---
base_model: BAAI/bge-large-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I don't want to handle any filtering tasks.
- text: Show me all customers who have the last name 'Doe'.
- text: What tables are available for data analysis in starhub_data_asset?
- text: what do you think it is?
- text: Provide data_asset_001_pcc product category details.
inference: true
model-index:
- name: SetFit with BAAI/bge-large-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9818181818181818
      name: Accuracy
---

# SetFit with BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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>'Show me median Intangible Assets'</li><li>'Can I have sum Cost_Entertainment?'</li><li>'Get me min RevenueVariance_Actual_vs_Forecast.'</li></ul>                                                                                                                                                                                                                                                                                          |
| Lookup_1     | <ul><li>'Show me data_asset_kpi_cf details.'</li><li>'Retrieve data_asset_kpi_cf details.'</li><li>'Show M&A deal size by sector.'</li></ul>                                                                                                                                                                                                                                                                                                        |
| Viewtables   | <ul><li>'What tables are included in the starhub_data_asset database that are required for performing a basic data analysis?'</li><li>'What is the full list of tables available for use in queries within the starhub_data_asset database?'</li><li>'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'</li></ul>                                                                    |
| Tablejoin    | <ul><li>'Is it possible to merge the Employees and Orders tables to see which employee handled each order?'</li><li>'Join data_asset_001_ta with data_asset_kpi_cf.'</li><li>'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'</li></ul>                                                                                                                                        |
| Lookup       | <ul><li>'Filter by customers who have placed more than 3 orders and get me their email addresses.'</li><li>"Filter by customers in the city 'New York' and show me their phone numbers."</li><li>"Can you filter by employees who work in the 'Research' department?"</li></ul>                                                                                                                                                                     |
| Generalreply | <ul><li>"Oh, I just stepped outside and it's actually quite lovely! The sun is shining and there's a light breeze. How about you?"</li><li>"One of my short-term goals is to learn a new skill, like coding or cooking. I also want to save up enough money for a weekend trip with friends. How about you, any short-term goals you're working towards?"</li><li>'Hey! My day is going pretty well, thanks for asking. How about yours?'</li></ul> |
| Rejection    | <ul><li>'I have no interest in generating more data.'</li><li>"I don't want to engage in filtering operations."</li><li>"I'd rather not filter this dataset."</li></ul>                                                                                                                                                                                                                                                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9818   |

## 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-large-en-v1.5-brahmaputra-iter-10-3rd")
# Run inference
preds = model("what do you think it is?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.7137 | 62  |

| Label        | Training Sample Count |
|:-------------|:----------------------|
| Tablejoin    | 128                   |
| Rejection    | 73                    |
| Aggregation  | 222                   |
| Lookup       | 55                    |
| Generalreply | 75                    |
| Viewtables   | 76                    |
| Lookup_1     | 157                   |

### 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        | 0.2001        | -               |
| 0.0022     | 50       | 0.1566        | -               |
| 0.0045     | 100      | 0.0816        | -               |
| 0.0067     | 150      | 0.0733        | -               |
| 0.0089     | 200      | 0.0075        | -               |
| 0.0112     | 250      | 0.0059        | -               |
| 0.0134     | 300      | 0.0035        | -               |
| 0.0156     | 350      | 0.0034        | -               |
| 0.0179     | 400      | 0.0019        | -               |
| 0.0201     | 450      | 0.0015        | -               |
| 0.0223     | 500      | 0.0021        | -               |
| 0.0246     | 550      | 0.003         | -               |
| 0.0268     | 600      | 0.0021        | -               |
| 0.0290     | 650      | 0.0011        | -               |
| 0.0313     | 700      | 0.0015        | -               |
| 0.0335     | 750      | 0.0011        | -               |
| 0.0357     | 800      | 0.001         | -               |
| 0.0380     | 850      | 0.001         | -               |
| 0.0402     | 900      | 0.0012        | -               |
| 0.0424     | 950      | 0.0012        | -               |
| 0.0447     | 1000     | 0.0011        | -               |
| 0.0469     | 1050     | 0.0008        | -               |
| 0.0491     | 1100     | 0.0009        | -               |
| 0.0514     | 1150     | 0.001         | -               |
| 0.0536     | 1200     | 0.0008        | -               |
| 0.0558     | 1250     | 0.0011        | -               |
| 0.0581     | 1300     | 0.0009        | -               |
| 0.0603     | 1350     | 0.001         | -               |
| 0.0625     | 1400     | 0.0007        | -               |
| 0.0647     | 1450     | 0.0008        | -               |
| 0.0670     | 1500     | 0.0007        | -               |
| 0.0692     | 1550     | 0.001         | -               |
| 0.0714     | 1600     | 0.0007        | -               |
| 0.0737     | 1650     | 0.0007        | -               |
| 0.0759     | 1700     | 0.0006        | -               |
| 0.0781     | 1750     | 0.0008        | -               |
| 0.0804     | 1800     | 0.0006        | -               |
| 0.0826     | 1850     | 0.0005        | -               |
| 0.0848     | 1900     | 0.0006        | -               |
| 0.0871     | 1950     | 0.0005        | -               |
| 0.0893     | 2000     | 0.0007        | -               |
| 0.0915     | 2050     | 0.0005        | -               |
| 0.0938     | 2100     | 0.0006        | -               |
| 0.0960     | 2150     | 0.0007        | -               |
| 0.0982     | 2200     | 0.0005        | -               |
| 0.1005     | 2250     | 0.0008        | -               |
| 0.1027     | 2300     | 0.0005        | -               |
| 0.1049     | 2350     | 0.0008        | -               |
| 0.1072     | 2400     | 0.0007        | -               |
| **0.1094** | **2450** | **0.0007**    | **0.0094**      |

* 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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