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Multimodal model for text and tabular data with HuggingFace transformers as building block for text data
| Date | Stars |
|---|---|
| 2026-07-24 | 618 |
| 2026-07-25 | 618 |
| 2026-07-28 | 618 |
| 2026-07-30 | 618 |
| 2026-07-31 | 619 |
| 2026-08-06 | 619 |
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# Multimodal Transformers | Transformers with Tabular Data -------------------------------------------------------------------------------- **[Documentation](https://multimodal-toolkit.readthedocs.io/en/latest/index.html)** | **[Colab Notebook](https://multimodal-toolkit.readthedocs.io/en/latest/notes/colab_example.html)** | **[Blog Post](https://medium.com/georgian-impact-blog/how-to-incorporate-tabular-data-with-huggingface-transformers-b70ac45fcfb4)** A toolkit for incorporating multimodal data on top of text data for classification and regression tasks. It uses HuggingFace transformers as the base model for text features. The toolkit adds a combining module that takes the outputs of the transformer in addition to categorical and numerical features to produce rich multimodal features for downstream classification/regression layers. Given a pretrained transformer, the parameters of the combining module and transformer are trained based on the supervised task. For a brief literature review, check out the accompanying [blog post](https://medium.com/georgian-impact-blog/how-to-incorporate-tabular-data-with-huggingface-transformers-b70ac45fcfb4) on Georgian's Impact Blog.  ## Installation The code was developed in Python 3.7 with PyTorch and Transformers 4.26.1. The multimodal specific code is in `multimodal_transformers` folder. ``` pip install multimodal-transformers ``` ## Supported Transformers The following Hugging Face Transformers are supported to handle tabular data. See the documentation [here](https://multimodal-toolkit.readthedocs.io/en/latest/modules/model.html#module-multimodal_transformers.model.tabular_transformers). * [BERT](https://huggingface.co/transformers/v3.1.0/model_doc/bert.html) from Devlin et al.: [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) (ACL 2019) * [ALBERT](https://huggingface.co/transformers/v3.1.0/model_doc/albert.html) from Lan et al.: [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations ](https://arxiv.org/abs/1909.11942) (ICLR 2020) * [DistilBERT](https://huggingface.co/transformers/v3.1.0/model_doc/distilbert.html) from Sanh et al.: [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) (NeurIPS 2019) * [RoBERTa](https://huggingface.co/transformers/v3.1.0/model_doc/roberta.html) from Liu et al.: [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) * [XLM](https://huggingface.co/transformers/v3.1.0/model_doc/xlm.html) from Lample et al.: [Cross-lingual Language Model Pretraining ](https://arxiv.org/abs/1901.07291) (NeurIPS 2019) * [XLNET](https://huggingface.co/transformers/v3.1.0/model_doc/xlnet.html) from Yang et al.: [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) (NeurIPS 2019) * [XLM-RoBERTa](https://huggingface.co/transformers/v3.1.0/model_doc/xlmroberta.html) from Conneau et al.: [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) (ACL 2020) ## Included Datasets This repository also includes two kaggle datasets which contain text data and rich tabular features * [Women's Clothing E-Commerce Reviews](https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews) for Recommendation Prediction (Classification) * [Melbourne Airbnb Open Data](https://www.kaggle.com/tylerx/melbourne-airbnb-open-data) for Price Prediction (Regression) * [PetFindermy Adoption Prediction](https://www.kaggle.com/c/petfinder-adoption-prediction) for Pet Adoption Speed Prediction (Multiclass Classification) ## Working Examples To quickly see these models in action on say one of the above datasets with preset configurations ``` $ python main.py ./datasets/Melbourne_Airbnb_Open_Data/train_config.json ``` Or if you prefe
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5d328bab1afc33a5, topic:transformer
matched fp:5d328bab1afc33a5, name:multimodal, desc:multimodal, readme:multimodal
matched fp:5d328bab1afc33a5, topic:natural-language-processing