setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 model-index region:us
---
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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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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