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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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tags: |
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- mteb |
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model-index: |
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- name: fin-mpnet-base-v0.1 |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/banking77 |
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name: MTEB Banking77Classification |
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config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
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- type: accuracy |
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value: 80.25 |
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- type: f1 |
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value: 79.64999520103544 |
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- task: |
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type: Retrieval |
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dataset: |
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type: fiqa |
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name: MTEB FiQA2018 |
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config: default |
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split: test |
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revision: None |
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metrics: |
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- type: map_at_1 |
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value: 37.747 |
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- type: map_at_10 |
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value: 72.223 |
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- type: map_at_100 |
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value: 73.802 |
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- type: map_at_1000 |
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value: 73.80499999999999 |
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- type: map_at_3 |
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value: 61.617999999999995 |
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- type: map_at_5 |
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value: 67.92200000000001 |
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- type: mrr_at_1 |
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value: 71.914 |
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- type: mrr_at_10 |
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value: 80.71000000000001 |
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- type: mrr_at_100 |
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value: 80.901 |
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- type: mrr_at_1000 |
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value: 80.901 |
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- type: mrr_at_3 |
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value: 78.935 |
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- type: mrr_at_5 |
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value: 80.193 |
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- type: ndcg_at_1 |
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value: 71.914 |
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- type: ndcg_at_10 |
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value: 79.912 |
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- type: ndcg_at_100 |
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value: 82.675 |
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- type: ndcg_at_1000 |
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value: 82.702 |
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- type: ndcg_at_3 |
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value: 73.252 |
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- type: ndcg_at_5 |
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value: 76.36 |
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- type: precision_at_1 |
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value: 71.914 |
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- type: precision_at_10 |
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value: 23.071 |
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- type: precision_at_100 |
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value: 2.62 |
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- type: precision_at_1000 |
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value: 0.263 |
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- type: precision_at_3 |
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value: 51.235 |
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- type: precision_at_5 |
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value: 38.117000000000004 |
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- type: recall_at_1 |
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value: 37.747 |
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- type: recall_at_10 |
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value: 91.346 |
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- type: recall_at_100 |
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value: 99.776 |
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- type: recall_at_1000 |
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value: 99.897 |
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- type: recall_at_3 |
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value: 68.691 |
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- type: recall_at_5 |
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value: 80.742 |
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--- |
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--- |
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v0.1 - full evaluation not complete |
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# {MODEL_NAME} |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('{MODEL_NAME}') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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Model was evaluated during training only on the new finance QA examples, as such only financial relevant benchmarks were evaluated on for v0.1 [FiQA-2018, BankingClassification77] |
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The model currently shows the highest FiQA Retrieval score on the test set, on the MTEB Leaderboard (https://huggingface.co/spaces/mteb/leaderboard) |
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The model will have likely suffered some performance on other benchmarks, i.e. BankingClassification77 has dropped from 81.6 to 80.25, this will be addressed for v0.2 and full evaluation on all sets will be run. |
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## Training |
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"sentence-transformers/all-mpnet-base-v2" was fine-tuned on 150k financial document QA examples using MNR Loss. |
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