SetFit with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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 Type: SetFit
- Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 tokens
- Number of Classes: 3 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 |
---|---|
question |
|
feature |
|
bug |
|
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("Data init API for TFLite Swift <details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Source
source
### Tensorflow Version
2.8+
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
The current Swift API only has `init` functions from files on disk unlike the Java (Android) API which has a byte buffer initializer. It'd be convenient if the Swift API could initialize `Interpreters` from `Data`.
Standalone code to reproduce the issue
No code. This is a feature request
Relevant log output
No response")
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 5 | 353.7433 | 6124 |
| Label | Training Sample Count |
|:---------|:----------------------|
| bug | 200 |
| feature | 200 |
| question | 200 |
### 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.1719 | - |
| 0.0067 | 10 | 0.2869 | - |
| 0.0133 | 20 | 0.2513 | - |
| 0.02 | 30 | 0.1871 | - |
| 0.0267 | 40 | 0.2065 | - |
| 0.0333 | 50 | 0.2302 | - |
| 0.04 | 60 | 0.1645 | - |
| 0.0467 | 70 | 0.1887 | - |
| 0.0533 | 80 | 0.1376 | - |
| 0.06 | 90 | 0.1171 | - |
| 0.0667 | 100 | 0.1303 | - |
| 0.0733 | 110 | 0.121 | - |
| 0.08 | 120 | 0.1126 | - |
| 0.0867 | 130 | 0.1247 | - |
| 0.0933 | 140 | 0.1764 | - |
| 0.1 | 150 | 0.0401 | - |
| 0.1067 | 160 | 0.1571 | - |
| 0.1133 | 170 | 0.0186 | - |
| 0.12 | 180 | 0.0501 | - |
| 0.1267 | 190 | 0.1003 | - |
| 0.1333 | 200 | 0.0152 | - |
| 0.14 | 210 | 0.0784 | - |
| 0.1467 | 220 | 0.1423 | - |
| 0.1533 | 230 | 0.1313 | - |
| 0.16 | 240 | 0.0799 | - |
| 0.1667 | 250 | 0.0542 | - |
| 0.1733 | 260 | 0.0426 | - |
| 0.18 | 270 | 0.047 | - |
| 0.1867 | 280 | 0.0062 | - |
| 0.1933 | 290 | 0.0085 | - |
| 0.2 | 300 | 0.0625 | - |
| 0.2067 | 310 | 0.095 | - |
| 0.2133 | 320 | 0.0262 | - |
| 0.22 | 330 | 0.0029 | - |
| 0.2267 | 340 | 0.0097 | - |
| 0.2333 | 350 | 0.063 | - |
| 0.24 | 360 | 0.0059 | - |
| 0.2467 | 370 | 0.0016 | - |
| 0.2533 | 380 | 0.0025 | - |
| 0.26 | 390 | 0.0033 | - |
| 0.2667 | 400 | 0.0006 | - |
| 0.2733 | 410 | 0.0032 | - |
| 0.28 | 420 | 0.0045 | - |
| 0.2867 | 430 | 0.0013 | - |
| 0.2933 | 440 | 0.0011 | - |
| 0.3 | 450 | 0.001 | - |
| 0.3067 | 460 | 0.0044 | - |
| 0.3133 | 470 | 0.001 | - |
| 0.32 | 480 | 0.0009 | - |
| 0.3267 | 490 | 0.0004 | - |
| 0.3333 | 500 | 0.0006 | - |
| 0.34 | 510 | 0.001 | - |
| 0.3467 | 520 | 0.0003 | - |
| 0.3533 | 530 | 0.0008 | - |
| 0.36 | 540 | 0.0003 | - |
| 0.3667 | 550 | 0.0023 | - |
| 0.3733 | 560 | 0.0336 | - |
| 0.38 | 570 | 0.0004 | - |
| 0.3867 | 580 | 0.0003 | - |
| 0.3933 | 590 | 0.0006 | - |
