---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: dataright np^sin 2 np^pi 224 t | Audio
- text: robust way to ask the database for its current transaction state. | AtomicTests
- text: the string marking the beginning of a print statement. | Environment
- text: handled otherwise by a particular method. | StringMethods
- text: table. | PlotAccessor
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A MultiOutputClassifier 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:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## 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("table. | PlotAccessor")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.9868 | 28  |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0006 | 1     | 0.2743        | -               |
| 0.0292 | 50    | 0.3546        | -               |
| 0.0585 | 100   | 0.3106        | -               |
| 0.0877 | 150   | 0.2652        | -               |
| 0.1170 | 200   | 0.2543        | -               |
| 0.1462 | 250   | 0.2544        | -               |
| 0.1754 | 300   | 0.2521        | -               |
| 0.2047 | 350   | 0.2508        | -               |
| 0.2339 | 400   | 0.2485        | -               |
| 0.2632 | 450   | 0.2499        | -               |
| 0.2924 | 500   | 0.2453        | -               |
| 0.3216 | 550   | 0.2414        | -               |
| 0.3509 | 600   | 0.2379        | -               |
| 0.3801 | 650   | 0.2426        | -               |
| 0.4094 | 700   | 0.2383        | -               |
| 0.4386 | 750   | 0.2385        | -               |
| 0.4678 | 800   | 0.2402        | -               |
| 0.4971 | 850   | 0.2329        | -               |
| 0.5263 | 900   | 0.2328        | -               |
| 0.5556 | 950   | 0.2309        | -               |
| 0.5848 | 1000  | 0.228         | -               |
| 0.6140 | 1050  | 0.2149        | -               |
| 0.6433 | 1100  | 0.2053        | -               |
| 0.6725 | 1150  | 0.1997        | -               |
| 0.7018 | 1200  | 0.1978        | -               |
| 0.7310 | 1250  | 0.1896        | -               |
| 0.7602 | 1300  | 0.1775        | -               |
| 0.7895 | 1350  | 0.1629        | -               |
| 0.8187 | 1400  | 0.1571        | -               |
| 0.8480 | 1450  | 0.1493        | -               |
| 0.8772 | 1500  | 0.1445        | -               |
| 0.9064 | 1550  | 0.1345        | -               |
| 0.9357 | 1600  | 0.1306        | -               |
| 0.9649 | 1650  | 0.1276        | -               |
| 0.9942 | 1700  | 0.1181        | -               |
| 1.0234 | 1750  | 0.1081        | -               |
| 1.0526 | 1800  | 0.1081        | -               |
| 1.0819 | 1850  | 0.1006        | -               |
| 1.1111 | 1900  | 0.0892        | -               |
| 1.1404 | 1950  | 0.0996        | -               |
| 1.1696 | 2000  | 0.0912        | -               |
| 1.1988 | 2050  | 0.0868        | -               |
| 1.2281 | 2100  | 0.089         | -               |
| 1.2573 | 2150  | 0.078         | -               |
| 1.2865 | 2200  | 0.0864        | -               |
| 1.3158 | 2250  | 0.0719        | -               |
| 1.3450 | 2300  | 0.0675        | -               |
| 1.3743 | 2350  | 0.0669        | -               |
| 1.4035 | 2400  | 0.0666        | -               |
| 1.4327 | 2450  | 0.074         | -               |
| 1.4620 | 2500  | 0.0671        | -               |
| 1.4912 | 2550  | 0.0663        | -               |
| 1.5205 | 2600  | 0.0599        | -               |
| 1.5497 | 2650  | 0.0612        | -               |
| 1.5789 | 2700  | 0.056         | -               |
| 1.6082 | 2750  | 0.0575        | -               |
| 1.6374 | 2800  | 0.0553        | -               |
| 1.6667 | 2850  | 0.0611        | -               |
| 1.6959 | 2900  | 0.0535        | -               |
