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---
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) -->
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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>'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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