Instructions to use nlpaueb/sec-bert-shape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-shape with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-shape")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-shape") - Notebooks
- Google Colab
- Kaggle
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## Publication
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The model has been officially released with the following article:<br>
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**"FiNER: Financial Numeric Entity Recognition for XBRL Tagging"**<br>
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Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br>
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In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022
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## Publication
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The model has been officially released with the following article:<br>
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**["FiNER: Financial Numeric Entity Recognition for XBRL Tagging"](https://arxiv.org/abs/2203.06482)**<br>
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Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br>
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In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022
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