| 1.7251 | 2950  | 0.0558        | -               |
| 1.7544 | 3000  | 0.054         | -               |
| 1.7836 | 3050  | 0.0552        | -               |
| 1.8129 | 3100  | 0.0494        | -               |
| 1.8421 | 3150  | 0.0489        | -               |
| 1.8713 | 3200  | 0.0494        | -               |
| 1.9006 | 3250  | 0.0468        | -               |
| 1.9298 | 3300  | 0.0527        | -               |
| 1.9591 | 3350  | 0.0496        | -               |
| 1.9883 | 3400  | 0.0492        | -               |
| 2.0175 | 3450  | 0.0415        | -               |
| 2.0468 | 3500  | 0.0434        | -               |
| 2.0760 | 3550  | 0.0456        | -               |
| 2.1053 | 3600  | 0.0394        | -               |
| 2.1345 | 3650  | 0.0387        | -               |
| 2.1637 | 3700  | 0.0381        | -               |
| 2.1930 | 3750  | 0.0378        | -               |
| 2.2222 | 3800  | 0.0387        | -               |
| 2.2515 | 3850  | 0.035         | -               |
| 2.2807 | 3900  | 0.0384        | -               |
| 2.3099 | 3950  | 0.0386        | -               |
| 2.3392 | 4000  | 0.0379        | -               |
| 2.3684 | 4050  | 0.0315        | -               |
| 2.3977 | 4100  | 0.0372        | -               |
| 2.4269 | 4150  | 0.0324        | -               |
| 2.4561 | 4200  | 0.0319        | -               |
| 2.4854 | 4250  | 0.0306        | -               |
| 2.5146 | 4300  | 0.0309        | -               |
| 2.5439 | 4350  | 0.0382        | -               |
| 2.5731 | 4400  | 0.0314        | -               |
| 2.6023 | 4450  | 0.0314        | -               |
| 2.6316 | 4500  | 0.0254        | -               |
| 2.6608 | 4550  | 0.0257        | -               |
| 2.6901 | 4600  | 0.0325        | -               |
| 2.7193 | 4650  | 0.0249        | -               |
| 2.7485 | 4700  | 0.026         | -               |
| 2.7778 | 4750  | 0.0298        | -               |
| 2.8070 | 4800  | 0.0253        | -               |
| 2.8363 | 4850  | 0.0306        | -               |
| 2.8655 | 4900  | 0.0285        | -               |
| 2.8947 | 4950  | 0.0273        | -               |
| 2.9240 | 5000  | 0.029         | -               |
| 2.9532 | 5050  | 0.0238        | -               |
| 2.9825 | 5100  | 0.0287        | -               |
| 3.0117 | 5150  | 0.0267        | -               |
| 3.0409 | 5200  | 0.0259        | -               |
| 3.0702 | 5250  | 0.0232        | -               |
| 3.0994 | 5300  | 0.0269        | -               |
| 3.1287 | 5350  | 0.0239        | -               |
| 3.1579 | 5400  | 0.0268        | -               |
| 3.1871 | 5450  | 0.0242        | -               |
| 3.2164 | 5500  | 0.0264        | -               |
| 3.2456 | 5550  | 0.0217        | -               |
| 3.2749 | 5600  | 0.026         | -               |
| 3.3041 | 5650  | 0.0248        | -               |
| 3.3333 | 5700  | 0.0242        | -               |
| 3.3626 | 5750  | 0.0239        | -               |
| 3.3918 | 5800  | 0.0229        | -               |
| 3.4211 | 5850  | 0.0205        | -               |
| 3.4503 | 5900  | 0.0252        | -               |
| 3.4795 | 5950  | 0.0208        | -               |
| 3.5088 | 6000  | 0.024         | -               |
| 3.5380 | 6050  | 0.025         | -               |
| 3.5673 | 6100  | 0.0235        | -               |
| 3.5965 | 6150  | 0.0228        | -               |
| 3.6257 | 6200  | 0.0213        | -               |
| 3.6550 | 6250  | 0.024         | -               |
| 3.6842 | 6300  | 0.021         | -               |
| 3.7135 | 6350  | 0.0236        | -               |
| 3.7427 | 6400  | 0.0213        | -               |
| 3.7719 | 6450  | 0.0188        | -               |
| 3.8012 | 6500  | 0.0239        | -               |
