---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- financial
- fiqa
- finance
- retrieval
- rag
- esg
- fixed-income
- equity
model-index:
- name: fin-mpnet-base-v0.1
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 29.128
    - type: f1
      value: 28.657401543151707
  - task:
      type: Retrieval
    dataset:
      type: arguana
      name: MTEB ArguAna
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 24.111
    - type: map_at_10
      value: 40.083
    - type: map_at_100
      value: 41.201
    - type: map_at_1000
      value: 41.215
    - type: map_at_3
      value: 35.325
    - type: map_at_5
      value: 37.796
    - type: mrr_at_1
      value: 25.036
    - type: mrr_at_10
      value: 40.436
    - type: mrr_at_100
      value: 41.554
    - type: mrr_at_1000
      value: 41.568
    - type: mrr_at_3
      value: 35.644999999999996
    - type: mrr_at_5
      value: 38.141000000000005
    - type: ndcg_at_1
      value: 24.111
    - type: ndcg_at_10
      value: 49.112
    - type: ndcg_at_100
      value: 53.669999999999995
    - type: ndcg_at_1000
      value: 53.944
    - type: ndcg_at_3
      value: 39.035
    - type: ndcg_at_5
      value: 43.503
    - type: precision_at_1
      value: 24.111
    - type: precision_at_10
      value: 7.817
    - type: precision_at_100
      value: 0.976
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 16.596
    - type: precision_at_5
      value: 12.134
    - type: recall_at_1
      value: 24.111
    - type: recall_at_10
      value: 78.16499999999999
    - type: recall_at_100
      value: 97.58200000000001
    - type: recall_at_1000
      value: 99.57300000000001
    - type: recall_at_3
      value: 49.787
    - type: recall_at_5
      value: 60.669
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 80.25
    - type: f1
      value: 79.64999520103544
  - task:
      type: Retrieval
    dataset:
      type: fiqa
      name: MTEB FiQA2018
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 37.747
    - type: map_at_10
      value: 72.223
    - type: map_at_100
      value: 73.802
    - type: map_at_1000
      value: 73.80499999999999
    - type: map_at_3
      value: 61.617999999999995
    - type: map_at_5
      value: 67.92200000000001
    - type: mrr_at_1
      value: 71.914
    - type: mrr_at_10
      value: 80.71000000000001
    - type: mrr_at_100
      value: 80.901
    - type: mrr_at_1000
      value: 80.901
    - type: mrr_at_3
      value: 78.935
    - type: mrr_at_5
      value: 80.193
    - type: ndcg_at_1
      value: 71.914
    - type: ndcg_at_10
      value: 79.912
    - type: ndcg_at_100
      value: 82.675
    - type: ndcg_at_1000
      value: 82.702
    - type: ndcg_at_3
      value: 73.252
    - type: ndcg_at_5
      value: 76.36
    - type: precision_at_1
      value: 71.914
    - type: precision_at_10
      value: 23.071
    - type: precision_at_100
      value: 2.62
    - type: precision_at_1000
      value: 0.263
    - type: precision_at_3
      value: 51.235
    - type: precision_at_5
      value: 38.117000000000004
    - type: recall_at_1
      value: 37.747
    - type: recall_at_10
      value: 91.346
    - type: recall_at_100
      value: 99.776
    - type: recall_at_1000
      value: 99.897
    - type: recall_at_3
      value: 68.691
    - type: recall_at_5
      value: 80.742
  - task:
      type: Retrieval
    dataset:
      type: nfcorpus
      name: MTEB NFCorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.124
    - type: map_at_10
      value: 10.206999999999999
    - type: map_at_100
      value: 13.181000000000001
    - type: map_at_1000
      value: 14.568
    - type: map_at_3
      value: 7.2620000000000005
    - type: map_at_5
      value: 8.622
    - type: mrr_at_1
      value: 39.009
    - type: mrr_at_10
      value: 48.144
    - type: mrr_at_100
      value: 48.746
