---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: It doesn't depend on hi-teck evangelism.
- text: But in the all region we see gender unequal; in 2000 boys have education often
then girls on 15 millions.
- text: There is opinion, that universities should have equal amount of male and female
students in every subject in society.
- text: A building's style may say a lot about its history.
- text: Manufactured goods by rail is the same amount as by road, Machinery transported
by road has minimal percent in second chart.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.592741935483871
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 5 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 |
|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Copying expression | - 'What is needed to improve the situation with widespread using of gadjets is definite action should be encouraged and promoted by means of avoiding them.'
- 'Inside every of us are our passions.'
- 'The number of 15-59 year old people will increase for 11% but the number of 0-14 will fall and become 37%.'
|
| Synonyms | - 'But some persons consider that the institutes should accept the equal amount of girls and boys in every faculty.'
- 'Nowadays problem of ecology and environment is rather acute and many people are alarmed by it.'
- 'The amount of people over 65 was rising between 1940 and the end of 1970s.'
|
| Tense semantics | - 'After that the figure uncreases dramatically from 180 billions in 2009 to approximately 279 billions in 2011.'
- 'On the contrary, in 2014 the UK book market demonstrate minimum income, only 2,6 and 1,8 billion dollars for print book and eBook, correspondely.'
- 'It is not clear, what it is depends on, but after the higest point in 42% in Japan the percentage get down to 30%.'
|
| Word form transmission | - 'A lot of people from music and cinema industry lose money due to somebody sends pirate copies to the internet.'
- 'The deal was worth $2 billions .'
- 'According to the projections numbers of people in the age of 15-60 years will show a considerable increase in 2050 by 11 per cent, such as people aged 60 and more years by 2,1 percent.'
|
| Transliteration | - 'According to the statistic a lot of people with some horible diseases can get cvalificate help only in Japan.'
- "It doesn't depend on hi-teck evangelism."
- 'GMO technologies are believed to be dangerous.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5927 |
## 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("Zlovoblachko/L1-classifier")
# Run inference
preds = model("It doesn't depend on hi-teck evangelism.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 2 | 20.788 | 54 |
| Label | Training Sample Count |
|:-----------------------|:----------------------|
| Synonyms | 91 |
| Copying expression | 55 |
| Tense semantics | 57 |
| Word form transmission | 32 |
| Transliteration | 15 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (15, 15)
- max_steps: -1
- sampling_strategy: oversampling
- 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0043 | 1 | 0.3438 | - |
| 0.0342 | 50 | 0.2906 | - |
| 0.0685 | 100 | 0.2761 | - |
| 0.1027 | 150 | 0.2696 | - |
| 0.1370 | 200 | 0.2381 | - |
| 0.1712 | 250 | 0.2542 | - |
