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We present FinISH, a [SRoBERTa](https://huggingface.co/sentence-transformers/nli-roberta-base-v2) base model fine-tuned on the [FIBO ontology](https://spec.edmcouncil.org/fibo/) dataset for domain-specific representation learning on the [**Semantic Search**](https://www.sbert.net/examples/applications/semantic-search/README.html) downstream task.
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## SRoBERTa Model Architecture
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Sentence-RoBERTa (SRoBERTa) is a modification of the pretrained RoBERTa network that uses siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with RoBERTa to about 5 seconds with SRoBERTa, while maintaining the accuracy from RoBERTa. SRoBERTa has been evaluated on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.
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We present FinISH, a [SRoBERTa](https://huggingface.co/sentence-transformers/nli-roberta-base-v2) base model fine-tuned on the [FIBO ontology](https://spec.edmcouncil.org/fibo/) dataset for domain-specific representation learning on the [**Semantic Search**](https://www.sbert.net/examples/applications/semantic-search/README.html) downstream task.
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The model is an implementation of the following paper: [Yseop at FinSim-3 Shared Task 2021: Specializing Financial Domain Learning with Phrase Representations](https://arxiv.org/abs/2108.09485)
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## SRoBERTa Model Architecture
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Sentence-RoBERTa (SRoBERTa) is a modification of the pretrained RoBERTa network that uses siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with RoBERTa to about 5 seconds with SRoBERTa, while maintaining the accuracy from RoBERTa. SRoBERTa has been evaluated on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.
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