| 0.4 | 600 | 0.0008 | - |
| 0.4067 | 610 | 0.0011 | - |
| 0.4133 | 620 | 0.0002 | - |
| 0.42 | 630 | 0.0004 | - |
| 0.4267 | 640 | 0.0005 | - |
| 0.4333 | 650 | 0.0601 | - |
| 0.44 | 660 | 0.0003 | - |
| 0.4467 | 670 | 0.0003 | - |
| 0.4533 | 680 | 0.0006 | - |
| 0.46 | 690 | 0.0005 | - |
| 0.4667 | 700 | 0.0003 | - |
| 0.4733 | 710 | 0.0006 | - |
| 0.48 | 720 | 0.0001 | - |
| 0.4867 | 730 | 0.0002 | - |
| 0.4933 | 740 | 0.0002 | - |
| 0.5 | 750 | 0.0002 | - |
| 0.5067 | 760 | 0.0002 | - |
| 0.5133 | 770 | 0.0016 | - |
| 0.52 | 780 | 0.0001 | - |
| 0.5267 | 790 | 0.0005 | - |
| 0.5333 | 800 | 0.0004 | - |
| 0.54 | 810 | 0.0039 | - |
| 0.5467 | 820 | 0.0031 | - |
| 0.5533 | 830 | 0.0008 | - |
| 0.56 | 840 | 0.0003 | - |
| 0.5667 | 850 | 0.0002 | - |
| 0.5733 | 860 | 0.0002 | - |
| 0.58 | 870 | 0.0002 | - |
| 0.5867 | 880 | 0.0001 | - |
| 0.5933 | 890 | 0.0004 | - |
| 0.6 | 900 | 0.0002 | - |
| 0.6067 | 910 | 0.0008 | - |
| 0.6133 | 920 | 0.0005 | - |
| 0.62 | 930 | 0.0005 | - |
| 0.6267 | 940 | 0.0002 | - |
| 0.6333 | 950 | 0.0001 | - |
| 0.64 | 960 | 0.0002 | - |
| 0.6467 | 970 | 0.0007 | - |
| 0.6533 | 980 | 0.0002 | - |
| 0.66 | 990 | 0.0002 | - |
| 0.6667 | 1000 | 0.0002 | - |
| 0.6733 | 1010 | 0.0002 | - |
| 0.68 | 1020 | 0.0002 | - |
| 0.6867 | 1030 | 0.0002 | - |
| 0.6933 | 1040 | 0.0004 | - |
| 0.7 | 1050 | 0.0076 | - |
| 0.7067 | 1060 | 0.0002 | - |
| 0.7133 | 1070 | 0.0002 | - |
| 0.72 | 1080 | 0.0001 | - |
| 0.7267 | 1090 | 0.0002 | - |
| 0.7333 | 1100 | 0.0001 | - |
| 0.74 | 1110 | 0.0365 | - |
| 0.7467 | 1120 | 0.0002 | - |
| 0.7533 | 1130 | 0.0002 | - |
| 0.76 | 1140 | 0.0003 | - |
| 0.7667 | 1150 | 0.0002 | - |
| 0.7733 | 1160 | 0.0002 | - |
| 0.78 | 1170 | 0.0004 | - |
| 0.7867 | 1180 | 0.0001 | - |
| 0.7933 | 1190 | 0.0001 | - |
| 0.8 | 1200 | 0.0001 | - |
| 0.8067 | 1210 | 0.0001 | - |
| 0.8133 | 1220 | 0.0002 | - |
| 0.82 | 1230 | 0.0002 | - |
| 0.8267 | 1240 | 0.0001 | - |
| 0.8333 | 1250 | 0.0001 | - |
| 0.84 | 1260 | 0.0002 | - |
| 0.8467 | 1270 | 0.0002 | - |
| 0.8533 | 1280 | 0.0 | - |
| 0.86 | 1290 | 0.0002 | - |
| 0.8667 | 1300 | 0.032 | - |
| 0.8733 | 1310 | 0.0001 | - |
| 0.88 | 1320 | 0.0001 | - |
| 0.8867 | 1330 | 0.0001 | - |
| 0.8933 | 1340 | 0.0003 | - |
| 0.9 | 1350 | 0.0001 | - |
| 0.9067 | 1360 | 0.0001 | - |
| 0.9133 | 1370 | 0.0001 | - |
| 0.92 | 1380 | 0.0001 | - |
| 0.9267 | 1390 | 0.0001 | - |
| 0.9333 | 1400 | 0.0001 | - |
| 0.94 | 1410 | 0.0001 | - |
| 0.9467 | 1420 | 0.0001 | - |
| 0.9533 | 1430 | 0.031 | - |
| 0.96 | 1440 | 0.0001 | - |
| 0.9667 | 1450 | 0.0003 | - |
| 0.9733 | 1460 | 0.0001 | - |
| 0.98 | 1470 | 0.0001 | - |
| 0.9867 | 1480 | 0.0001 | - |
| 0.9933 | 1490 | 0.0001 | - |
| 1.0 | 1500 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## 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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