Collab-uniba/fprime-binary-setfit

SetFit

This is a SetFit model that can be used for Text Classification. A LogisticRegression 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 with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 384 tokens
• Number of Classes: 2 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
bug	"A global-buffer-overflow error in BufferQueueTest.cpp line 126\n
non-bug	"Install Upgraded PIP in new Project\n

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

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Switch Framer and Deframer to use Mallocator Pattern
| | |
|:---|:---|
|**_F´ Version_**| |
|**_Affected Component_**|   |
---
## Problem Description

Mallocator pattern is preferred over member-allocated buffers.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	124.1383	2486

Label	Training Sample Count
bug	296
non-bug	304

Training Hyperparameters

• batch_size: (16, 2)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• 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
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0007	1	0.447	-
0.0333	50	0.2333	-
0.0667	100	0.083	-
0.1	150	0.039	-
0.1333	200	0.0354	-
0.1667	250	0.0177	-
0.2	300	0.0053	-
0.2333	350	0.0004	-
0.2667	400	0.0027	-
0.3	450	0.0015	-
0.3333	500	0.002	-
0.3667	550	0.0003	-
0.4	600	0.0001	-
0.4333	650	0.0001	-
0.4667	700	0.0001	-
0.5	750	0.0001	-
0.5333	800	0.0001	-
0.5667	850	0.0001	-
0.6	900	0.0001	-
0.6333	950	0.0001	-
0.6667	1000	0.0001	-
0.7	1050	0.0	-
0.7333	1100	0.0	-
0.7667	1150	0.0001	-
0.8	1200	0.0	-
0.8333	1250	0.0001	-
0.8667	1300	0.0	-
0.9	1350	0.0	-
0.9333	1400	0.0001	-
0.9667	1450	0.0	-
1.0	1500	0.0	-

Framework Versions

• Python: 3.11.6
• SetFit: 1.1.0
• Sentence Transformers: 3.0.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Datasets: 2.21.0
• Tokenizers: 0.19.1

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}
}