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---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What’s the total number of orders placed by each customer?
- text: I like to read books and listen to music in my free time. How about you?
- text: Get company-wise intangible asset ratio.
- text: Show me data_asset_001_ta by product.
- text: Show me average asset value.
inference: true
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: 0.9915254237288136
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:** 7 classes
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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 |
|:-------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Aggregation | <ul><li>'Please show med CostVariance_Actual_vs_Forecast.'</li><li>'Get me data_asset_001_kpm group by metrics.'</li><li>'Provide data_asset_kpi_cf group by quarter.'</li></ul> |
| Tablejoin | <ul><li>'Join data_asset_kpi_cf with data_asset_001_kpm tables.'</li><li>'Could you link the Products and Orders tables to track sales trends for different product categories?'</li><li>'Can I have a merge of income statement and key performance metrics tables?'</li></ul> |
| Lookup | <ul><li>"Filter by the 'Sales' department and show me the employees."</li><li>"Filter by the 'Toys' category and get me the product names."</li><li>'Can you get me the products with a price above 100?'</li></ul> |
| Rejection | <ul><li>"Let's avoid generating additional reports."</li><li>"I'd rather not filter this dataset."</li><li>"I'd prefer not to apply any filters."</li></ul> |
| Lookup_1 | <ul><li>'Show me key income statement metrics.'</li><li>'can I have kpm table'</li><li>'Retrieve data_asset_kpi_ma_product records.'</li></ul> |
| Generalreply | <ul><li>"Hey! It's going pretty well, thanks for asking. How about yours?"</li><li>'Not much, just taking it one day at a time. How about you?'</li><li>"'What is your favorite quote?'"</li></ul> |
| Viewtables | <ul><li>'What are the table names that relate to customer service in the starhub_data_asset database?'</li><li>'What tables are available in the starhub_data_asset database that can be joined to track user behavior?'</li><li>'What are the tables that are available for analysis in the starhub_data_asset database?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9915 |
## 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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10-3rd")
# Run inference
preds = model("Show me average asset value.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 8.7839 | 62 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| Tablejoin | 127 |
| Rejection | 76 |
| Aggregation | 281 |
| Lookup | 59 |
| Generalreply | 71 |
| Viewtables | 75 |
| Lookup_1 | 158 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: 2450
- sampling_strategy: oversampling
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2317 | - |
| 0.0025 | 50 | 0.2478 | - |
| 0.0050 | 100 | 0.2213 | - |
| 0.0075 | 150 | 0.0779 | - |
| 0.0100 | 200 | 0.1089 | - |
| 0.0125 | 250 | 0.0372 | - |
| 0.0149 | 300 | 0.0219 | - |
| 0.0174 | 350 | 0.0344 | - |
| 0.0199 | 400 | 0.012 | - |
| 0.0224 | 450 | 0.0049 | - |
| 0.0249 | 500 | 0.0041 | - |
| 0.0274 | 550 | 0.0083 | - |
| 0.0299 | 600 | 0.0057 | - |
| 0.0324 | 650 | 0.0047 | - |
| 0.0349 | 700 | 0.0022 | - |
| 0.0374 | 750 | 0.0015 | - |
| 0.0399 | 800 | 0.0032 | - |
| 0.0423 | 850 | 0.002 | - |
| 0.0448 | 900 | 0.0028 | - |
| 0.0473 | 950 | 0.0017 | - |
| 0.0498 | 1000 | 0.0017 | - |
| 0.0523 | 1050 | 0.0027 | - |
| 0.0548 | 1100 | 0.0022 | - |
| 0.0573 | 1150 | 0.0018 | - |
| 0.0598 | 1200 | 0.001 | - |
| 0.0623 | 1250 | 0.002 | - |
| 0.0648 | 1300 | 0.001 | - |
| 0.0673 | 1350 | 0.0013 | - |
| 0.0697 | 1400 | 0.0012 | - |
| 0.0722 | 1450 | 0.0018 | - |
| 0.0747 | 1500 | 0.0012 | - |
| 0.0772 | 1550 | 0.0016 | - |
| 0.0797 | 1600 | 0.0012 | - |
| 0.0822 | 1650 | 0.0016 | - |
| 0.0847 | 1700 | 0.0027 | - |
| 0.0872 | 1750 | 0.0014 | - |
| 0.0897 | 1800 | 0.0011 | - |
| 0.0922 | 1850 | 0.0011 | - |
| 0.0947 | 1900 | 0.0012 | - |
| 0.0971 | 1950 | 0.0014 | - |
| 0.0996 | 2000 | 0.0014 | - |
| 0.1021 | 2050 | 0.0015 | - |
| 0.1046 | 2100 | 0.0009 | - |
| 0.1071 | 2150 | 0.0015 | - |
| 0.1096 | 2200 | 0.0013 | - |
| 0.1121 | 2250 | 0.0013 | - |
| 0.1146 | 2300 | 0.001 | - |
| 0.1171 | 2350 | 0.0017 | - |
| 0.1196 | 2400 | 0.0013 | - |
| **0.1221** | **2450** | **0.0008** | **0.0323** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## 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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