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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Upgrade all installed packages with superuser privileges
- text: Install package 'vim' as superuser
- text: Remove package 'firefox' with superuser privileges
- text: Change permissions of directory 'docs' to writable
- text: Update package lists using superuser privileges
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.0
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:** 30 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 |
|:----------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| ls | <ul><li>'List all files and directories'</li><li>'Show files in the current directory'</li><li>'Display contents of the current directory'</li></ul> |
| cd | <ul><li>'Change to the specified directory'</li><li>'Move to the home directory'</li><li>'Navigate to the specified directory path'</li></ul> |
| mkdir docs | <ul><li>"Create a new directory named 'docs'"</li></ul> |
| mkdir projects | <ul><li>"Make a directory named 'projects'"</li></ul> |
| mkdir data | <ul><li>"Create a folder called 'data'"</li></ul> |
| mkdir images | <ul><li>"Make a directory named 'images'"</li></ul> |
| mkdir scripts | <ul><li>"Create a new folder named 'scripts'"</li></ul> |
| rm example.txt | <ul><li>"Remove the file named 'example.txt'"</li></ul> |
| rm temp.txt | <ul><li>"Delete the file called 'temp.txt'"</li></ul> |
| rm file1 | <ul><li>"Remove the file named 'file1'"</li></ul> |
| rm file2 | <ul><li>"Delete the file named 'file2'"</li></ul> |
| rm backup.txt | <ul><li>"Remove the file named 'backup.txt'"</li></ul> |
| cp file1 /destination | <ul><li>'Copy file1 to directory /destination'</li></ul> |
| cp file2 /backup | <ul><li>'Duplicate file2 to directory /backup'</li></ul> |
| cp file3 /archive | <ul><li>'Copy file3 to folder /archive'</li></ul> |
| cp file4 /temp | <ul><li>'Duplicate file4 to folder /temp'</li></ul> |
| cp file5 /images | <ul><li>'Copy file5 to directory /images'</li></ul> |
| mv file2 /new_location | <ul><li>'Move file2 to directory /new_location'</li></ul> |
| mv file3 /backup | <ul><li>'Transfer file3 to directory /backup'</li></ul> |
| mv file4 /archive | <ul><li>'Move file4 to folder /archive'</li></ul> |
| mv file5 /temp | <ul><li>'Transfer file5 to folder /temp'</li></ul> |
| mv file6 /images | <ul><li>'Move file6 to directory /images'</li></ul> |
| cat README.md | <ul><li>"Display the contents of file 'README.md'"</li></ul> |
| cat notes.txt | <ul><li>"Show the content of file 'notes.txt'"</li></ul> |
| cat data.csv | <ul><li>"Print the contents of file 'data.csv'"</li></ul> |
| cat script.sh | <ul><li>"Display the content of file 'script.sh'"</li></ul> |
| cat config.ini | <ul><li>"Show the contents of file 'config.ini'"</li></ul> |
| grep 'pattern' data.txt | <ul><li>"Search for 'pattern' in file 'data.txt'"</li></ul> |
| grep 'word' text.txt | <ul><li>"Find occurrences of 'word' in file 'text.txt'"</li></ul> |
| grep 'keyword' document.txt | <ul><li>"Search for 'keyword' in file 'document.txt'"</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.0 |
## 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("souvenger/NLP2Linux")
# Run inference
preds = model("Install package 'vim' as superuser")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 5 | 5.6667 | 9 |
| Label | Training Sample Count |
|:----------------------------|:----------------------|
| cat README.md | 1 |
| cat config.ini | 1 |
| cat data.csv | 1 |
| cat notes.txt | 1 |
| cat script.sh | 1 |
| cd | 10 |
| cp file1 /destination | 1 |
| cp file2 /backup | 1 |
| cp file3 /archive | 1 |
| cp file4 /temp | 1 |
| cp file5 /images | 1 |
| grep 'keyword' document.txt | 1 |
| grep 'pattern' data.txt | 1 |
| grep 'word' text.txt | 1 |
| ls | 10 |
| mkdir data | 1 |
| mkdir docs | 1 |
| mkdir images | 1 |
| mkdir projects | 1 |
| mkdir scripts | 1 |
| mv file2 /new_location | 1 |
| mv file3 /backup | 1 |
| mv file4 /archive | 1 |
| mv file5 /temp | 1 |
| mv file6 /images | 1 |
| rm backup.txt | 1 |
| rm example.txt | 1 |
| rm file1 | 1 |
| rm file2 | 1 |
| rm temp.txt | 1 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0042 | 1 | 0.1215 | - |
| 0.2083 | 50 | 0.0232 | - |
| 0.4167 | 100 | 0.01 | - |
| 0.625 | 150 | 0.0044 | - |
| 0.8333 | 200 | 0.0025 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.0
- PyTorch: 2.1.2
- Datasets: 2.1.0
- Tokenizers: 0.15.1
## 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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