| 3.8304 | 6550  | 0.0244        | -               |
| 3.8596 | 6600  | 0.0228        | -               |
| 3.8889 | 6650  | 0.0219        | -               |
| 3.9181 | 6700  | 0.0251        | -               |
| 3.9474 | 6750  | 0.02          | -               |
| 3.9766 | 6800  | 0.0209        | -               |
| 4.0058 | 6850  | 0.0204        | -               |
| 4.0351 | 6900  | 0.022         | -               |
| 4.0643 | 6950  | 0.0197        | -               |
| 4.0936 | 7000  | 0.019         | -               |
| 4.1228 | 7050  | 0.0212        | -               |
| 4.1520 | 7100  | 0.0201        | -               |
| 4.1813 | 7150  | 0.021         | -               |
| 4.2105 | 7200  | 0.0219        | -               |
| 4.2398 | 7250  | 0.0223        | -               |
| 4.2690 | 7300  | 0.0236        | -               |
| 4.2982 | 7350  | 0.0206        | -               |
| 4.3275 | 7400  | 0.02          | -               |
| 4.3567 | 7450  | 0.0223        | -               |
| 4.3860 | 7500  | 0.0212        | -               |
| 4.4152 | 7550  | 0.0205        | -               |
| 4.4444 | 7600  | 0.0212        | -               |
| 4.4737 | 7650  | 0.0189        | -               |
| 4.5029 | 7700  | 0.0213        | -               |
| 4.5322 | 7750  | 0.021         | -               |
| 4.5614 | 7800  | 0.0212        | -               |
| 4.5906 | 7850  | 0.0196        | -               |
| 4.6199 | 7900  | 0.0187        | -               |
| 4.6491 | 7950  | 0.0185        | -               |
| 4.6784 | 8000  | 0.017         | -               |
| 4.7076 | 8050  | 0.0211        | -               |
| 4.7368 | 8100  | 0.0177        | -               |
| 4.7661 | 8150  | 0.0208        | -               |
| 4.7953 | 8200  | 0.0235        | -               |
| 4.8246 | 8250  | 0.0196        | -               |
| 4.8538 | 8300  | 0.0193        | -               |
| 4.8830 | 8350  | 0.0185        | -               |
| 4.9123 | 8400  | 0.022         | -               |
| 4.9415 | 8450  | 0.0196        | -               |
| 4.9708 | 8500  | 0.0196        | -               |
| 5.0    | 8550  | 0.0227        | -               |
| 5.0292 | 8600  | 0.0188        | -               |
| 5.0585 | 8650  | 0.0183        | -               |
| 5.0877 | 8700  | 0.0192        | -               |
| 5.1170 | 8750  | 0.0219        | -               |
| 5.1462 | 8800  | 0.0181        | -               |
| 5.1754 | 8850  | 0.0173        | -               |
| 5.2047 | 8900  | 0.0178        | -               |
| 5.2339 | 8950  | 0.0183        | -               |
| 5.2632 | 9000  | 0.0199        | -               |
| 5.2924 | 9050  | 0.0194        | -               |
| 5.3216 | 9100  | 0.0219        | -               |
| 5.3509 | 9150  | 0.0218        | -               |
| 5.3801 | 9200  | 0.0186        | -               |
| 5.4094 | 9250  | 0.0202        | -               |
| 5.4386 | 9300  | 0.0195        | -               |
| 5.4678 | 9350  | 0.0181        | -               |
| 5.4971 | 9400  | 0.0197        | -               |
| 5.5263 | 9450  | 0.0176        | -               |
| 5.5556 | 9500  | 0.0181        | -               |
| 5.5848 | 9550  | 0.0193        | -               |
| 5.6140 | 9600  | 0.0183        | -               |
| 5.6433 | 9650  | 0.0206        | -               |
| 5.6725 | 9700  | 0.0191        | -               |
| 5.7018 | 9750  | 0.0179        | -               |
| 5.7310 | 9800  | 0.0192        | -               |
| 5.7602 | 9850  | 0.0184        | -               |
| 5.7895 | 9900  | 0.0194        | -               |
| 5.8187 | 9950  | 0.0186        | -               |
| 5.8480 | 10000 | 0.0193        | -               |
| 5.8772 | 10050 | 0.0176        | -               |
| 5.9064 | 10100 | 0.0187        | -               |
| 5.9357 | 10150 | 0.0193        | -               |