| 0.2055 | 300 | 0.1781 | - |
| 0.2397 | 350 | 0.2067 | - |
| 0.2740 | 400 | 0.222 | - |
| 0.3082 | 450 | 0.2372 | - |
| 0.3425 | 500 | 0.193 | - |
| 0.3767 | 550 | 0.2399 | - |
| 0.4110 | 600 | 0.1712 | - |
| 0.4452 | 650 | 0.1697 | - |
| 0.4795 | 700 | 0.1507 | - |
| 0.5137 | 750 | 0.0947 | - |
| 0.5479 | 800 | 0.0722 | - |
| 0.5822 | 850 | 0.0975 | - |
| 0.6164 | 900 | 0.035 | - |
| 0.6507 | 950 | 0.0114 | - |
| 0.6849 | 1000 | 0.0332 | - |
| 0.7192 | 1050 | 0.0274 | - |
| 0.7534 | 1100 | 0.0126 | - |
| 0.7877 | 1150 | 0.0267 | - |
| 0.8219 | 1200 | 0.0194 | - |
| 0.8562 | 1250 | 0.0206 | - |
| 0.8904 | 1300 | 0.0228 | - |
| 0.9247 | 1350 | 0.0076 | - |
| 0.9589 | 1400 | 0.0342 | - |
| 0.9932 | 1450 | 0.0252 | - |
| 1.0274 | 1500 | 0.0164 | - |
| 1.0616 | 1550 | 0.0049 | - |
| 1.0959 | 1600 | 0.0043 | - |
| 1.1301 | 1650 | 0.0114 | - |
| 1.1644 | 1700 | 0.03 | - |
| 1.1986 | 1750 | 0.0026 | - |
| 1.2329 | 1800 | 0.0012 | - |
| 1.2671 | 1850 | 0.0073 | - |
| 1.3014 | 1900 | 0.0146 | - |
| 1.3356 | 1950 | 0.001 | - |
| 1.3699 | 2000 | 0.0088 | - |
| 1.4041 | 2050 | 0.0031 | - |
| 1.4384 | 2100 | 0.0125 | - |
| 1.4726 | 2150 | 0.0357 | - |
| 1.5068 | 2200 | 0.0186 | - |
| 1.5411 | 2250 | 0.0178 | - |
| 1.5753 | 2300 | 0.0071 | - |
| 1.6096 | 2350 | 0.0186 | - |
| 1.6438 | 2400 | 0.0077 | - |
| 1.6781 | 2450 | 0.0183 | - |
| 1.7123 | 2500 | 0.0007 | - |
| 1.7466 | 2550 | 0.0007 | - |
| 1.7808 | 2600 | 0.0052 | - |
| 1.8151 | 2650 | 0.0077 | - |
| 1.8493 | 2700 | 0.0421 | - |
| 1.8836 | 2750 | 0.0272 | - |
| 1.9178 | 2800 | 0.0144 | - |
| 1.9521 | 2850 | 0.0038 | - |
| 1.9863 | 2900 | 0.0043 | - |
| 2.0205 | 2950 | 0.0187 | - |
| 2.0548 | 3000 | 0.0075 | - |
| 2.0890 | 3050 | 0.0151 | - |
| 2.1233 | 3100 | 0.0114 | - |
| 2.1575 | 3150 | 0.0022 | - |
| 2.1918 | 3200 | 0.0007 | - |
| 2.2260 | 3250 | 0.0196 | - |
| 2.2603 | 3300 | 0.0266 | - |
| 2.2945 | 3350 | 0.0139 | - |
| 2.3288 | 3400 | 0.0169 | - |
| 2.3630 | 3450 | 0.0124 | - |
| 2.3973 | 3500 | 0.0018 | - |
| 2.4315 | 3550 | 0.0242 | - |
| 2.4658 | 3600 | 0.0402 | - |
| 2.5 | 3650 | 0.0015 | - |
| 2.5342 | 3700 | 0.0042 | - |
| 2.5685 | 3750 | 0.0437 | - |
| 2.6027 | 3800 | 0.006 | - |
| 2.6370 | 3850 | 0.0005 | - |
| 2.6712 | 3900 | 0.0118 | - |
| 2.7055 | 3950 | 0.0166 | - |
| 2.7397 | 4000 | 0.025 | - |
| 2.7740 | 4050 | 0.0167 | - |
| 2.8082 | 4100 | 0.0285 | - |
| 2.8425 | 4150 | 0.0048 | - |
| 2.8767 | 4200 | 0.0149 | - |
| 2.9110 | 4250 | 0.0078 | - |
| 2.9452 | 4300 | 0.0097 | - |
| 2.9795 | 4350 | 0.0068 | - |
| 3.0137 | 4400 | 0.0235 | - |
| 3.0479 | 4450 | 0.0004 | - |
| 3.0822 | 4500 | 0.0355 | - |
| 3.1164 | 4550 | 0.0237 | - |
| 3.1507 | 4600 | 0.0004 | - |
| 3.1849 | 4650 | 0.0003 | - |
| 3.2192 | 4700 | 0.0038 | - |
| 3.2534 | 4750 | 0.0002 | - |
| 3.2877 | 4800 | 0.0105 | - |
| 3.3219 | 4850 | 0.0055 | - |
| 3.3562 | 4900 | 0.0282 | - |
| 3.3904 | 4950 | 0.0105 | - |
| 3.4247 | 5000 | 0.0362 | - |
| 3.4589 | 5050 | 0.0004 | - |
| 3.4932 | 5100 | 0.0229 | - |
| 3.5274 | 5150 | 0.0092 | - |
| 3.5616 | 5200 | 0.033 | - |
| 3.5959 | 5250 | 0.0003 | - |
| 3.6301 | 5300 | 0.0444 | - |
| 3.6644 | 5350 | 0.0181 | - |
| 3.6986 | 5400 | 0.0254 | - |
| 3.7329 | 5450 | 0.0057 | - |
| 3.7671 | 5500 | 0.0511 | - |
| 3.8014 | 5550 | 0.0024 | - |
| 3.8356 | 5600 | 0.0195 | - |
