Model reference · open weights
lilt-infoxlm is an open-weight embedding model from SCUT-DLVCLab. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | SCUT-DLVCLab |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 284M |
| Context | 514 tokens |
| Runs with | transformers |
| Released | 2022-10-10 |
| Popularity | 1k downloads / month |
| Licence | Open weights |
About
Language-Independent Layout Transformer - InfoXLM model by stitching a pre-trained InfoXLM and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Wang et al. and first released in this repository.
Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team.
The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language.
The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the model hub to look for fine-tuned versions on a task that interests you.
For code examples, we refer to the documentation.
@misc{https://doi.org/10.48550/arxiv.2202.13669,
doi = {10.48550/ARXIV.2202.13669},
url = {https://arxiv.org/abs/2202.13669},
author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys lilt-infoxlm for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (lilt-infoxlm below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"lilt-infoxlm","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.