---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I'm encountering errors with a pod in the "kube-public" namespace. Any suggestions
    on how to debug it?
- text: Can you check sandbox-1 for problems?
- text: I need permissions for the prod-aws account to troubleshoot an issue.
- text: Can you tell me about your hobbies?
- text: How can I reduce stress?
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9961538461538462
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **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:** 5 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                                                                                                                                                                                                                                                                                                                 |
|:------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| NONE              | <ul><li>'How do I learn to play the guitar?'</li><li>"What's the longest river in the world?"</li><li>'How do I overcome procrastination?'</li></ul>                                                                                                                                                                     |
| KUBIE             | <ul><li>'What logs should I check to identify container crashes in the qa-soc-svcs namespace?'</li><li>'Can you suggest ways to troubleshoot an image pull error in the "kube-public" namespace?'</li><li>"I'm encountering errors with a pod in the sandbox-6 namespace. Any suggestions on how to debug it?"</li></ul> |
| aws_iam           | <ul><li>'Show me the IAM role details including attached policies.'</li><li>'Show me the IAM roles that have the "admin" prefix.'</li><li>'How can I get detailed information about a particular IAM role?'</li></ul>                                                                                                    |
| DOC               | <ul><li>'How to access ArgoCD on Production?'</li><li>'How to run terraform in CDO?'</li><li>'How to push images to dockerhub.cisco.com?'</li></ul>                                                                                                                                                                      |
| access_management | <ul><li>'Access to prod-aws infrastructure is required urgently for a deployment.'</li><li>'Could you provide me access to the dev-aws resources?'</li><li>'I require access to the prod-sagemaker instance for machine learning experiments.'</li></ul>                                                                 |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9962   |

## 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("setfit_model_id")
# Run inference
preds = model("How can I reduce stress?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.5408 | 17  |

| Label             | Training Sample Count |
|:------------------|:----------------------|
| aws_iam           | 20                    |
| access_management | 20                    |
| DOC               | 18                    |
| KUBIE             | 20                    |
| NONE              | 20                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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 |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0021  | 1       | 0.2675        | -               |
| 0.1042  | 50      | 0.1143        | -               |
| 0.2083  | 100     | 0.0578        | -               |
| 0.3125  | 150     | 0.0028        | -               |
| 0.4167  | 200     | 0.0032        | -               |
| 0.5208  | 250     | 0.0007        | -               |
| 0.625   | 300     | 0.0006        | -               |
| 0.7292  | 350     | 0.0004        | -               |
| 0.8333  | 400     | 0.0005        | -               |
| 0.9375  | 450     | 0.0006        | -               |
| **1.0** | **480** | **-**         | **0.0027**      |
| 1.0417  | 500     | 0.0004        | -               |
| 1.1458  | 550     | 0.0002        | -               |
| 1.25    | 600     | 0.0003        | -               |
| 1.3542  | 650     | 0.0002        | -               |
| 1.4583  | 700     | 0.0002        | -               |
| 1.5625  | 750     | 0.0002        | -               |
| 1.6667  | 800     | 0.0002        | -               |
| 1.7708  | 850     | 0.0002        | -               |
| 1.875   | 900     | 0.0002        | -               |
| 1.9792  | 950     | 0.0001        | -               |
| 2.0     | 960     | -             | 0.0032          |
| 2.0833  | 1000    | 0.0001        | -               |
| 2.1875  | 1050    | 0.0002        | -               |
| 2.2917  | 1100    | 0.0001        | -               |
| 2.3958  | 1150    | 0.0002        | -               |
| 2.5     | 1200    | 0.0002        | -               |
| 2.6042  | 1250    | 0.0001        | -               |
| 2.7083  | 1300    | 0.0002        | -               |
| 2.8125  | 1350    | 0.0001        | -               |
| 2.9167  | 1400    | 0.0001        | -               |
| 3.0     | 1440    | -             | 0.004           |
| 3.0208  | 1450    | 0.0001        | -               |
| 3.125   | 1500    | 0.0001        | -               |
| 3.2292  | 1550    | 0.0002        | -               |
| 3.3333  | 1600    | 0.0002        | -               |
| 3.4375  | 1650    | 0.0001        | -               |
| 3.5417  | 1700    | 0.0002        | -               |
| 3.6458  | 1750    | 0.0001        | -               |
| 3.75    | 1800    | 0.0001        | -               |
| 3.8542  | 1850    | 0.0001        | -               |
| 3.9583  | 1900    | 0.0002        | -               |
| 4.0     | 1920    | -             | 0.0037          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.9.6
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.1.2
- Datasets: 2.19.0
- 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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