README.md

---
base_model: BAAI/bge-large-en-v1.5
datasets:
- nazhan/qa-lookup-dataset-iter-1
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Get me the first names of employees working in the 'Legal' department.
- text: Provide the value of the export tariff paid on shipments to 'Country Z' in
 2024.
- text: Show me the value of the freight charges for the shipment made on October
 10, 2023.
- text: Show me the value of the refund issued to 'Customer K' for a defective product.
- text: Provide the value of the environmental compliance cost for 2023.
inference: true
model-index:
- name: SetFit with BAAI/bge-large-en-v1.5
 results:
 - task:
 type: text-classification
 name: Text Classification
 dataset:
 name: nazhan/qa-lookup-dataset-iter-1
 type: nazhan/qa-lookup-dataset-iter-1
 split: test
 metrics:
 - type: accuracy
 value: 1.0
 name: Accuracy
---

# SetFit with BAAI/bge-large-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/qa-lookup-dataset-iter-1](https://huggingface.co/datasets/nazhan/qa-lookup-dataset-iter-1) dataset 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:** 2 classes
- **Training Dataset:** [nazhan/qa-lookup-dataset-iter-1](https://huggingface.co/datasets/nazhan/qa-lookup-dataset-iter-1)
<!-- - **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 |
|:-------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Lookup | <ul><li>'Get me the list of customers who placed their first order in 2024.'</li><li>"Filter by products in the 'Gadgets' category and show me their prices."</li><li>'Get me the email addresses of customers who have made a purchase.'</li></ul> |
| qa | <ul><li>'Provide the value of the accrued vacation liability as of June 2023.'</li><li>'Show me the value of the courier service charges for November 2023.'</li><li>"Provide the value of the consulting contract with 'Client N' finalized in 2023."</li></ul> |

## Evaluation

### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |

## 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-qa-lookup-iter-1-2-epoch")
# Run inference
preds = model("Provide the value of the environmental compliance cost for 2023.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 8 | 12.8309 | 19 |

| Label | Training Sample Count |
|:-------|:----------------------|
| Lookup | 65 |
| qa | 71 |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0034 | 1 | 0.1823 | - |
| 0.1701 | 50 | 0.0031 | - |
| 0.3401 | 100 | 0.0012 | - |
| 0.5102 | 150 | 0.0011 | - |
| 0.6803 | 200 | 0.0009 | - |
| 0.8503 | 250 | 0.0008 | - |
| 1.0 | 294 | - | 0.0004 |
| 1.0204 | 300 | 0.0008 | - |
| 1.1905 | 350 | 0.0008 | - |
| 1.3605 | 400 | 0.0007 | - |
| 1.5306 | 450 | 0.0006 | - |
| 1.7007 | 500 | 0.0006 | - |
| 1.8707 | 550 | 0.0006 | - |
| **2.0** | **588** | **-** | **0.0003** |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->