| 5.9649 | 10200 | 0.0199        | -               |
| 5.9942 | 10250 | 0.0169        | -               |
| 6.0234 | 10300 | 0.017         | -               |
| 6.0526 | 10350 | 0.0207        | -               |
| 6.0819 | 10400 | 0.0188        | -               |
| 6.1111 | 10450 | 0.018         | -               |
| 6.1404 | 10500 | 0.0184        | -               |
| 6.1696 | 10550 | 0.0153        | -               |
| 6.1988 | 10600 | 0.0173        | -               |
| 6.2281 | 10650 | 0.0172        | -               |
| 6.2573 | 10700 | 0.0188        | -               |
| 6.2865 | 10750 | 0.02          | -               |
| 6.3158 | 10800 | 0.0193        | -               |
| 6.3450 | 10850 | 0.0188        | -               |
| 6.3743 | 10900 | 0.0183        | -               |
| 6.4035 | 10950 | 0.0185        | -               |
| 6.4327 | 11000 | 0.0203        | -               |
| 6.4620 | 11050 | 0.018         | -               |
| 6.4912 | 11100 | 0.0184        | -               |
| 6.5205 | 11150 | 0.0182        | -               |
| 6.5497 | 11200 | 0.0173        | -               |
| 6.5789 | 11250 | 0.0173        | -               |
| 6.6082 | 11300 | 0.0189        | -               |
| 6.6374 | 11350 | 0.0167        | -               |
| 6.6667 | 11400 | 0.0169        | -               |
| 6.6959 | 11450 | 0.0171        | -               |
| 6.7251 | 11500 | 0.0174        | -               |
| 6.7544 | 11550 | 0.0169        | -               |
| 6.7836 | 11600 | 0.0193        | -               |
| 6.8129 | 11650 | 0.0184        | -               |
| 6.8421 | 11700 | 0.0175        | -               |
| 6.8713 | 11750 | 0.0173        | -               |
| 6.9006 | 11800 | 0.0146        | -               |
| 6.9298 | 11850 | 0.0163        | -               |
| 6.9591 | 11900 | 0.0173        | -               |
| 6.9883 | 11950 | 0.0196        | -               |
| 7.0175 | 12000 | 0.0188        | -               |
| 7.0468 | 12050 | 0.0182        | -               |
| 7.0760 | 12100 | 0.0168        | -               |
| 7.1053 | 12150 | 0.0169        | -               |
| 7.1345 | 12200 | 0.0164        | -               |
| 7.1637 | 12250 | 0.0159        | -               |
| 7.1930 | 12300 | 0.0187        | -               |
| 7.2222 | 12350 | 0.0197        | -               |
| 7.2515 | 12400 | 0.0186        | -               |
| 7.2807 | 12450 | 0.0163        | -               |
| 7.3099 | 12500 | 0.0178        | -               |
| 7.3392 | 12550 | 0.0184        | -               |
| 7.3684 | 12600 | 0.0184        | -               |
| 7.3977 | 12650 | 0.0177        | -               |
| 7.4269 | 12700 | 0.0157        | -               |
| 7.4561 | 12750 | 0.0184        | -               |
| 7.4854 | 12800 | 0.0184        | -               |
| 7.5146 | 12850 | 0.0182        | -               |
| 7.5439 | 12900 | 0.0182        | -               |
| 7.5731 | 12950 | 0.0169        | -               |
| 7.6023 | 13000 | 0.0182        | -               |
| 7.6316 | 13050 | 0.0156        | -               |
| 7.6608 | 13100 | 0.0173        | -               |
| 7.6901 | 13150 | 0.0159        | -               |
| 7.7193 | 13200 | 0.0167        | -               |
| 7.7485 | 13250 | 0.0175        | -               |
| 7.7778 | 13300 | 0.016         | -               |
| 7.8070 | 13350 | 0.0175        | -               |
| 7.8363 | 13400 | 0.0169        | -               |
| 7.8655 | 13450 | 0.0167        | -               |
| 7.8947 | 13500 | 0.0159        | -               |
| 7.9240 | 13550 | 0.0168        | -               |
| 7.9532 | 13600 | 0.0183        | -               |
| 7.9825 | 13650 | 0.0162        | -               |
| 8.0117 | 13700 | 0.0162        | -               |
| 8.0409 | 13750 | 0.017         | -               |
