---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: Google Maps
- text: 'IN NEED OF OBEDIENCE CLASSES? '
- text: ' .modal-content '
- text: 'U Pere ris, AM sees FULLUW! \SfkE Ka £'' | '
- text: 'exclusively MAX FACTOR Beeiting new lipstick concept makes all others obsolete! '
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: 0.5003125
name: Accuracy
- type: precision
value: 0.0
name: Precision
- type: recall
value: 0.0
name: Recall
- type: f1
value: 0.0
name: F1
---
# 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:** 2 classes
### 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 |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| False | - 'Persistent'
- 'Forensic Contract'
- 'View Vendor List'
|
| True | - 'winming camp at Taj Deccan. Morning and evening batches. Start today. Become a champ. Monday to Friday till 3 Ist march '
- '您的反馈已记录，我们将努力改善您的浏览体验。'
- 'Ve ConcerrualL DESIGNER '
|
## Evaluation
### Metrics
| Label | Accuracy | Precision | Recall | F1 |
|:--------|:---------|:----------|:-------|:----|
| **all** | 0.5003 | 0.0 | 0.0 | 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("setfit_model_id")
# Run inference
preds = model("Google Maps")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 8.5055 | 706 |
| Label | Training Sample Count |
|:------|:----------------------|
| False | 6399 |
| True | 6401 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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
- run_name: PG-OCR-test-1
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.5 | - |
| 0.0016 | 50 | 0.5 | - |
| 0.0031 | 100 | 0.5 | - |
| 0.0047 | 150 | 0.5 | - |
| 0.0063 | 200 | 0.5 | - |
| 0.0078 | 250 | 0.5 | - |
| 0.0094 | 300 | 0.5 | - |
| 0.0109 | 350 | 0.5 | - |
| 0.0125 | 400 | 0.5 | - |
| 0.0141 | 450 | 0.5 | - |
| 0.0156 | 500 | 0.5 | - |
| 0.0172 | 550 | 0.5 | - |
| 0.0187 | 600 | 0.5 | - |
| 0.0203 | 650 | 0.5 | - |
| 0.0219 | 700 | 0.5 | - |
| 0.0234 | 750 | 0.5 | - |
| 0.025 | 800 | 0.5 | - |
| 0.0266 | 850 | 0.5 | - |
| 0.0281 | 900 | 0.5 | - |
| 0.0297 | 950 | 0.5 | - |
| 0.0312 | 1000 | 0.5 | - |
| 0.0328 | 1050 | 0.5 | - |
| 0.0344 | 1100 | 0.5 | - |
| 0.0359 | 1150 | 0.5 | - |
| 0.0375 | 1200 | 0.5 | - |
| 0.0391 | 1250 | 0.5 | - |
| 0.0406 | 1300 | 0.5 | - |
| 0.0422 | 1350 | 0.5 | - |
| 0.0437 | 1400 | 0.5 | - |
| 0.0453 | 1450 | 0.5 | - |
| 0.0469 | 1500 | 0.5 | - |
| 0.0484 | 1550 | 0.5 | - |
| 0.05 | 1600 | 0.5 | - |
| 0.0516 | 1650 | 0.5 | - |
| 0.0531 | 1700 | 0.5 | - |
