Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
mteb
financial
fiqa
finance
retrieval
rag
esg
fixed-income
equity
Eval Results (legacy)
text-embeddings-inference
Instructions to use mukaj/fin-mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mukaj/fin-mpnet-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mukaj/fin-mpnet-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Julian Mukaj commited on
Commit ·
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Parent(s): 5560c81
Initial commit + new eval table
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README.md
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@@ -329,13 +329,13 @@ This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model: It ma
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This model aims to be very strong on Financial Document Retrieval Tasks, while trying to maintain as much generalized performance as possible.
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| | FiQA | SciFact | AmazonReviews | OnlineBankingIntent |
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| fin-mpnet-base | 79.91 | 65.40 | 29.12 | 80.25 |
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| all-mpnet-base-v2 | 49.96 | 65.57 | 31.92 | 81.86 |
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| previous SoTA | 56.59 |
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v0.1 shows SoTA results on FiQA Test set while other non-financial benchmarks only drop a few small %.
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## Usage (Sentence-Transformers)
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This model aims to be very strong on Financial Document Retrieval Tasks, while trying to maintain as much generalized performance as possible.
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| | FiQA | SciFact | AmazonReviews | OnlineBankingIntent | ArguAna |
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| fin-mpnet-base | 79.91 | 65.40 | 29.12 | 80.25 | 49.11 |
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| all-mpnet-base-v2 | 49.96 | 65.57 | 31.92 | 81.86 | 46.52 |
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| previous SoTA | 56.59 | - | - | - | - |
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v0.1 shows SoTA results on FiQA Test set while other non-financial benchmarks only drop a few small % and improvement in others.
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## Usage (Sentence-Transformers)
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