| 3.8699 | 5650 | 0.0202 | - |
| 3.9041 | 5700 | 0.0003 | - |
| 3.9384 | 5750 | 0.0322 | - |
| 3.9726 | 5800 | 0.0123 | - |
| 4.0068 | 5850 | 0.0002 | - |
| 4.0411 | 5900 | 0.0002 | - |
| 4.0753 | 5950 | 0.008 | - |
| 4.1096 | 6000 | 0.0053 | - |
| 4.1438 | 6050 | 0.0003 | - |
| 4.1781 | 6100 | 0.0213 | - |
| 4.2123 | 6150 | 0.0046 | - |
| 4.2466 | 6200 | 0.0331 | - |
| 4.2808 | 6250 | 0.0078 | - |
| 4.3151 | 6300 | 0.0042 | - |
| 4.3493 | 6350 | 0.0234 | - |
| 4.3836 | 6400 | 0.0043 | - |
| 4.4178 | 6450 | 0.0253 | - |
| 4.4521 | 6500 | 0.0303 | - |
| 4.4863 | 6550 | 0.004 | - |
| 4.5205 | 6600 | 0.0166 | - |
| 4.5548 | 6650 | 0.0269 | - |
| 4.5890 | 6700 | 0.0079 | - |
| 4.6233 | 6750 | 0.0001 | - |
| 4.6575 | 6800 | 0.0002 | - |
| 4.6918 | 6850 | 0.0002 | - |
| 4.7260 | 6900 | 0.0199 | - |
| 4.7603 | 6950 | 0.0282 | - |
| 4.7945 | 7000 | 0.0016 | - |
| 4.8288 | 7050 | 0.0068 | - |
| 4.8630 | 7100 | 0.0054 | - |
| 4.8973 | 7150 | 0.036 | - |
| 4.9315 | 7200 | 0.0054 | - |
| 4.9658 | 7250 | 0.0174 | - |
| 5.0 | 7300 | 0.0001 | - |
| 5.0342 | 7350 | 0.0123 | - |
| 5.0685 | 7400 | 0.0218 | - |
| 5.1027 | 7450 | 0.0162 | - |
| 5.1370 | 7500 | 0.0181 | - |
| 5.1712 | 7550 | 0.0001 | - |
| 5.2055 | 7600 | 0.0201 | - |
| 5.2397 | 7650 | 0.0232 | - |
| 5.2740 | 7700 | 0.0003 | - |
| 5.3082 | 7750 | 0.0002 | - |
| 5.3425 | 7800 | 0.0094 | - |
| 5.3767 | 7850 | 0.0151 | - |
| 5.4110 | 7900 | 0.0099 | - |
| 5.4452 | 7950 | 0.01 | - |
| 5.4795 | 8000 | 0.0378 | - |
| 5.5137 | 8050 | 0.0199 | - |
| 5.5479 | 8100 | 0.0201 | - |
| 5.5822 | 8150 | 0.0242 | - |
| 5.6164 | 8200 | 0.0015 | - |
| 5.6507 | 8250 | 0.0002 | - |
| 5.6849 | 8300 | 0.0047 | - |
| 5.7192 | 8350 | 0.0002 | - |
| 5.7534 | 8400 | 0.0001 | - |
| 5.7877 | 8450 | 0.0215 | - |
| 5.8219 | 8500 | 0.0159 | - |
| 5.8562 | 8550 | 0.0001 | - |
| 5.8904 | 8600 | 0.0194 | - |
| 5.9247 | 8650 | 0.0058 | - |
| 5.9589 | 8700 | 0.0001 | - |
| 5.9932 | 8750 | 0.0164 | - |
| 6.0274 | 8800 | 0.0272 | - |
| 6.0616 | 8850 | 0.0001 | - |
| 6.0959 | 8900 | 0.0031 | - |
| 6.1301 | 8950 | 0.0154 | - |
| 6.1644 | 9000 | 0.0403 | - |
| 6.1986 | 9050 | 0.0035 | - |
| 6.2329 | 9100 | 0.0001 | - |
| 6.2671 | 9150 | 0.0061 | - |
| 6.3014 | 9200 | 0.0118 | - |
| 6.3356 | 9250 | 0.0031 | - |
| 6.3699 | 9300 | 0.0001 | - |
| 6.4041 | 9350 | 0.0098 | - |
| 6.4384 | 9400 | 0.0001 | - |
| 6.4726 | 9450 | 0.0343 | - |
| 6.5068 | 9500 | 0.017 | - |
| 6.5411 | 9550 | 0.0025 | - |
| 6.5753 | 9600 | 0.0001 | - |
| 6.6096 | 9650 | 0.0181 | - |
| 6.6438 | 9700 | 0.0191 | - |
| 6.6781 | 9750 | 0.0186 | - |
| 6.7123 | 9800 | 0.0001 | - |
| 6.7466 | 9850 | 0.0002 | - |
| 6.7808 | 9900 | 0.0001 | - |
| 6.8151 | 9950 | 0.0086 | - |
| 6.8493 | 10000 | 0.0377 | - |
| 6.8836 | 10050 | 0.0167 | - |
| 6.9178 | 10100 | 0.0034 | - |
| 6.9521 | 10150 | 0.0054 | - |
| 6.9863 | 10200 | 0.0048 | - |
| 7.0205 | 10250 | 0.0219 | - |
| 7.0548 | 10300 | 0.0001 | - |
| 7.0890 | 10350 | 0.0001 | - |
| 7.1233 | 10400 | 0.0262 | - |
| 7.1575 | 10450 | 0.0069 | - |
| 7.1918 | 10500 | 0.0001 | - |
| 7.2260 | 10550 | 0.0158 | - |
| 7.2603 | 10600 | 0.0192 | - |
| 7.2945 | 10650 | 0.0098 | - |
| 7.3288 | 10700 | 0.0001 | - |
| 7.3630 | 10750 | 0.0002 | - |
| 7.3973 | 10800 | 0.0021 | - |
| 7.4315 | 10850 | 0.0252 | - |