| 0.0547 | 1750 | 0.5 | - |
| 0.0563 | 1800 | 0.5 | - |
| 0.0578 | 1850 | 0.5 | - |
| 0.0594 | 1900 | 0.5 | - |
| 0.0609 | 1950 | 0.5 | - |
| 0.0625 | 2000 | 0.5 | - |
| 0.0641 | 2050 | 0.5 | - |
| 0.0656 | 2100 | 0.5 | - |
| 0.0672 | 2150 | 0.5 | - |
| 0.0688 | 2200 | 0.5 | - |
| 0.0703 | 2250 | 0.5 | - |
| 0.0719 | 2300 | 0.5 | - |
| 0.0734 | 2350 | 0.5 | - |
| 0.075 | 2400 | 0.5 | - |
| 0.0766 | 2450 | 0.5 | - |
| 0.0781 | 2500 | 0.5 | - |
| 0.0797 | 2550 | 0.5 | - |
| 0.0813 | 2600 | 0.5 | - |
| 0.0828 | 2650 | 0.5 | - |
| 0.0844 | 2700 | 0.5 | - |
| 0.0859 | 2750 | 0.5 | - |
| 0.0875 | 2800 | 0.5 | - |
| 0.0891 | 2850 | 0.5 | - |
| 0.0906 | 2900 | 0.5 | - |
| 0.0922 | 2950 | 0.5 | - |
| 0.0938 | 3000 | 0.5 | - |
| 0.0953 | 3050 | 0.5 | - |
| 0.0969 | 3100 | 0.5 | - |
| 0.0984 | 3150 | 0.5 | - |
| 0.1 | 3200 | 0.5 | - |
| 0.1016 | 3250 | 0.5 | - |
| 0.1031 | 3300 | 0.5 | - |
| 0.1047 | 3350 | 0.5 | - |
| 0.1062 | 3400 | 0.5 | - |
| 0.1078 | 3450 | 0.5 | - |
| 0.1094 | 3500 | 0.5 | - |
| 0.1109 | 3550 | 0.5 | - |
| 0.1125 | 3600 | 0.5 | - |
| 0.1141 | 3650 | 0.5 | - |
| 0.1156 | 3700 | 0.5 | - |
| 0.1172 | 3750 | 0.5 | - |
| 0.1187 | 3800 | 0.5 | - |
| 0.1203 | 3850 | 0.5 | - |
| 0.1219 | 3900 | 0.5 | - |
| 0.1234 | 3950 | 0.5 | - |
| 0.125 | 4000 | 0.5 | - |
| 0.1266 | 4050 | 0.5 | - |
| 0.1281 | 4100 | 0.5 | - |
| 0.1297 | 4150 | 0.5 | - |
| 0.1313 | 4200 | 0.5 | - |
| 0.1328 | 4250 | 0.5 | - |
| 0.1344 | 4300 | 0.5 | - |
| 0.1359 | 4350 | 0.5 | - |
| 0.1375 | 4400 | 0.5 | - |
| 0.1391 | 4450 | 0.5 | - |
| 0.1406 | 4500 | 0.5 | - |
| 0.1422 | 4550 | 0.5 | - |
| 0.1437 | 4600 | 0.5 | - |
| 0.1453 | 4650 | 0.5 | - |
| 0.1469 | 4700 | 0.5 | - |
| 0.1484 | 4750 | 0.5 | - |
| 0.15 | 4800 | 0.5 | - |
| 0.1516 | 4850 | 0.5 | - |
| 0.1531 | 4900 | 0.5 | - |
| 0.1547 | 4950 | 0.5 | - |
| 0.1562 | 5000 | 0.5 | 0.5 |
| 0.1578 | 5050 | 0.5 | - |
| 0.1594 | 5100 | 0.5 | - |
| 0.1609 | 5150 | 0.5 | - |
| 0.1625 | 5200 | 0.5 | - |
| 0.1641 | 5250 | 0.5 | - |
| 0.1656 | 5300 | 0.5 | - |
| 0.1672 | 5350 | 0.5 | - |
| 0.1688 | 5400 | 0.5 | - |
| 0.1703 | 5450 | 0.5 | - |
| 0.1719 | 5500 | 0.5 | - |
| 0.1734 | 5550 | 0.5 | - |
| 0.175 | 5600 | 0.5 | - |
| 0.1766 | 5650 | 0.5 | - |
| 0.1781 | 5700 | 0.5 | - |
| 0.1797 | 5750 | 0.5 | - |
| 0.1812 | 5800 | 0.5 | - |
| 0.1828 | 5850 | 0.5 | - |
| 0.1844 | 5900 | 0.5 | - |
| 0.1859 | 5950 | 0.5 | - |
| 0.1875 | 6000 | 0.5 | - |
| 0.1891 | 6050 | 0.5 | - |
| 0.1906 | 6100 | 0.5 | - |
