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---
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.
|