| 7.4658 | 10900 | 0.0383 | - |
| 7.5 | 10950 | 0.0001 | - |
| 7.5342 | 11000 | 0.0001 | - |
| 7.5685 | 11050 | 0.0491 | - |
| 7.6027 | 11100 | 0.0076 | - |
| 7.6370 | 11150 | 0.0089 | - |
| 7.6712 | 11200 | 0.0162 | - |
| 7.7055 | 11250 | 0.0163 | - |
| 7.7397 | 11300 | 0.0188 | - |
| 7.7740 | 11350 | 0.0141 | - |
| 7.8082 | 11400 | 0.0277 | - |
| 7.8425 | 11450 | 0.0001 | - |
| 7.8767 | 11500 | 0.0001 | - |
| 7.9110 | 11550 | 0.0055 | - |
| 7.9452 | 11600 | 0.0029 | - |
| 7.9795 | 11650 | 0.0001 | - |
| 8.0137 | 11700 | 0.0186 | - |
| 8.0479 | 11750 | 0.0037 | - |
| 8.0822 | 11800 | 0.0205 | - |
| 8.1164 | 11850 | 0.0217 | - |
| 8.1507 | 11900 | 0.0036 | - |
| 8.1849 | 11950 | 0.0039 | - |
| 8.2192 | 12000 | 0.0001 | - |
| 8.2534 | 12050 | 0.0055 | - |
| 8.2877 | 12100 | 0.0027 | - |
| 8.3219 | 12150 | 0.0029 | - |
| 8.3562 | 12200 | 0.0279 | - |
| 8.3904 | 12250 | 0.0139 | - |
| 8.4247 | 12300 | 0.04 | - |
| 8.4589 | 12350 | 0.003 | - |
| 8.4932 | 12400 | 0.0161 | - |
| 8.5274 | 12450 | 0.0001 | - |
| 8.5616 | 12500 | 0.035 | - |
| 8.5959 | 12550 | 0.0021 | - |
| 8.6301 | 12600 | 0.0355 | - |
| 8.6644 | 12650 | 0.0139 | - |
| 8.6986 | 12700 | 0.0183 | - |
| 8.7329 | 12750 | 0.0041 | - |
| 8.7671 | 12800 | 0.0354 | - |
| 8.8014 | 12850 | 0.0 | - |
| 8.8356 | 12900 | 0.0197 | - |
| 8.8699 | 12950 | 0.0189 | - |
| 8.9041 | 13000 | 0.0063 | - |
| 8.9384 | 13050 | 0.0309 | - |
| 8.9726 | 13100 | 0.0029 | - |
| 9.0068 | 13150 | 0.0027 | - |
| 9.0411 | 13200 | 0.0018 | - |
| 9.0753 | 13250 | 0.0104 | - |
| 9.1096 | 13300 | 0.0057 | - |
| 9.1438 | 13350 | 0.0051 | - |
| 9.1781 | 13400 | 0.0172 | - |
| 9.2123 | 13450 | 0.0001 | - |
| 9.2466 | 13500 | 0.0347 | - |
| 9.2808 | 13550 | 0.0024 | - |
| 9.3151 | 13600 | 0.0147 | - |
| 9.3493 | 13650 | 0.0218 | - |
| 9.3836 | 13700 | 0.0028 | - |
| 9.4178 | 13750 | 0.0205 | - |
| 9.4521 | 13800 | 0.0215 | - |
| 9.4863 | 13850 | 0.0001 | - |
| 9.5205 | 13900 | 0.0157 | - |
| 9.5548 | 13950 | 0.0227 | - |
| 9.5890 | 14000 | 0.0001 | - |
| 9.6233 | 14050 | 0.0048 | - |
| 9.6575 | 14100 | 0.0106 | - |
| 9.6918 | 14150 | 0.0077 | - |
| 9.7260 | 14200 | 0.0225 | - |
| 9.7603 | 14250 | 0.0173 | - |
| 9.7945 | 14300 | 0.0028 | - |
| 9.8288 | 14350 | 0.0022 | - |
| 9.8630 | 14400 | 0.003 | - |
| 9.8973 | 14450 | 0.0355 | - |
| 9.9315 | 14500 | 0.0001 | - |
| 9.9658 | 14550 | 0.0187 | - |
| 10.0 | 14600 | 0.0001 | - |
| 0.0007 | 1 | 0.0055 | - |
| 0.0342 | 50 | 0.0127 | - |
| 0.0685 | 100 | 0.0206 | - |
| 0.1027 | 150 | 0.0195 | - |
| 0.1370 | 200 | 0.0238 | - |
| 0.1712 | 250 | 0.0029 | - |
| 0.2055 | 300 | 0.0204 | - |
| 0.2397 | 350 | 0.0174 | - |
| 0.2740 | 400 | 0.0001 | - |
| 0.3082 | 450 | 0.0023 | - |
| 0.3425 | 500 | 0.0001 | - |
| 0.3767 | 550 | 0.0254 | - |
| 0.4110 | 600 | 0.0029 | - |
| 0.4452 | 650 | 0.0082 | - |
| 0.4795 | 700 | 0.0411 | - |
| 0.5137 | 750 | 0.0159 | - |
| 0.5479 | 800 | 0.0207 | - |
| 0.5822 | 850 | 0.0173 | - |
| 0.6164 | 900 | 0.0001 | - |
| 0.6507 | 950 | 0.0018 | - |
| 0.6849 | 1000 | 0.0059 | - |
| 0.7192 | 1050 | 0.0014 | - |
| 0.7534 | 1100 | 0.0022 | - |
| 0.7877 | 1150 | 0.0187 | - |
| 0.8219 | 1200 | 0.0158 | - |
| 0.8562 | 1250 | 0.0025 | - |
| 0.8904 | 1300 | 0.0113 | - |
| 0.9247 | 1350 | 0.0007 | - |
| 0.9589 | 1400 | 0.004 | - |