| 0.1922 | 6150 | 0.5 | - |
| 0.1938 | 6200 | 0.5 | - |
| 0.1953 | 6250 | 0.5 | - |
| 0.1969 | 6300 | 0.5 | - |
| 0.1984 | 6350 | 0.5 | - |
| 0.2 | 6400 | 0.5 | - |
| 0.2016 | 6450 | 0.5 | - |
| 0.2031 | 6500 | 0.5 | - |
| 0.2047 | 6550 | 0.5 | - |
| 0.2062 | 6600 | 0.5 | - |
| 0.2078 | 6650 | 0.5 | - |
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| 0.2109 | 6750 | 0.5 | - |
| 0.2125 | 6800 | 0.5 | - |
| 0.2141 | 6850 | 0.5 | - |
| 0.2156 | 6900 | 0.5 | - |
| 0.2172 | 6950 | 0.5 | - |
| 0.2188 | 7000 | 0.5 | - |
| 0.2203 | 7050 | 0.5 | - |
| 0.2219 | 7100 | 0.5 | - |
| 0.2234 | 7150 | 0.5 | - |
| 0.225 | 7200 | 0.5 | - |
| 0.2266 | 7250 | 0.5 | - |
| 0.2281 | 7300 | 0.5 | - |
| 0.2297 | 7350 | 0.5 | - |
| 0.2313 | 7400 | 0.5 | - |
| 0.2328 | 7450 | 0.5 | - |
| 0.2344 | 7500 | 0.5 | - |
| 0.2359 | 7550 | 0.5 | - |
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| 0.2391 | 7650 | 0.5 | - |
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| 0.2453 | 7850 | 0.5 | - |
| 0.2469 | 7900 | 0.5 | - |
| 0.2484 | 7950 | 0.5 | - |
| 0.25 | 8000 | 0.5 | - |
| 0.2516 | 8050 | 0.5 | - |
| 0.2531 | 8100 | 0.5 | - |
| 0.2547 | 8150 | 0.5 | - |
| 0.2562 | 8200 | 0.5 | - |
| 0.2578 | 8250 | 0.5 | - |
| 0.2594 | 8300 | 0.5 | - |
| 0.2609 | 8350 | 0.5 | - |
| 0.2625 | 8400 | 0.5 | - |
| 0.2641 | 8450 | 0.5 | - |
| 0.2656 | 8500 | 0.5 | - |
| 0.2672 | 8550 | 0.5 | - |
| 0.2687 | 8600 | 0.5 | - |
| 0.2703 | 8650 | 0.5 | - |
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| 0.275 | 8800 | 0.5 | - |
| 0.2766 | 8850 | 0.5 | - |
| 0.2781 | 8900 | 0.5 | - |
| 0.2797 | 8950 | 0.5 | - |
| 0.2812 | 9000 | 0.5 | - |
| 0.2828 | 9050 | 0.5 | - |
| 0.2844 | 9100 | 0.5 | - |
| 0.2859 | 9150 | 0.5 | - |
| 0.2875 | 9200 | 0.5 | - |
| 0.2891 | 9250 | 0.5 | - |
| 0.2906 | 9300 | 0.5 | - |
| 0.2922 | 9350 | 0.5 | - |
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| 0.3 | 9600 | 0.5 | - |
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| 0.3109 | 9950 | 0.5 | - |
| 0.3125 | 10000 | 0.5 | 0.5 |
| 0.3141 | 10050 | 0.5 | - |
| 0.3156 | 10100 | 0.5 | - |
| 0.3172 | 10150 | 0.5 | - |
| 0.3187 | 10200 | 0.5 | - |
| 0.3203 | 10250 | 0.5 | - |
| 0.3219 | 10300 | 0.5 | - |
| 0.3234 | 10350 | 0.5 | - |
| 0.325 | 10400 | 0.5 | - |
| 0.3266 | 10450 | 0.5 | - |
| 0.3281 | 10500 | 0.5 | - |
| 0.3297 | 10550 | 0.5 | - |
| 0.3312 | 10600 | 0.5 | - |
| 0.3328 | 10650 | 0.5 | - |
| 0.3344 | 10700 | 0.5 | - |
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| 0.3375 | 10800 | 0.5 | - |