| 8.0702 | 13800 | 0.018         | -               |
| 8.0994 | 13850 | 0.0161        | -               |
| 8.1287 | 13900 | 0.0159        | -               |
| 8.1579 | 13950 | 0.0185        | -               |
| 8.1871 | 14000 | 0.017         | -               |
| 8.2164 | 14050 | 0.0167        | -               |
| 8.2456 | 14100 | 0.0154        | -               |
| 8.2749 | 14150 | 0.0166        | -               |
| 8.3041 | 14200 | 0.0173        | -               |
| 8.3333 | 14250 | 0.0156        | -               |
| 8.3626 | 14300 | 0.0175        | -               |
| 8.3918 | 14350 | 0.0144        | -               |
| 8.4211 | 14400 | 0.0198        | -               |
| 8.4503 | 14450 | 0.0184        | -               |
| 8.4795 | 14500 | 0.0168        | -               |
| 8.5088 | 14550 | 0.0183        | -               |
| 8.5380 | 14600 | 0.0175        | -               |
| 8.5673 | 14650 | 0.0155        | -               |
| 8.5965 | 14700 | 0.0168        | -               |
| 8.6257 | 14750 | 0.0179        | -               |
| 8.6550 | 14800 | 0.0162        | -               |
| 8.6842 | 14850 | 0.0181        | -               |
| 8.7135 | 14900 | 0.017         | -               |
| 8.7427 | 14950 | 0.0169        | -               |
| 8.7719 | 15000 | 0.0177        | -               |
| 8.8012 | 15050 | 0.0174        | -               |
| 8.8304 | 15100 | 0.015         | -               |
| 8.8596 | 15150 | 0.0159        | -               |
| 8.8889 | 15200 | 0.0191        | -               |
| 8.9181 | 15250 | 0.0168        | -               |
| 8.9474 | 15300 | 0.0147        | -               |
| 8.9766 | 15350 | 0.0166        | -               |
| 9.0058 | 15400 | 0.0163        | -               |
| 9.0351 | 15450 | 0.0156        | -               |
| 9.0643 | 15500 | 0.0171        | -               |
| 9.0936 | 15550 | 0.0168        | -               |
| 9.1228 | 15600 | 0.0174        | -               |
| 9.1520 | 15650 | 0.0152        | -               |
| 9.1813 | 15700 | 0.017         | -               |
| 9.2105 | 15750 | 0.0172        | -               |
| 9.2398 | 15800 | 0.0149        | -               |
| 9.2690 | 15850 | 0.0172        | -               |
| 9.2982 | 15900 | 0.0161        | -               |
| 9.3275 | 15950 | 0.0174        | -               |
| 9.3567 | 16000 | 0.0181        | -               |
| 9.3860 | 16050 | 0.0167        | -               |
| 9.4152 | 16100 | 0.0159        | -               |
| 9.4444 | 16150 | 0.0157        | -               |
| 9.4737 | 16200 | 0.0174        | -               |
| 9.5029 | 16250 | 0.0155        | -               |
| 9.5322 | 16300 | 0.0158        | -               |
| 9.5614 | 16350 | 0.0164        | -               |
| 9.5906 | 16400 | 0.0165        | -               |
| 9.6199 | 16450 | 0.0164        | -               |
| 9.6491 | 16500 | 0.0155        | -               |
| 9.6784 | 16550 | 0.0164        | -               |
| 9.7076 | 16600 | 0.016         | -               |
| 9.7368 | 16650 | 0.0154        | -               |
| 9.7661 | 16700 | 0.0171        | -               |
| 9.7953 | 16750 | 0.0173        | -               |
| 9.8246 | 16800 | 0.0158        | -               |
| 9.8538 | 16850 | 0.0169        | -               |
| 9.8830 | 16900 | 0.0163        | -               |
| 9.9123 | 16950 | 0.0177        | -               |
| 9.9415 | 17000 | 0.0167        | -               |
| 9.9708 | 17050 | 0.0172        | -               |
| 10.0   | 17100 | 0.0172        | -               |

### Framework Versions
- Python: 3.10.8
- SetFit: 1.1.2
- Sentence Transformers: 5.0.0
- Transformers: 4.54.1
- PyTorch: 2.7.1+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.4

## 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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