| 0.9932 | 1450 | 0.0216 | - |
| 1.0274 | 1500 | 0.0213 | - |
| 1.0616 | 1550 | 0.0044 | - |
| 1.0959 | 1600 | 0.0025 | - |
| 1.1301 | 1650 | 0.0154 | - |
| 1.1644 | 1700 | 0.038 | - |
| 1.1986 | 1750 | 0.0001 | - |
| 1.2329 | 1800 | 0.0004 | - |
| 1.2671 | 1850 | 0.0065 | - |
| 1.3014 | 1900 | 0.0087 | - |
| 1.3356 | 1950 | 0.0001 | - |
| 1.3699 | 2000 | 0.0039 | - |
| 1.4041 | 2050 | 0.0005 | - |
| 1.4384 | 2100 | 0.0087 | - |
| 1.4726 | 2150 | 0.0369 | - |
| 1.5068 | 2200 | 0.0157 | - |
| 1.5411 | 2250 | 0.0094 | - |
| 1.5753 | 2300 | 0.0042 | - |
| 1.6096 | 2350 | 0.018 | - |
| 1.6438 | 2400 | 0.014 | - |
| 1.6781 | 2450 | 0.0161 | - |
| 1.7123 | 2500 | 0.0011 | - |
| 1.7466 | 2550 | 0.0001 | - |
| 1.7808 | 2600 | 0.004 | - |
| 1.8151 | 2650 | 0.0048 | - |
| 1.8493 | 2700 | 0.0403 | - |
| 1.8836 | 2750 | 0.0254 | - |
| 1.9178 | 2800 | 0.0124 | - |
| 1.9521 | 2850 | 0.0028 | - |
| 1.9863 | 2900 | 0.0026 | - |
| 2.0205 | 2950 | 0.0171 | - |
| 2.0548 | 3000 | 0.0049 | - |
| 2.0890 | 3050 | 0.0092 | - |
| 2.1233 | 3100 | 0.0134 | - |
| 2.1575 | 3150 | 0.0021 | - |
| 2.1918 | 3200 | 0.0001 | - |
| 2.2260 | 3250 | 0.0153 | - |
| 2.2603 | 3300 | 0.0253 | - |
| 2.2945 | 3350 | 0.0095 | - |
| 2.3288 | 3400 | 0.0144 | - |
| 2.3630 | 3450 | 0.0064 | - |
| 2.3973 | 3500 | 0.0013 | - |
| 2.4315 | 3550 | 0.0216 | - |
| 2.4658 | 3600 | 0.0387 | - |
| 2.5 | 3650 | 0.0018 | - |
| 2.5342 | 3700 | 0.0034 | - |
| 2.5685 | 3750 | 0.0428 | - |
| 2.6027 | 3800 | 0.0055 | - |
| 2.6370 | 3850 | 0.0001 | - |
| 2.6712 | 3900 | 0.0154 | - |
| 2.7055 | 3950 | 0.0176 | - |
| 2.7397 | 4000 | 0.0213 | - |
| 2.7740 | 4050 | 0.016 | - |
| 2.8082 | 4100 | 0.0293 | - |
| 2.8425 | 4150 | 0.0034 | - |
| 2.8767 | 4200 | 0.0119 | - |
| 2.9110 | 4250 | 0.0061 | - |
| 2.9452 | 4300 | 0.0068 | - |
| 2.9795 | 4350 | 0.006 | - |
| 3.0137 | 4400 | 0.0211 | - |
| 3.0479 | 4450 | 0.0001 | - |
| 3.0822 | 4500 | 0.0303 | - |
| 3.1164 | 4550 | 0.0225 | - |
| 3.1507 | 4600 | 0.0001 | - |
| 3.1849 | 4650 | 0.0002 | - |
| 3.2192 | 4700 | 0.0031 | - |
| 3.2534 | 4750 | 0.0001 | - |
| 3.2877 | 4800 | 0.0103 | - |
| 3.3219 | 4850 | 0.0055 | - |
| 3.3562 | 4900 | 0.0297 | - |
| 3.3904 | 4950 | 0.0121 | - |
| 3.4247 | 5000 | 0.0348 | - |
| 3.4589 | 5050 | 0.0003 | - |
| 3.4932 | 5100 | 0.0212 | - |
| 3.5274 | 5150 | 0.0077 | - |
| 3.5616 | 5200 | 0.0339 | - |
| 3.5959 | 5250 | 0.0001 | - |
| 3.6301 | 5300 | 0.0444 | - |
| 3.6644 | 5350 | 0.0167 | - |
| 3.6986 | 5400 | 0.0245 | - |
| 3.7329 | 5450 | 0.005 | - |
| 3.7671 | 5500 | 0.047 | - |
| 3.8014 | 5550 | 0.0021 | - |
| 3.8356 | 5600 | 0.019 | - |
| 3.8699 | 5650 | 0.0187 | - |
| 3.9041 | 5700 | 0.0001 | - |
| 3.9384 | 5750 | 0.0328 | - |
| 3.9726 | 5800 | 0.0097 | - |
| 4.0068 | 5850 | 0.0001 | - |
| 4.0411 | 5900 | 0.0001 | - |
| 4.0753 | 5950 | 0.0078 | - |
| 4.1096 | 6000 | 0.0057 | - |
| 4.1438 | 6050 | 0.0002 | - |
| 4.1781 | 6100 | 0.0218 | - |
| 4.2123 | 6150 | 0.0038 | - |
| 4.2466 | 6200 | 0.0337 | - |
| 4.2808 | 6250 | 0.0065 | - |
| 4.3151 | 6300 | 0.0033 | - |
| 4.3493 | 6350 | 0.0228 | - |
| 4.3836 | 6400 | 0.0033 | - |
| 4.4178 | 6450 | 0.0244 | - |
| 4.4521 | 6500 | 0.027 | - |
| 4.4863 | 6550 | 0.0027 | - |
| 4.5205 | 6600 | 0.0153 | - |
| 4.5548 | 6650 | 0.0241 | - |
| 4.5890 | 6700 | 0.0071 | - |