| 0.3391 | 10850 | 0.5 | - |
| 0.3406 | 10900 | 0.5 | - |
| 0.3422 | 10950 | 0.5 | - |
| 0.3438 | 11000 | 0.5 | - |
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| 0.35 | 11200 | 0.5 | - |
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| 0.3563 | 11400 | 0.5 | - |
| 0.3578 | 11450 | 0.5 | - |
| 0.3594 | 11500 | 0.5 | - |
| 0.3609 | 11550 | 0.5 | - |
| 0.3625 | 11600 | 0.5 | - |
| 0.3641 | 11650 | 0.5 | - |
| 0.3656 | 11700 | 0.5 | - |
| 0.3672 | 11750 | 0.5 | - |
| 0.3688 | 11800 | 0.5 | - |
| 0.3703 | 11850 | 0.5 | - |
| 0.3719 | 11900 | 0.5 | - |
| 0.3734 | 11950 | 0.5 | - |
| 0.375 | 12000 | 0.5 | - |
| 0.3766 | 12050 | 0.5 | - |
| 0.3781 | 12100 | 0.5 | - |
| 0.3797 | 12150 | 0.5 | - |
| 0.3812 | 12200 | 0.5 | - |
| 0.3828 | 12250 | 0.5 | - |
| 0.3844 | 12300 | 0.5 | - |
| 0.3859 | 12350 | 0.5 | - |
| 0.3875 | 12400 | 0.5 | - |
| 0.3891 | 12450 | 0.5 | - |
| 0.3906 | 12500 | 0.5 | - |
| 0.3922 | 12550 | 0.5 | - |
| 0.3937 | 12600 | 0.5 | - |
| 0.3953 | 12650 | 0.5 | - |
| 0.3969 | 12700 | 0.5 | - |
| 0.3984 | 12750 | 0.5 | - |
| 0.4 | 12800 | 0.5 | - |
| 0.4016 | 12850 | 0.5 | - |
| 0.4031 | 12900 | 0.5 | - |
| 0.4047 | 12950 | 0.5 | - |
| 0.4062 | 13000 | 0.5 | - |
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| 0.4094 | 13100 | 0.5 | - |
| 0.4109 | 13150 | 0.5 | - |
| 0.4125 | 13200 | 0.5 | - |
| 0.4141 | 13250 | 0.5 | - |
| 0.4156 | 13300 | 0.5 | - |
| 0.4172 | 13350 | 0.5 | - |
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| 0.425 | 13600 | 0.5 | - |
| 0.4266 | 13650 | 0.5 | - |
| 0.4281 | 13700 | 0.5 | - |
| 0.4297 | 13750 | 0.5 | - |
| 0.4313 | 13800 | 0.5 | - |
| 0.4328 | 13850 | 0.5 | - |
| 0.4344 | 13900 | 0.5 | - |
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| 0.4375 | 14000 | 0.5 | - |
| 0.4391 | 14050 | 0.5 | - |
| 0.4406 | 14100 | 0.5 | - |
| 0.4422 | 14150 | 0.5 | - |
| 0.4437 | 14200 | 0.5 | - |
| 0.4453 | 14250 | 0.5 | - |
| 0.4469 | 14300 | 0.5 | - |
| 0.4484 | 14350 | 0.5 | - |
| 0.45 | 14400 | 0.5 | - |
| 0.4516 | 14450 | 0.5 | - |
| 0.4531 | 14500 | 0.5 | - |
| 0.4547 | 14550 | 0.5 | - |
| 0.4562 | 14600 | 0.5 | - |
| 0.4578 | 14650 | 0.5 | - |
| 0.4594 | 14700 | 0.5 | - |
| 0.4609 | 14750 | 0.5 | - |
| 0.4625 | 14800 | 0.5 | - |
| 0.4641 | 14850 | 0.5 | - |
| 0.4656 | 14900 | 0.5 | - |
| 0.4672 | 14950 | 0.5 | - |
| 0.4688 | 15000 | 0.5 | 0.5 |
| 0.4703 | 15050 | 0.5 | - |
| 0.4719 | 15100 | 0.5 | - |
| 0.4734 | 15150 | 0.5 | - |
| 0.475 | 15200 | 0.5 | - |
