Boriscii commited on
Commit
65756b2
1 Parent(s): c80bfdc

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +2 -0
README.md CHANGED
@@ -35,5 +35,7 @@ Please refer to the Colab workbook or the blog post to learn more!
35
  Reach out to [[email protected]](mailto:[email protected]) if you'd like help with deploying the model in commerical setting.
36
 
37
  [1] Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020). LayoutLM: Pre-training of Text and Layout for Document Image Understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1192-1200).
 
38
  [2] Mathew, M., Karatzas, D., & Jawahar, C. V. (2021). DocVQA: A Dataset for VQA on Document Images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2200-2209).
 
39
  [3] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3982-3992).
 
35
  Reach out to [[email protected]](mailto:[email protected]) if you'd like help with deploying the model in commerical setting.
36
 
37
  [1] Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020). LayoutLM: Pre-training of Text and Layout for Document Image Understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1192-1200).
38
+
39
  [2] Mathew, M., Karatzas, D., & Jawahar, C. V. (2021). DocVQA: A Dataset for VQA on Document Images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2200-2209).
40
+
41
  [3] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3982-3992).