| 4.6233 | 6750 | 0.0001 | - |
| 4.6575 | 6800 | 0.0 | - |
| 4.6918 | 6850 | 0.0001 | - |
| 4.7260 | 6900 | 0.0203 | - |
| 4.7603 | 6950 | 0.0273 | - |
| 4.7945 | 7000 | 0.0017 | - |
| 4.8288 | 7050 | 0.0062 | - |
| 4.8630 | 7100 | 0.0043 | - |
| 4.8973 | 7150 | 0.0346 | - |
| 4.9315 | 7200 | 0.005 | - |
| 4.9658 | 7250 | 0.0182 | - |
| 5.0 | 7300 | 0.0001 | - |
| 5.0342 | 7350 | 0.0108 | - |
| 5.0685 | 7400 | 0.0218 | - |
| 5.1027 | 7450 | 0.0163 | - |
| 5.1370 | 7500 | 0.0195 | - |
| 5.1712 | 7550 | 0.0001 | - |
| 5.2055 | 7600 | 0.0195 | - |
| 5.2397 | 7650 | 0.0222 | - |
| 5.2740 | 7700 | 0.0002 | - |
| 5.3082 | 7750 | 0.0001 | - |
| 5.3425 | 7800 | 0.0078 | - |
| 5.3767 | 7850 | 0.0158 | - |
| 5.4110 | 7900 | 0.0081 | - |
| 5.4452 | 7950 | 0.0087 | - |
| 5.4795 | 8000 | 0.0372 | - |
| 5.5137 | 8050 | 0.019 | - |
| 5.5479 | 8100 | 0.0188 | - |
| 5.5822 | 8150 | 0.0238 | - |
| 5.6164 | 8200 | 0.0018 | - |
| 5.6507 | 8250 | 0.0001 | - |
| 5.6849 | 8300 | 0.0046 | - |
| 5.7192 | 8350 | 0.0001 | - |
| 5.7534 | 8400 | 0.0001 | - |
| 5.7877 | 8450 | 0.0216 | - |
| 5.8219 | 8500 | 0.0164 | - |
| 5.8562 | 8550 | 0.0 | - |
| 5.8904 | 8600 | 0.018 | - |
| 5.9247 | 8650 | 0.0059 | - |
| 5.9589 | 8700 | 0.0001 | - |
| 5.9932 | 8750 | 0.0168 | - |
| 6.0274 | 8800 | 0.0259 | - |
| 6.0616 | 8850 | 0.0001 | - |
| 6.0959 | 8900 | 0.0029 | - |
| 6.1301 | 8950 | 0.0159 | - |
| 6.1644 | 9000 | 0.041 | - |
| 6.1986 | 9050 | 0.0035 | - |
| 6.2329 | 9100 | 0.0001 | - |
| 6.2671 | 9150 | 0.005 | - |
| 6.3014 | 9200 | 0.0101 | - |
| 6.3356 | 9250 | 0.0027 | - |
| 6.3699 | 9300 | 0.0 | - |
| 6.4041 | 9350 | 0.0094 | - |
| 6.4384 | 9400 | 0.0001 | - |
| 6.4726 | 9450 | 0.0335 | - |
| 6.5068 | 9500 | 0.0168 | - |
| 6.5411 | 9550 | 0.0025 | - |
| 6.5753 | 9600 | 0.0001 | - |
| 6.6096 | 9650 | 0.0185 | - |
| 6.6438 | 9700 | 0.0188 | - |
| 6.6781 | 9750 | 0.0187 | - |
| 6.7123 | 9800 | 0.0001 | - |
| 6.7466 | 9850 | 0.0002 | - |
| 6.7808 | 9900 | 0.0001 | - |
| 6.8151 | 9950 | 0.0087 | - |
| 6.8493 | 10000 | 0.0371 | - |
| 6.8836 | 10050 | 0.0172 | - |
| 6.9178 | 10100 | 0.0028 | - |
| 6.9521 | 10150 | 0.0055 | - |
| 6.9863 | 10200 | 0.0043 | - |
| 7.0205 | 10250 | 0.0219 | - |
| 7.0548 | 10300 | 0.0 | - |
| 7.0890 | 10350 | 0.0001 | - |
| 7.1233 | 10400 | 0.026 | - |
| 7.1575 | 10450 | 0.0067 | - |
| 7.1918 | 10500 | 0.0001 | - |
| 7.2260 | 10550 | 0.0162 | - |
| 7.2603 | 10600 | 0.019 | - |
| 7.2945 | 10650 | 0.0093 | - |
| 7.3288 | 10700 | 0.0001 | - |
| 7.3630 | 10750 | 0.0002 | - |
| 7.3973 | 10800 | 0.002 | - |
| 7.4315 | 10850 | 0.0247 | - |
| 7.4658 | 10900 | 0.0394 | - |
| 7.5 | 10950 | 0.0001 | - |
| 7.5342 | 11000 | 0.0001 | - |
| 7.5685 | 11050 | 0.0503 | - |
| 7.6027 | 11100 | 0.0066 | - |
| 7.6370 | 11150 | 0.0087 | - |
| 7.6712 | 11200 | 0.0165 | - |
| 7.7055 | 11250 | 0.0164 | - |
| 7.7397 | 11300 | 0.019 | - |
| 7.7740 | 11350 | 0.0143 | - |
| 7.8082 | 11400 | 0.0282 | - |
| 7.8425 | 11450 | 0.0001 | - |
| 7.8767 | 11500 | 0.0 | - |
| 7.9110 | 11550 | 0.0049 | - |
| 7.9452 | 11600 | 0.0028 | - |
| 7.9795 | 11650 | 0.0001 | - |
| 8.0137 | 11700 | 0.0184 | - |
| 8.0479 | 11750 | 0.0038 | - |
| 8.0822 | 11800 | 0.0211 | - |
| 8.1164 | 11850 | 0.0217 | - |
| 8.1507 | 11900 | 0.0035 | - |
| 8.1849 | 11950 | 0.0039 | - |