| 0.4766 | 15250 | 0.5 | - |
| 0.4781 | 15300 | 0.5 | - |
| 0.4797 | 15350 | 0.5 | - |
| 0.4813 | 15400 | 0.5 | - |
| 0.4828 | 15450 | 0.5 | - |
| 0.4844 | 15500 | 0.5 | - |
| 0.4859 | 15550 | 0.5 | - |
| 0.4875 | 15600 | 0.5 | - |
| 0.4891 | 15650 | 0.5 | - |
| 0.4906 | 15700 | 0.5 | - |
| 0.4922 | 15750 | 0.5 | - |
| 0.4938 | 15800 | 0.5 | - |
| 0.4953 | 15850 | 0.5 | - |
| 0.4969 | 15900 | 0.5 | - |
| 0.4984 | 15950 | 0.5 | - |
| 0.5 | 16000 | 0.5 | - |
| 0.5016 | 16050 | 0.5 | - |
| 0.5031 | 16100 | 0.5 | - |
| 0.5047 | 16150 | 0.5 | - |
| 0.5062 | 16200 | 0.5 | - |
| 0.5078 | 16250 | 0.5 | - |
| 0.5094 | 16300 | 0.5 | - |
| 0.5109 | 16350 | 0.5 | - |
| 0.5125 | 16400 | 0.5 | - |
| 0.5141 | 16450 | 0.5 | - |
| 0.5156 | 16500 | 0.5 | - |
| 0.5172 | 16550 | 0.5 | - |
| 0.5188 | 16600 | 0.5 | - |
| 0.5203 | 16650 | 0.5 | - |
| 0.5219 | 16700 | 0.5 | - |
| 0.5234 | 16750 | 0.5 | - |
| 0.525 | 16800 | 0.5 | - |
| 0.5266 | 16850 | 0.5 | - |
| 0.5281 | 16900 | 0.5 | - |
| 0.5297 | 16950 | 0.5 | - |
| 0.5312 | 17000 | 0.5 | - |
| 0.5328 | 17050 | 0.5 | - |
| 0.5344 | 17100 | 0.5 | - |
| 0.5359 | 17150 | 0.5 | - |
| 0.5375 | 17200 | 0.5 | - |
| 0.5391 | 17250 | 0.5 | - |
| 0.5406 | 17300 | 0.5 | - |
| 0.5422 | 17350 | 0.5 | - |
| 0.5437 | 17400 | 0.5 | - |
| 0.5453 | 17450 | 0.5 | - |
| 0.5469 | 17500 | 0.5 | - |
| 0.5484 | 17550 | 0.5 | - |
| 0.55 | 17600 | 0.5 | - |
| 0.5516 | 17650 | 0.5 | - |
| 0.5531 | 17700 | 0.5 | - |
| 0.5547 | 17750 | 0.5 | - |
| 0.5563 | 17800 | 0.5 | - |
| 0.5578 | 17850 | 0.5 | - |
| 0.5594 | 17900 | 0.5 | - |
| 0.5609 | 17950 | 0.5 | - |
| 0.5625 | 18000 | 0.5 | - |
| 0.5641 | 18050 | 0.5 | - |
| 0.5656 | 18100 | 0.5 | - |
| 0.5672 | 18150 | 0.5 | - |
| 0.5687 | 18200 | 0.5 | - |
| 0.5703 | 18250 | 0.5 | - |
| 0.5719 | 18300 | 0.5 | - |
| 0.5734 | 18350 | 0.5 | - |
| 0.575 | 18400 | 0.5 | - |
| 0.5766 | 18450 | 0.5 | - |
| 0.5781 | 18500 | 0.5 | - |
| 0.5797 | 18550 | 0.5 | - |
| 0.5813 | 18600 | 0.5 | - |
| 0.5828 | 18650 | 0.5 | - |
| 0.5844 | 18700 | 0.5 | - |
| 0.5859 | 18750 | 0.5 | - |
| 0.5875 | 18800 | 0.5 | - |
| 0.5891 | 18850 | 0.5 | - |
| 0.5906 | 18900 | 0.5 | - |
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| 0.5938 | 19000 | 0.5 | - |
| 0.5953 | 19050 | 0.5 | - |
| 0.5969 | 19100 | 0.5 | - |
| 0.5984 | 19150 | 0.5 | - |
| 0.6 | 19200 | 0.5 | - |
| 0.6016 | 19250 | 0.5 | - |
| 0.6031 | 19300 | 0.5 | - |
| 0.6047 | 19350 | 0.5 | - |
| 0.6062 | 19400 | 0.5 | - |
| 0.6078 | 19450 | 0.5 | - |
