---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Thank you for your email. Please go ahead and issue. Please invoice in KES
- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020
    AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02
    - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420
    $21.00
- text: We need your assistance with the payment for the recent office supplies order.
    Let us know once it's done.
- text: I have reported this in November and not only was the trip supposed to be
    cancelled and credited I was double billed and the billing has not been corrected.
    The total credit should be $667.20. Please confirm this will be done.
- text: The invoice for the travel arrangements needs to be settled. Kindly provide
    payment confirmation.
inference: true
---

# SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 14 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** 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)


## 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("mann2107/BCMPIIRAB_MiniLM_ALLNew")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 25.6577 | 136 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 24                    |
| 1     | 24                    |
| 2     | 24                    |
| 3     | 24                    |
| 4     | 24                    |
| 5     | 24                    |
| 6     | 24                    |
| 7     | 24                    |
| 8     | 24                    |
| 9     | 24                    |
| 10    | 24                    |
| 11    | 24                    |
| 12    | 24                    |
| 13    | 24                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 99
- body_learning_rate: (0.0002733656643765287, 0.0002733656643765287)
- head_learning_rate: 2.7029049129688732e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- max_length: 512
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0002  | 1        | 0.2546        | -               |
| 0.0120  | 50       | 0.1667        | -               |
| 0.0241  | 100      | 0.1165        | -               |
| 0.0361  | 150      | 0.0799        | -               |
| 0.0481  | 200      | 0.0212        | -               |
| 0.0601  | 250      | 0.0188        | -               |
| 0.0722  | 300      | 0.0531        | -               |
| 0.0842  | 350      | 0.0273        | -               |
| 0.0962  | 400      | 0.0111        | -               |
| 0.1082  | 450      | 0.0203        | -               |
| 0.1203  | 500      | 0.0397        | -               |
| 0.1323  | 550      | 0.0164        | -               |
| 0.1443  | 600      | 0.0045        | -               |
| 0.1563  | 650      | 0.0032        | -               |
| 0.1684  | 700      | 0.001         | -               |
| 0.1804  | 750      | 0.0011        | -               |
| 0.1924  | 800      | 0.0004        | -               |
| 0.2044  | 850      | 0.0009        | -               |
| 0.2165  | 900      | 0.0006        | -               |
| 0.2285  | 950      | 0.0008        | -               |
| 0.2405  | 1000     | 0.0004        | -               |
| 0.2525  | 1050     | 0.0008        | -               |
| 0.2646  | 1100     | 0.0005        | -               |
| 0.2766  | 1150     | 0.0006        | -               |
| 0.2886  | 1200     | 0.0007        | -               |
| 0.3006  | 1250     | 0.0043        | -               |
| 0.3127  | 1300     | 0.0004        | -               |
| 0.3247  | 1350     | 0.0005        | -               |
| 0.3367  | 1400     | 0.0005        | -               |
| 0.3487  | 1450     | 0.0004        | -               |
| 0.3608  | 1500     | 0.0004        | -               |
| 0.3728  | 1550     | 0.0005        | -               |
| 0.3848  | 1600     | 0.0007        | -               |
| 0.3968  | 1650     | 0.0006        | -               |
| 0.4089  | 1700     | 0.0002        | -               |
| 0.4209  | 1750     | 0.0006        | -               |
| 0.4329  | 1800     | 0.0008        | -               |
| 0.4449  | 1850     | 0.0003        | -               |
| 0.4570  | 1900     | 0.0005        | -               |
| 0.4690  | 1950     | 0.0003        | -               |
| 0.4810  | 2000     | 0.0003        | -               |
| 0.4930  | 2050     | 0.0003        | -               |
| 0.5051  | 2100     | 0.0006        | -               |
| 0.5171  | 2150     | 0.0003        | -               |
| 0.5291  | 2200     | 0.0002        | -               |
| 0.5411  | 2250     | 0.0002        | -               |
| 0.5532  | 2300     | 0.0002        | -               |
| 0.5652  | 2350     | 0.0004        | -               |
| 0.5772  | 2400     | 0.0003        | -               |
| 0.5892  | 2450     | 0.0003        | -               |
| 0.6013  | 2500     | 0.0002        | -               |
| 0.6133  | 2550     | 0.0002        | -               |
| 0.6253  | 2600     | 0.0013        | -               |
| 0.6373  | 2650     | 0.0002        | -               |
| 0.6494  | 2700     | 0.0007        | -               |
| 0.6614  | 2750     | 0.0004        | -               |
| 0.6734  | 2800     | 0.0007        | -               |
| 0.6854  | 2850     | 0.0018        | -               |
| 0.6975  | 2900     | 0.0002        | -               |
| 0.7095  | 2950     | 0.0003        | -               |
| 0.7215  | 3000     | 0.0006        | -               |
| 0.7335  | 3050     | 0.0003        | -               |
| 0.7456  | 3100     | 0.0002        | -               |
| 0.7576  | 3150     | 0.0002        | -               |
| 0.7696  | 3200     | 0.0002        | -               |
| 0.7816  | 3250     | 0.0002        | -               |
| 0.7937  | 3300     | 0.0002        | -               |
| 0.8057  | 3350     | 0.0001        | -               |
| 0.8177  | 3400     | 0.0003        | -               |
| 0.8297  | 3450     | 0.0002        | -               |
| 0.8418  | 3500     | 0.0002        | -               |
| 0.8538  | 3550     | 0.0002        | -               |
| 0.8658  | 3600     | 0.0002        | -               |
| 0.8778  | 3650     | 0.0002        | -               |
| 0.8899  | 3700     | 0.0002        | -               |
| 0.9019  | 3750     | 0.0005        | -               |
| 0.9139  | 3800     | 0.0002        | -               |
| 0.9259  | 3850     | 0.0001        | -               |
| 0.9380  | 3900     | 0.0004        | -               |
| 0.9500  | 3950     | 0.0001        | -               |
| 0.9620  | 4000     | 0.0005        | -               |
| 0.9740  | 4050     | 0.0002        | -               |
| 0.9861  | 4100     | 0.0002        | -               |
| 0.9981  | 4150     | 0.0001        | -               |
| **1.0** | **4158** | **-**         | **0.0302**      |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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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