| 8.2192 | 12000 | 0.0 | - |
| 8.2534 | 12050 | 0.0055 | - |
| 8.2877 | 12100 | 0.0027 | - |
| 8.3219 | 12150 | 0.0031 | - |
| 8.3562 | 12200 | 0.0271 | - |
| 8.3904 | 12250 | 0.0138 | - |
| 8.4247 | 12300 | 0.0413 | - |
| 8.4589 | 12350 | 0.0029 | - |
| 8.4932 | 12400 | 0.0161 | - |
| 8.5274 | 12450 | 0.0 | - |
| 8.5616 | 12500 | 0.0352 | - |
| 8.5959 | 12550 | 0.0018 | - |
| 8.6301 | 12600 | 0.0363 | - |
| 8.6644 | 12650 | 0.0136 | - |
| 8.6986 | 12700 | 0.0175 | - |
| 8.7329 | 12750 | 0.0045 | - |
| 8.7671 | 12800 | 0.036 | - |
| 8.8014 | 12850 | 0.0001 | - |
| 8.8356 | 12900 | 0.0188 | - |
| 8.8699 | 12950 | 0.0192 | - |
| 8.9041 | 13000 | 0.0059 | - |
| 8.9384 | 13050 | 0.0298 | - |
| 8.9726 | 13100 | 0.0026 | - |
| 9.0068 | 13150 | 0.0027 | - |
| 9.0411 | 13200 | 0.0017 | - |
| 9.0753 | 13250 | 0.0103 | - |
| 9.1096 | 13300 | 0.0061 | - |
| 9.1438 | 13350 | 0.0043 | - |
| 9.1781 | 13400 | 0.0189 | - |
| 9.2123 | 13450 | 0.0001 | - |
| 9.2466 | 13500 | 0.0363 | - |
| 9.2808 | 13550 | 0.0019 | - |
| 9.3151 | 13600 | 0.0141 | - |
| 9.3493 | 13650 | 0.0213 | - |
| 9.3836 | 13700 | 0.0029 | - |
| 9.4178 | 13750 | 0.0217 | - |
| 9.4521 | 13800 | 0.0218 | - |
| 9.4863 | 13850 | 0.0001 | - |
| 9.5205 | 13900 | 0.014 | - |
| 9.5548 | 13950 | 0.0213 | - |
| 9.5890 | 14000 | 0.0 | - |
| 9.6233 | 14050 | 0.004 | - |
| 9.6575 | 14100 | 0.0112 | - |
| 9.6918 | 14150 | 0.0077 | - |
| 9.7260 | 14200 | 0.0237 | - |
| 9.7603 | 14250 | 0.0202 | - |
| 9.7945 | 14300 | 0.003 | - |
| 9.8288 | 14350 | 0.002 | - |
| 9.8630 | 14400 | 0.0028 | - |
| 9.8973 | 14450 | 0.0398 | - |
| 9.9315 | 14500 | 0.0001 | - |
| 9.9658 | 14550 | 0.0185 | - |
| 10.0 | 14600 | 0.0001 | - |
| 10.0342 | 14650 | 0.0102 | - |
| 10.0685 | 14700 | 0.0164 | - |
| 10.1027 | 14750 | 0.0161 | - |
| 10.1370 | 14800 | 0.0221 | - |
| 10.1712 | 14850 | 0.0016 | - |
| 10.2055 | 14900 | 0.0151 | - |
| 10.2397 | 14950 | 0.0215 | - |
| 10.2740 | 15000 | 0.0021 | - |
| 10.3082 | 15050 | 0.0075 | - |
| 10.3425 | 15100 | 0.0001 | - |
| 10.3767 | 15150 | 0.0211 | - |
| 10.4110 | 15200 | 0.0022 | - |
| 10.4452 | 15250 | 0.0001 | - |
| 10.4795 | 15300 | 0.0348 | - |
| 10.5137 | 15350 | 0.0211 | - |
| 10.5479 | 15400 | 0.0193 | - |
| 10.5822 | 15450 | 0.0203 | - |
| 10.6164 | 15500 | 0.0001 | - |
| 10.6507 | 15550 | 0.0 | - |
| 10.6849 | 15600 | 0.0028 | - |
| 10.7192 | 15650 | 0.0025 | - |
| 10.7534 | 15700 | 0.003 | - |
| 10.7877 | 15750 | 0.0199 | - |
| 10.8219 | 15800 | 0.0238 | - |
| 10.8562 | 15850 | 0.0024 | - |
| 10.8904 | 15900 | 0.0149 | - |
| 10.9247 | 15950 | 0.0019 | - |
| 10.9589 | 16000 | 0.0001 | - |
| 10.9932 | 16050 | 0.0206 | - |
| 11.0274 | 16100 | 0.0187 | - |
| 11.0616 | 16150 | 0.0025 | - |
| 11.0959 | 16200 | 0.0001 | - |
| 11.1301 | 16250 | 0.0185 | - |
| 11.1644 | 16300 | 0.0476 | - |
| 11.1986 | 16350 | 0.0027 | - |
| 11.2329 | 16400 | 0.0064 | - |
| 11.2671 | 16450 | 0.0026 | - |
| 11.3014 | 16500 | 0.0055 | - |
| 11.3356 | 16550 | 0.0024 | - |
| 11.3699 | 16600 | 0.0059 | - |
| 11.4041 | 16650 | 0.0 | - |
| 11.4384 | 16700 | 0.0 | - |
| 11.4726 | 16750 | 0.0333 | - |
| 11.5068 | 16800 | 0.0231 | - |
| 11.5411 | 16850 | 0.0084 | - |
| 11.5753 | 16900 | 0.0001 | - |
| 11.6096 | 16950 | 0.0173 | - |
| 11.6438 | 17000 | 0.0207 | - |