| 0.6094 | 19500 | 0.5 | - |
| 0.6109 | 19550 | 0.5 | - |
| 0.6125 | 19600 | 0.5 | - |
| 0.6141 | 19650 | 0.5 | - |
| 0.6156 | 19700 | 0.5 | - |
| 0.6172 | 19750 | 0.5 | - |
| 0.6188 | 19800 | 0.5 | - |
| 0.6203 | 19850 | 0.5 | - |
| 0.6219 | 19900 | 0.5 | - |
| 0.6234 | 19950 | 0.5 | - |
| 0.625 | 20000 | 0.5 | 0.5 |
| 0.6266 | 20050 | 0.5 | - |
| 0.6281 | 20100 | 0.5 | - |
| 0.6297 | 20150 | 0.5 | - |
| 0.6312 | 20200 | 0.5 | - |
| 0.6328 | 20250 | 0.5 | - |
| 0.6344 | 20300 | 0.5 | - |
| 0.6359 | 20350 | 0.5 | - |
| 0.6375 | 20400 | 0.5 | - |
| 0.6391 | 20450 | 0.5 | - |
| 0.6406 | 20500 | 0.5 | - |
| 0.6422 | 20550 | 0.5 | - |
| 0.6438 | 20600 | 0.5 | - |
| 0.6453 | 20650 | 0.5 | - |
| 0.6469 | 20700 | 0.5 | - |
| 0.6484 | 20750 | 0.5 | - |
| 0.65 | 20800 | 0.5 | - |
| 0.6516 | 20850 | 0.5 | - |
| 0.6531 | 20900 | 0.5 | - |
| 0.6547 | 20950 | 0.5 | - |
| 0.6562 | 21000 | 0.5 | - |
| 0.6578 | 21050 | 0.5 | - |
| 0.6594 | 21100 | 0.5 | - |
| 0.6609 | 21150 | 0.5 | - |
| 0.6625 | 21200 | 0.5 | - |
| 0.6641 | 21250 | 0.5 | - |
| 0.6656 | 21300 | 0.5 | - |
| 0.6672 | 21350 | 0.5 | - |
| 0.6687 | 21400 | 0.5 | - |
| 0.6703 | 21450 | 0.5 | - |
| 0.6719 | 21500 | 0.5 | - |
| 0.6734 | 21550 | 0.5 | - |
| 0.675 | 21600 | 0.5 | - |
| 0.6766 | 21650 | 0.5 | - |
| 0.6781 | 21700 | 0.5 | - |
| 0.6797 | 21750 | 0.5 | - |
| 0.6813 | 21800 | 0.5 | - |
| 0.6828 | 21850 | 0.5 | - |
| 0.6844 | 21900 | 0.5 | - |
| 0.6859 | 21950 | 0.5 | - |
| 0.6875 | 22000 | 0.5 | - |
| 0.6891 | 22050 | 0.5 | - |
| 0.6906 | 22100 | 0.5 | - |
| 0.6922 | 22150 | 0.5 | - |
| 0.6937 | 22200 | 0.5 | - |
| 0.6953 | 22250 | 0.5 | - |
| 0.6969 | 22300 | 0.5 | - |
| 0.6984 | 22350 | 0.5 | - |
| 0.7 | 22400 | 0.5 | - |
| 0.7016 | 22450 | 0.5 | - |
| 0.7031 | 22500 | 0.5 | - |
| 0.7047 | 22550 | 0.5 | - |
| 0.7063 | 22600 | 0.5 | - |
| 0.7078 | 22650 | 0.5 | - |
| 0.7094 | 22700 | 0.5 | - |
| 0.7109 | 22750 | 0.5 | - |
| 0.7125 | 22800 | 0.5 | - |
| 0.7141 | 22850 | 0.5 | - |
| 0.7156 | 22900 | 0.5 | - |
| 0.7172 | 22950 | 0.5 | - |
| 0.7188 | 23000 | 0.5 | - |
| 0.7203 | 23050 | 0.5 | - |
| 0.7219 | 23100 | 0.5 | - |
| 0.7234 | 23150 | 0.5 | - |
| 0.725 | 23200 | 0.5 | - |
| 0.7266 | 23250 | 0.5 | - |
| 0.7281 | 23300 | 0.5 | - |
| 0.7297 | 23350 | 0.5 | - |
| 0.7312 | 23400 | 0.5 | - |
| 0.7328 | 23450 | 0.5 | - |
| 0.7344 | 23500 | 0.5 | - |
| 0.7359 | 23550 | 0.5 | - |
| 0.7375 | 23600 | 0.5 | - |
| 0.7391 | 23650 | 0.5 | - |
| 0.7406 | 23700 | 0.5 | - |
