SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
ls |
- 'List all files and directories'
- 'Show files in the current directory'
- 'Display contents of the current directory'
|
cd |
- 'Change to the specified directory'
- 'Move to the home directory'
- 'Navigate to the specified directory path'
|
mkdir docs |
- "Create a new directory named 'docs'"
|
mkdir projects |
- "Make a directory named 'projects'"
|
mkdir data |
- "Create a folder called 'data'"
|
mkdir images |
- "Make a directory named 'images'"
|
mkdir scripts |
- "Create a new folder named 'scripts'"
|
rm example.txt |
- "Remove the file named 'example.txt'"
|
rm temp.txt |
- "Delete the file called 'temp.txt'"
|
rm file1 |
- "Remove the file named 'file1'"
|
rm file2 |
- "Delete the file named 'file2'"
|
rm backup.txt |
- "Remove the file named 'backup.txt'"
|
cp file1 /destination |
- 'Copy file1 to directory /destination'
|
cp file2 /backup |
- 'Duplicate file2 to directory /backup'
|
cp file3 /archive |
- 'Copy file3 to folder /archive'
|
cp file4 /temp |
- 'Duplicate file4 to folder /temp'
|
cp file5 /images |
- 'Copy file5 to directory /images'
|
mv file2 /new_location |
- 'Move file2 to directory /new_location'
|
mv file3 /backup |
- 'Transfer file3 to directory /backup'
|
mv file4 /archive |
- 'Move file4 to folder /archive'
|
mv file5 /temp |
- 'Transfer file5 to folder /temp'
|
mv file6 /images |
- 'Move file6 to directory /images'
|
cat README.md |
- "Display the contents of file 'README.md'"
|
cat notes.txt |
- "Show the content of file 'notes.txt'"
|
cat data.csv |
- "Print the contents of file 'data.csv'"
|
cat script.sh |
- "Display the content of file 'script.sh'"
|
cat config.ini |
- "Show the contents of file 'config.ini'"
|
grep 'pattern' data.txt |
- "Search for 'pattern' in file 'data.txt'"
|
grep 'word' text.txt |
- "Find occurrences of 'word' in file 'text.txt'"
|
grep 'keyword' document.txt |
- "Search for 'keyword' in file 'document.txt'"
|
Evaluation
Metrics
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("souvenger/NLP2Linux")
preds = model("Install package 'vim' as superuser")
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
@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}
}