| 11.6781 | 17050 | 0.0162 | - |
| 11.7123 | 17100 | 0.0071 | - |
| 11.7466 | 17150 | 0.0049 | - |
| 11.7808 | 17200 | 0.0025 | - |
| 11.8151 | 17250 | 0.011 | - |
| 11.8493 | 17300 | 0.035 | - |
| 11.8836 | 17350 | 0.0168 | - |
| 11.9178 | 17400 | 0.0085 | - |
| 11.9521 | 17450 | 0.0028 | - |
| 11.9863 | 17500 | 0.0 | - |
| 12.0205 | 17550 | 0.0239 | - |
| 12.0548 | 17600 | 0.0026 | - |
| 12.0890 | 17650 | 0.008 | - |
| 12.1233 | 17700 | 0.0165 | - |
| 12.1575 | 17750 | 0.0027 | - |
| 12.1918 | 17800 | 0.0069 | - |
| 12.2260 | 17850 | 0.0215 | - |
| 12.2603 | 17900 | 0.0236 | - |
| 12.2945 | 17950 | 0.0001 | - |
| 12.3288 | 18000 | 0.0001 | - |
| 12.3630 | 18050 | 0.0025 | - |
| 12.3973 | 18100 | 0.0026 | - |
| 12.4315 | 18150 | 0.0164 | - |
| 12.4658 | 18200 | 0.035 | - |
| 12.5 | 18250 | 0.0032 | - |
| 12.5342 | 18300 | 0.0 | - |
| 12.5685 | 18350 | 0.0343 | - |
| 12.6027 | 18400 | 0.0024 | - |
| 12.6370 | 18450 | 0.0025 | - |
| 12.6712 | 18500 | 0.0202 | - |
| 12.7055 | 18550 | 0.0192 | - |
| 12.7397 | 18600 | 0.017 | - |
| 12.7740 | 18650 | 0.02 | - |
| 12.8082 | 18700 | 0.0321 | - |
| 12.8425 | 18750 | 0.0001 | - |
| 12.8767 | 18800 | 0.0023 | - |
| 12.9110 | 18850 | 0.0028 | - |
| 12.9452 | 18900 | 0.0 | - |
| 12.9795 | 18950 | 0.0 | - |
| 13.0137 | 19000 | 0.0267 | - |
| 13.0479 | 19050 | 0.0056 | - |
| 13.0822 | 19100 | 0.0219 | - |
| 13.1164 | 19150 | 0.0184 | - |
| 13.1507 | 19200 | 0.0028 | - |
| 13.1849 | 19250 | 0.0 | - |
| 13.2192 | 19300 | 0.005 | - |
| 13.2534 | 19350 | 0.0056 | - |
| 13.2877 | 19400 | 0.0033 | - |
| 13.3219 | 19450 | 0.0 | - |
| 13.3562 | 19500 | 0.034 | - |
| 13.3904 | 19550 | 0.0173 | - |
| 13.4247 | 19600 | 0.033 | - |
| 13.4589 | 19650 | 0.0025 | - |
| 13.4932 | 19700 | 0.0279 | - |
| 13.5274 | 19750 | 0.0052 | - |
| 13.5616 | 19800 | 0.0351 | - |
| 13.5959 | 19850 | 0.0 | - |
| 13.6301 | 19900 | 0.035 | - |
| 13.6644 | 19950 | 0.0069 | - |
| 13.6986 | 20000 | 0.0227 | - |
| 13.7329 | 20050 | 0.0 | - |
| 13.7671 | 20100 | 0.0347 | - |
| 13.8014 | 20150 | 0.0 | - |
| 13.8356 | 20200 | 0.0217 | - |
| 13.8699 | 20250 | 0.02 | - |
| 13.9041 | 20300 | 0.0 | - |
| 13.9384 | 20350 | 0.0393 | - |
| 13.9726 | 20400 | 0.0053 | - |
| 14.0068 | 20450 | 0.0026 | - |
| 14.0411 | 20500 | 0.0025 | - |
| 14.0753 | 20550 | 0.0049 | - |
| 14.1096 | 20600 | 0.0 | - |
| 14.1438 | 20650 | 0.0 | - |
| 14.1781 | 20700 | 0.0184 | - |
| 14.2123 | 20750 | 0.0029 | - |
| 14.2466 | 20800 | 0.0313 | - |
| 14.2808 | 20850 | 0.0 | - |
| 14.3151 | 20900 | 0.0051 | - |
| 14.3493 | 20950 | 0.0157 | - |
| 14.3836 | 21000 | 0.0059 | - |
| 14.4178 | 21050 | 0.0182 | - |
| 14.4521 | 21100 | 0.0242 | - |
| 14.4863 | 21150 | 0.0024 | - |
| 14.5205 | 21200 | 0.026 | - |
| 14.5548 | 21250 | 0.0211 | - |
| 14.5890 | 21300 | 0.0053 | - |
| 14.6233 | 21350 | 0.0 | - |
| 14.6575 | 21400 | 0.0 | - |
| 14.6918 | 21450 | 0.0034 | - |
| 14.7260 | 21500 | 0.0239 | - |
| 14.7603 | 21550 | 0.0209 | - |
| 14.7945 | 21600 | 0.0028 | - |
| 14.8288 | 21650 | 0.0 | - |
| 14.8630 | 21700 | 0.0022 | - |
| 14.8973 | 21750 | 0.0364 | - |
| 14.9315 | 21800 | 0.0052 | - |
| 14.9658 | 21850 | 0.0239 | - |
| 15.0 | 21900 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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}
}
```