Easy Text Classification with Deep Learning Models

LinkTransformer allows a simple way to access the power of Transformer language models in a simple API to classify rows of a data frame in one line of code! It supports both OpenAI and Hugging Face models for the task. We recommend binary classification for beginners, but also support multi-class classification and also support weighted training loss to allow for unbalanced training classes. All you need is a data frame of text you want to classify (row-by-row) to use a pre-trained model for classification (like ChatGPT) or a data frame of Text and Class “labels” to train your own models within minutes - that too within a colab session! Check out this Tutorial!

▲ Classify Text using Transformer language models as if it were a feature of Pandas!

All models on Hugging Face and Open AI embeddings are supported

LinkTransformer supports all models on the Hugging Face Hub (we recommend those trained for sentence similarity tasks) and OpenAI Embedding models. To accommodate heterogeneous use cases, LinkTransformer has also trained our own collection of open-source language models on different datasets for record linkage. A guide to selecting models is included in our Introductory Notebook. Currently, there are over 20 models trained on diverse datasets which can be downloaded from Hugging Face and are discoverable at this link.

For base models for various languages, we recommend the following models:

Easy to Train Your Own Record Linkage models with one line of code

LinkTransformer also comes with full support for customized record linkage model training on your own dataset. This enables you to achieve optimal prediction accuracy on your own dataset and can simplify your pipeline. You can train your own or fine-tune any pretrained model from Hugging Face! Learn more details at our repo.

See Also:

• Tutorial

Easy to Train Your own Transformer models for Text Classification with one line of code - for any base model on HuggingFace!

LinkTransformer also supports training classification models - just with a single line of code with a data frame of text and labels!

See Also:

• Tutorial

Sharing Trained models on the HuggingFace Hub

LinkTransformer aims to create a community for deep record linkage, as deep learning has currently made few inroads into record linkage. Making open-source, pre-trained models available is particularly important for democratizing the benefits of deep learning. LinkTransformer is well-integrated with the hugging face hub, and all models can be uploaded/downloaded with a single line of code. Our own models are shared on the hub, and we encourage the community to share their models as well. If you use the library to upload LinkTransformer models, they automatically get a “tag” on Hugging Face and become discoverable on this link. By streamlining the distribution of record linkage models, as well as promoting the reusability of these deep learning-based pipelines, Linktransformer can transform how social scientists build their record linkage workflows.

• Introducing LinkTransformers
• Train your own LinkTransformer model
• Feature Round-up for wrangling using Linktransformer
• Classify text with one line of code (and train your own models!)

• LinkTransformer API Preview

Coming Soon!

• Hard-negative mining for efficient training
• FAISS GPU, cuDF, cuML and cuGraph integration
• Convenience wrapper to use our models (trained on UN products and Wikidata)
• Integration of other modalities in this framework (Vision/Multimodal models) Tell us what other features you would like - raise an issue on our repo or help us develop it!

Team

• Melissa Dell (Co-author)
• Abhishek Arora (Co-author)
• Sam Caronna Jones (Research Assistant and Package Maintainer)

Join us!

Collaborate with us on the package - check out our repo and contribute!