| 0.7422 | 23750 | 0.5 | - |
| 0.7438 | 23800 | 0.5 | - |
| 0.7453 | 23850 | 0.5 | - |
| 0.7469 | 23900 | 0.5 | - |
| 0.7484 | 23950 | 0.5 | - |
| 0.75 | 24000 | 0.5 | - |
| 0.7516 | 24050 | 0.5 | - |
| 0.7531 | 24100 | 0.5 | - |
| 0.7547 | 24150 | 0.5 | - |
| 0.7562 | 24200 | 0.5 | - |
| 0.7578 | 24250 | 0.5 | - |
| 0.7594 | 24300 | 0.5 | - |
| 0.7609 | 24350 | 0.5 | - |
| 0.7625 | 24400 | 0.5 | - |
| 0.7641 | 24450 | 0.5 | - |
| 0.7656 | 24500 | 0.5 | - |
| 0.7672 | 24550 | 0.5 | - |
| 0.7688 | 24600 | 0.5 | - |
| 0.7703 | 24650 | 0.5 | - |
| 0.7719 | 24700 | 0.5 | - |
| 0.7734 | 24750 | 0.5 | - |
| 0.775 | 24800 | 0.5 | - |
| 0.7766 | 24850 | 0.5 | - |
| 0.7781 | 24900 | 0.5 | - |
| 0.7797 | 24950 | 0.5 | - |
| 0.7812 | 25000 | 0.5 | 0.5 |
| 0.7828 | 25050 | 0.5 | - |
| 0.7844 | 25100 | 0.5 | - |
| 0.7859 | 25150 | 0.5 | - |
| 0.7875 | 25200 | 0.5 | - |
| 0.7891 | 25250 | 0.5 | - |
| 0.7906 | 25300 | 0.5 | - |
| 0.7922 | 25350 | 0.5 | - |
| 0.7937 | 25400 | 0.5 | - |
| 0.7953 | 25450 | 0.5 | - |
| 0.7969 | 25500 | 0.5 | - |
| 0.7984 | 25550 | 0.5 | - |
| 0.8 | 25600 | 0.5 | - |
| 0.8016 | 25650 | 0.5 | - |
| 0.8031 | 25700 | 0.5 | - |
| 0.8047 | 25750 | 0.5 | - |
| 0.8063 | 25800 | 0.5 | - |
| 0.8078 | 25850 | 0.5 | - |
| 0.8094 | 25900 | 0.5 | - |
| 0.8109 | 25950 | 0.5 | - |
| 0.8125 | 26000 | 0.5 | - |
| 0.8141 | 26050 | 0.5 | - |
| 0.8156 | 26100 | 0.5 | - |
| 0.8172 | 26150 | 0.5 | - |
| 0.8187 | 26200 | 0.5 | - |
| 0.8203 | 26250 | 0.5 | - |
| 0.8219 | 26300 | 0.5 | - |
| 0.8234 | 26350 | 0.5 | - |
| 0.825 | 26400 | 0.5 | - |
| 0.8266 | 26450 | 0.5 | - |
| 0.8281 | 26500 | 0.5 | - |
| 0.8297 | 26550 | 0.5 | - |
| 0.8313 | 26600 | 0.5 | - |
| 0.8328 | 26650 | 0.5 | - |
| 0.8344 | 26700 | 0.5 | - |
| 0.8359 | 26750 | 0.5 | - |
| 0.8375 | 26800 | 0.5 | - |
| 0.8391 | 26850 | 0.5 | - |
| 0.8406 | 26900 | 0.5 | - |
| 0.8422 | 26950 | 0.5 | - |
| 0.8438 | 27000 | 0.5 | - |
| 0.8453 | 27050 | 0.5 | - |
| 0.8469 | 27100 | 0.5 | - |
| 0.8484 | 27150 | 0.5 | - |
| 0.85 | 27200 | 0.5 | - |
| 0.8516 | 27250 | 0.5 | - |
| 0.8531 | 27300 | 0.5 | - |
| 0.8547 | 27350 | 0.5 | - |
| 0.8562 | 27400 | 0.5 | - |
| 0.8578 | 27450 | 0.5 | - |
| 0.8594 | 27500 | 0.5 | - |
| 0.8609 | 27550 | 0.5 | - |
| 0.8625 | 27600 | 0.5 | - |
| 0.8641 | 27650 | 0.5 | - |
| 0.8656 | 27700 | 0.5 | - |
| 0.8672 | 27750 | 0.5 | - |
| 0.8688 | 27800 | 0.5 | - |
| 0.8703 | 27850 | 0.5 | - |
| 0.8719 | 27900 | 0.5 | - |
| 0.8734 | 27950 | 0.5 | - |
| 0.875 | 28000 | 0.5 | - |
