---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Intel Pentium 3556U (Mobile) 1.70 GHz
- text: Sign in
- text: Discover the mountains
- text: "\n Other services\n "
- text: Iceland
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: 0.9034931912374186
name: Accuracy
---
# 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 |
|:------|:--------------------------------------------------------------------------------------------------------|
| True | - 'Pug (1×1)'
- 'Pug (1×1)'
- '/ 0:31'
|
| False | - 'Forum FAQ'
- 'Hearthstone'
- 'NVIDIA GeForce GTX 670MX (Mobile) 3072 MB'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9035 |
## 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("Sign in")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 5.4853 | 301 |
| Label | Training Sample Count |
|:------|:----------------------|
| False | 6755 |
| True | 6757 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.3555 | - |
| 0.0015 | 50 | 0.3874 | - |
| 0.0030 | 100 | 0.3422 | - |
| 0.0044 | 150 | 0.3148 | - |
| 0.0059 | 200 | 0.2496 | - |
| 0.0074 | 250 | 0.2681 | - |
| 0.0089 | 300 | 0.2412 | - |
| 0.0104 | 350 | 0.2927 | - |
| 0.0118 | 400 | 0.2389 | - |
| 0.0133 | 450 | 0.2559 | - |
| 0.0148 | 500 | 0.204 | - |
| 0.0163 | 550 | 0.158 | - |
| 0.0178 | 600 | 0.1479 | - |
| 0.0192 | 650 | 0.1958 | - |
| 0.0207 | 700 | 0.2173 | - |
| 0.0222 | 750 | 0.1231 | - |
| 0.0237 | 800 | 0.1966 | - |
| 0.0252 | 850 | 0.1599 | - |
| 0.0266 | 900 | 0.1373 | - |
| 0.0281 | 950 | 0.2491 | - |
| 0.0296 | 1000 | 0.0951 | - |
| 0.0311 | 1050 | 0.2253 | - |
| 0.0326 | 1100 | 0.2046 | - |
| 0.0340 | 1150 | 0.2174 | - |
| 0.0355 | 1200 | 0.1401 | - |
| 0.0370 | 1250 | 0.1549 | - |
| 0.0385 | 1300 | 0.1872 | - |
| 0.0400 | 1350 | 0.2262 | - |
| 0.0414 | 1400 | 0.1277 | - |
| 0.0429 | 1450 | 0.1653 | - |
| 0.0444 | 1500 | 0.1355 | - |
| 0.0459 | 1550 | 0.1235 | - |
| 0.0474 | 1600 | 0.0947 | - |
| 0.0488 | 1650 | 0.11 | - |
| 0.0503 | 1700 | 0.1149 | - |
| 0.0518 | 1750 | 0.1823 | - |
| 0.0533 | 1800 | 0.2104 | - |
| 0.0548 | 1850 | 0.0871 | - |
| 0.0562 | 1900 | 0.1275 | - |
| 0.0577 | 1950 | 0.0977 | - |
| 0.0592 | 2000 | 0.2031 | - |
| 0.0607 | 2050 | 0.1872 | - |
| 0.0622 | 2100 | 0.0996 | - |
| 0.0636 | 2150 | 0.1487 | - |
| 0.0651 | 2200 | 0.1647 | - |
| 0.0666 | 2250 | 0.0861 | - |
| 0.0681 | 2300 | 0.0464 | - |
| 0.0696 | 2350 | 0.1026 | - |
| 0.0710 | 2400 | 0.2031 | - |
| 0.0725 | 2450 | 0.1815 | - |
| 0.0740 | 2500 | 0.0644 | - |
| 0.0755 | 2550 | 0.1039 | - |
| 0.0770 | 2600 | 0.0115 | - |
| 0.0784 | 2650 | 0.0426 | - |
| 0.0799 | 2700 | 0.0895 | - |
| 0.0814 | 2750 | 0.1562 | - |
| 0.0829 | 2800 | 0.0835 | - |
| 0.0844 | 2850 | 0.1681 | - |
| 0.0858 | 2900 | 0.1159 | - |
