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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Ruukki Group calculates that it has lost EUR 4mn in the failed project .
- text: The Tecnomen Convergent Charging solution includes functionality for prepaid
and post-paid billing , charging and rating of voice calls , video calls , raw
data traffic and any type of content services in both mobile and fixed networks
.
- text: The combined value of the planned investments is about EUR 30mn .
- text: The Diameter Protocol is developed according to the standards IETF RFC 3588
and IETF RFC 3539 .
- text: Below are unaudited consolidated results for Aspocomp Group under IFRS reporting
standards .
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9426048565121413
name: Accuracy
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'</li><li>'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'</li><li>"`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."</li></ul> |
| neutral | <ul><li>'Their names have not yet been released .'</li><li>'The contract includes design , construction , delivery of equipment , installation and commissioning .'</li><li>"Tieto 's service is also used to send , process and receive materials related to absentee voting ."</li></ul> |
| negative | <ul><li>'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'</li><li>'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'</li><li>'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9426 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5-SetFit-FSA")
# Run inference
preds = model("The combined value of the planned investments is about EUR 30mn .")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 22.4020 | 60 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 266 |
| neutral | 1142 |
| positive | 403 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.2832 | - |
| 0.0221 | 50 | 0.209 | - |
| 0.0442 | 100 | 0.1899 | - |
| 0.0663 | 150 | 0.1399 | - |
| 0.0883 | 200 | 0.1274 | - |
| 0.1104 | 250 | 0.0586 | - |
| 0.1325 | 300 | 0.0756 | - |
| 0.1546 | 350 | 0.0777 | - |
| 0.1767 | 400 | 0.0684 | - |
| 0.1988 | 450 | 0.0311 | - |
| 0.2208 | 500 | 0.0102 | - |
| 0.2429 | 550 | 0.052 | - |
| 0.2650 | 600 | 0.0149 | - |
| 0.2871 | 650 | 0.1042 | - |
| 0.3092 | 700 | 0.061 | - |
| 0.3313 | 750 | 0.0083 | - |
| 0.3534 | 800 | 0.0036 | - |
| 0.3754 | 850 | 0.002 | - |
| 0.3975 | 900 | 0.0598 | - |
| 0.4196 | 950 | 0.0036 | - |
| 0.4417 | 1000 | 0.0027 | - |
| 0.4638 | 1050 | 0.0617 | - |
| 0.4859 | 1100 | 0.0015 | - |
| 0.5080 | 1150 | 0.0022 | - |
| 0.5300 | 1200 | 0.0016 | - |
| 0.5521 | 1250 | 0.0009 | - |
| 0.5742 | 1300 | 0.0013 | - |
| 0.5963 | 1350 | 0.0009 | - |
| 0.6184 | 1400 | 0.0015 | - |
| 0.6405 | 1450 | 0.0018 | - |
| 0.6625 | 1500 | 0.0015 | - |
| 0.6846 | 1550 | 0.0018 | - |
| 0.7067 | 1600 | 0.0016 | - |
| 0.7288 | 1650 | 0.0022 | - |
| 0.7509 | 1700 | 0.0013 | - |
| 0.7730 | 1750 | 0.0108 | - |
| 0.7951 | 1800 | 0.0016 | - |
| 0.8171 | 1850 | 0.0021 | - |
| 0.8392 | 1900 | 0.002 | - |
| 0.8613 | 1950 | 0.0015 | - |
| 0.8834 | 2000 | 0.0016 | - |
| 0.9055 | 2050 | 0.0028 | - |
| 0.9276 | 2100 | 0.0013 | - |
| 0.9496 | 2150 | 0.0019 | - |
| 0.9717 | 2200 | 0.0075 | - |
| 0.9938 | 2250 | 0.0015 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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