| 0.8766 | 28050 | 0.5 | - |
| 0.8781 | 28100 | 0.5 | - |
| 0.8797 | 28150 | 0.5 | - |
| 0.8812 | 28200 | 0.5 | - |
| 0.8828 | 28250 | 0.5 | - |
| 0.8844 | 28300 | 0.5 | - |
| 0.8859 | 28350 | 0.5 | - |
| 0.8875 | 28400 | 0.5 | - |
| 0.8891 | 28450 | 0.5 | - |
| 0.8906 | 28500 | 0.5 | - |
| 0.8922 | 28550 | 0.5 | - |
| 0.8938 | 28600 | 0.5 | - |
| 0.8953 | 28650 | 0.5 | - |
| 0.8969 | 28700 | 0.5 | - |
| 0.8984 | 28750 | 0.5 | - |
| 0.9 | 28800 | 0.5 | - |
| 0.9016 | 28850 | 0.5 | - |
| 0.9031 | 28900 | 0.5 | - |
| 0.9047 | 28950 | 0.5 | - |
| 0.9062 | 29000 | 0.5 | - |
| 0.9078 | 29050 | 0.5 | - |
| 0.9094 | 29100 | 0.5 | - |
| 0.9109 | 29150 | 0.5 | - |
| 0.9125 | 29200 | 0.5 | - |
| 0.9141 | 29250 | 0.5 | - |
| 0.9156 | 29300 | 0.5 | - |
| 0.9172 | 29350 | 0.5 | - |
| 0.9187 | 29400 | 0.5 | - |
| 0.9203 | 29450 | 0.5 | - |
| 0.9219 | 29500 | 0.5 | - |
| 0.9234 | 29550 | 0.5 | - |
| 0.925 | 29600 | 0.5 | - |
| 0.9266 | 29650 | 0.5 | - |
| 0.9281 | 29700 | 0.5 | - |
| 0.9297 | 29750 | 0.5 | - |
| 0.9313 | 29800 | 0.5 | - |
| 0.9328 | 29850 | 0.5 | - |
| 0.9344 | 29900 | 0.5 | - |
| 0.9359 | 29950 | 0.5 | - |
| 0.9375 | 30000 | 0.5 | 0.5 |
| 0.9391 | 30050 | 0.5 | - |
| 0.9406 | 30100 | 0.5 | - |
| 0.9422 | 30150 | 0.5 | - |
| 0.9437 | 30200 | 0.5 | - |
| 0.9453 | 30250 | 0.5 | - |
| 0.9469 | 30300 | 0.5 | - |
| 0.9484 | 30350 | 0.5 | - |
| 0.95 | 30400 | 0.5 | - |
| 0.9516 | 30450 | 0.5 | - |
| 0.9531 | 30500 | 0.5 | - |
| 0.9547 | 30550 | 0.5 | - |
| 0.9563 | 30600 | 0.5 | - |
| 0.9578 | 30650 | 0.5 | - |
| 0.9594 | 30700 | 0.5 | - |
| 0.9609 | 30750 | 0.5 | - |
| 0.9625 | 30800 | 0.5 | - |
| 0.9641 | 30850 | 0.5 | - |
| 0.9656 | 30900 | 0.5 | - |
| 0.9672 | 30950 | 0.5 | - |
| 0.9688 | 31000 | 0.5 | - |
| 0.9703 | 31050 | 0.5 | - |
| 0.9719 | 31100 | 0.5 | - |
| 0.9734 | 31150 | 0.5 | - |
| 0.975 | 31200 | 0.5 | - |
| 0.9766 | 31250 | 0.5 | - |
| 0.9781 | 31300 | 0.5 | - |
| 0.9797 | 31350 | 0.5 | - |
| 0.9812 | 31400 | 0.5 | - |
| 0.9828 | 31450 | 0.5 | - |
| 0.9844 | 31500 | 0.5 | - |
| 0.9859 | 31550 | 0.5 | - |
| 0.9875 | 31600 | 0.5 | - |
| 0.9891 | 31650 | 0.5 | - |
| 0.9906 | 31700 | 0.5 | - |
| 0.9922 | 31750 | 0.5 | - |
| 0.9938 | 31800 | 0.5 | - |
| 0.9953 | 31850 | 0.5 | - |
| 0.9969 | 31900 | 0.5 | - |
| 0.9984 | 31950 | 0.5 | - |
| 1.0 | 32000 | 0.5 | - |
### Framework Versions
- Python: 3.11.0
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.16.1
- 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}
}
```