huggingface / huggingface/transformers
Adding RelationExtraction head to layoutLMv2 and layoutXLM models
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- Python
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Description
# 🌟 New model head addition
Relation Extraction Head for LayoutLMv2/XLM
## Addition description
Hey all,
I've see a bunch of different requests across huggingface issues [[0]](https://github.com/huggingface/transformers/issues/14330), unilm issues [[0]](https://github.com/microsoft/unilm/issues/286)[[1]](https://github.com/microsoft/unilm/issues/465) and on @NielsRogge Transformer Tutorials issues [[0]](https://github.com/NielsRogge/Transformers-Tutorials/issues/6)[[1]](https://github.com/NielsRogge/Transformers-Tutorials/issues/39) about adding the relation extraction head from layoutlmv2 to the huggingface library. As the model is quite difficult to use in it's current state I was going to write my own layer ontop but I saw in this [issue](https://github.com/NielsRogge/Transformers-Tutorials/issues/39) that it may be a good idea to add it to transformers as a separate layoutlmv2/xlm head and thought it would be a good way to contribute back to a library I use so much.
I've gone ahead and added it under my own [branch](https://github.com/R0bk/transformers/tree/layoutlm-relation-extraction) and got it successfully working with the library. [Here](https://colab.research.google.com/drive/16wqA3oTUf7yzUKsSSZxiMf1443_ZO3wC?usp=sharing) is a colab using my branch of transformers if you want to test it yourself.
Before I add tests/ write more docs I just wanted to post here first to see if there's interest in potentially merging this in. If there is interest I have a few questions that it would be helpful to get some info on to ensure that I've correctly done the integration.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the layoutlm-relation-extraction branch and its linked Colab to understand the proposed LayoutLMv2/XLM relation-extraction head. The issue explicitly leaves tests and documentation to be added; completion would require maintainer agreement on integration, followed by those tests and docs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100