://github.com/guenthermi/table-embeddings.git
+```
+
+After that you can encode text with the following Python snippet:
+
+```python
+from table_embeddings import TableEmbeddingModel
+model = TableEmbeddingModel.load_model('ddrg/web_table_embeddings_combo150')
+embedding = model.get_header_vector('headline')
+```
+
+## Model Types
+
+| Model Type | Description | Download-Links |
+| ---------- | ----------- | -------------- |
+| W-tax      | Model of relations between table header and table body | ([64dim](https://huggingface.co/ddrg/web_table_embeddings_tax64), [150dim](https://huggingface.co/ddrg/web_table_embeddings_tax150))
+| W-row      | Model of row-wise relations in tables | ([64dim](https://huggingface.co/ddrg/web_table_embeddings_row64), [150dim](https://huggingface.co/ddrg/web_table_embeddings_row150))
+| W-combo      | Model of row-wise relations and relations between table header and table body | ([64dim](https://huggingface.co/ddrg/web_table_embeddings_combo64), [150dim](https://huggingface.co/ddrg/web_table_embeddings_combo150))
+| W-plain      | Model of row-wise relations in tables without pre-processing | ([64dim](https://huggingface.co/ddrg/web_table_embeddings_plain64), [150dim](https://huggingface.co/ddrg/web_table_embeddings_plain150))
+
+## More Information
+
+For examples on how to use the models, you can take a look at the [Github repository](https://github.com/guenthermi/table-embeddings)
+
+More information can be found in the paper [Pre-Trained Web Table Embeddings for Table Discovery](https://dl.acm.org/doi/10.1145/3464509.3464892)
+```
+@inproceedings{gunther2021pre,
+  title={Pre-Trained Web Table Embeddings for Table Discovery},
+  author={G{\"u}nther, Michael and Thiele, Maik and Gonsior, Julius and Lehner, Wolfgang},
+  booktitle={Fourth Workshop in Exploiting AI Techniques for Data Management},
+  pages={24--31},
+  year={2021}
+}
+```