    - type: mrr_at_1000
      value: 48.789
    - type: mrr_at_3
      value: 45.356
    - type: mrr_at_5
      value: 47.152
    - type: ndcg_at_1
      value: 36.533
    - type: ndcg_at_10
      value: 29.643000000000004
    - type: ndcg_at_100
      value: 27.893
    - type: ndcg_at_1000
      value: 37.307
    - type: ndcg_at_3
      value: 33.357
    - type: ndcg_at_5
      value: 32.25
    - type: precision_at_1
      value: 38.7
    - type: precision_at_10
      value: 22.941
    - type: precision_at_100
      value: 7.303
    - type: precision_at_1000
      value: 2.028
    - type: precision_at_3
      value: 31.889
    - type: precision_at_5
      value: 29.04
    - type: recall_at_1
      value: 4.124
    - type: recall_at_10
      value: 14.443
    - type: recall_at_100
      value: 29.765000000000004
    - type: recall_at_1000
      value: 63.074
    - type: recall_at_3
      value: 8.516
    - type: recall_at_5
      value: 10.979
  - task:
      type: Retrieval
    dataset:
      type: scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 49.010999999999996
    - type: map_at_10
      value: 60.094
    - type: map_at_100
      value: 60.79900000000001
    - type: map_at_1000
      value: 60.828
    - type: map_at_3
      value: 57.175
    - type: map_at_5
      value: 58.748
    - type: mrr_at_1
      value: 51.666999999999994
    - type: mrr_at_10
      value: 61.312
    - type: mrr_at_100
      value: 61.821000000000005
    - type: mrr_at_1000
      value: 61.85000000000001
    - type: mrr_at_3
      value: 59.0
    - type: mrr_at_5
      value: 60.199999999999996
    - type: ndcg_at_1
      value: 51.666999999999994
    - type: ndcg_at_10
      value: 65.402
    - type: ndcg_at_100
      value: 68.377
    - type: ndcg_at_1000
      value: 69.094
    - type: ndcg_at_3
      value: 60.153999999999996
    - type: ndcg_at_5
      value: 62.455000000000005
    - type: precision_at_1
      value: 51.666999999999994
    - type: precision_at_10
      value: 9.067
    - type: precision_at_100
      value: 1.0670000000000002
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 24.0
    - type: precision_at_5
      value: 15.933
    - type: recall_at_1
      value: 49.010999999999996
    - type: recall_at_10
      value: 80.511
    - type: recall_at_100
      value: 94.0
    - type: recall_at_1000
      value: 99.5
    - type: recall_at_3
      value: 66.2
    - type: recall_at_5
      value: 71.944
---

full evaluation not complete

# Fin-MPNET-Base (v0.1)

This is a fine-tuned [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.

This model aims to be very strong on Financial Document Retrieval Tasks, while trying to maintain as much generalized performance as possible.


|                   | FiQA  | SciFact | AmazonReviews | OnlineBankingIntent | ArguAna |
|-------------------|-------|---------|---------------|---------------------|---------|
| fin-mpnet-base    | 79.91 | 65.40   | 29.12         | 80.25               | 49.11   |
| all-mpnet-base-v2 | 49.96 | 65.57   | 31.92         | 81.86               | 46.52   |
| previous SoTA     | 56.59 |   -     |   -           |   -                 |   -     |

v0.1 shows SoTA results on FiQA Test set while other non-financial benchmarks only drop a few small % and improvement in others.


## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('mukaj/fin-mpnet-base')
embeddings = model.encode(sentences)
print(embeddings)
```


## Evaluation Results

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]

The model currently shows the highest FiQA Retrieval score on the test set, on the MTEB Leaderboard (https://huggingface.co/spaces/mteb/leaderboard)

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.


## Training

"sentence-transformers/all-mpnet-base-v2" was fine-tuned on 150k+ financial document QA examples using MNR Loss.