| 0.0873 | 2950 | 0.0162 | - |
| 0.0888 | 3000 | 0.0634 | - |
| 0.0903 | 3050 | 0.1161 | - |
| 0.0918 | 3100 | 0.1086 | - |
| 0.0933 | 3150 | 0.0548 | - |
| 0.0947 | 3200 | 0.1209 | - |
| 0.0962 | 3250 | 0.0425 | - |
| 0.0977 | 3300 | 0.0157 | - |
| 0.0992 | 3350 | 0.1293 | - |
| 0.1007 | 3400 | 0.1847 | - |
| 0.1021 | 3450 | 0.1965 | - |
| 0.1036 | 3500 | 0.1286 | - |
| 0.1051 | 3550 | 0.104 | - |
| 0.1066 | 3600 | 0.0899 | - |
| 0.1081 | 3650 | 0.1513 | - |
| 0.1095 | 3700 | 0.0443 | - |
| 0.1110 | 3750 | 0.053 | - |
| 0.1125 | 3800 | 0.0096 | - |
| 0.1140 | 3850 | 0.0399 | - |
| 0.1155 | 3900 | 0.068 | - |
| 0.1169 | 3950 | 0.0537 | - |
| 0.1184 | 4000 | 0.0235 | - |
| 0.1199 | 4050 | 0.0625 | - |
| 0.1214 | 4100 | 0.1303 | - |
| 0.1229 | 4150 | 0.1208 | - |
| 0.1243 | 4200 | 0.0041 | - |
| 0.1258 | 4250 | 0.059 | - |
| 0.1273 | 4300 | 0.0543 | - |
| 0.1288 | 4350 | 0.1664 | - |
| 0.1303 | 4400 | 0.0591 | - |
| 0.1317 | 4450 | 0.0631 | - |
| 0.1332 | 4500 | 0.2538 | - |
| 0.1347 | 4550 | 0.0484 | - |
| 0.1362 | 4600 | 0.003 | - |
| 0.1377 | 4650 | 0.0849 | - |
| 0.1391 | 4700 | 0.1109 | - |
| 0.1406 | 4750 | 0.0403 | - |
| 0.1421 | 4800 | 0.0481 | - |
| 0.1436 | 4850 | 0.0172 | - |
| 0.1451 | 4900 | 0.0049 | - |
| 0.1465 | 4950 | 0.006 | - |
| 0.1480 | 5000 | 0.0009 | - |
| 0.1495 | 5050 | 0.0712 | - |
| 0.1510 | 5100 | 0.1076 | - |
| 0.1525 | 5150 | 0.1123 | - |
| 0.1539 | 5200 | 0.0029 | - |
| 0.1554 | 5250 | 0.0519 | - |
| 0.1569 | 5300 | 0.0523 | - |
| 0.1584 | 5350 | 0.097 | - |
| 0.1599 | 5400 | 0.0471 | - |
| 0.1613 | 5450 | 0.0371 | - |
| 0.1628 | 5500 | 0.1127 | - |
| 0.1643 | 5550 | 0.0535 | - |
| 0.1658 | 5600 | 0.0067 | - |
| 0.1673 | 5650 | 0.01 | - |
| 0.1687 | 5700 | 0.0085 | - |
| 0.1702 | 5750 | 0.0739 | - |
| 0.1717 | 5800 | 0.0019 | - |
| 0.1732 | 5850 | 0.0045 | - |
| 0.1747 | 5900 | 0.1316 | - |
| 0.1761 | 5950 | 0.0623 | - |
| 0.1776 | 6000 | 0.088 | - |
| 0.1791 | 6050 | 0.0498 | - |
| 0.1806 | 6100 | 0.0028 | - |
| 0.1821 | 6150 | 0.1206 | - |
| 0.1835 | 6200 | 0.0041 | - |
| 0.1850 | 6250 | 0.0849 | - |
| 0.1865 | 6300 | 0.247 | - |
| 0.1880 | 6350 | 0.0042 | - |
| 0.1895 | 6400 | 0.0944 | - |
| 0.1909 | 6450 | 0.1046 | - |
| 0.1924 | 6500 | 0.0481 | - |
| 0.1939 | 6550 | 0.0034 | - |
| 0.1954 | 6600 | 0.0066 | - |
| 0.1969 | 6650 | 0.0015 | - |
| 0.1983 | 6700 | 0.0816 | - |
| 0.1998 | 6750 | 0.0511 | - |
| 0.2013 | 6800 | 0.0739 | - |
| 0.2028 | 6850 | 0.0024 | - |
| 0.2043 | 6900 | 0.0221 | - |
| 0.2057 | 6950 | 0.0678 | - |
| 0.2072 | 7000 | 0.0838 | - |
| 0.2087 | 7050 | 0.0023 | - |
| 0.2102 | 7100 | 0.0043 | - |
| 0.2117 | 7150 | 0.0551 | - |
| 0.2131 | 7200 | 0.0167 | - |
| 0.2146 | 7250 | 0.0033 | - |
| 0.2161 | 7300 | 0.008 | - |
| 0.2176 | 7350 | 0.0259 | - |
| 0.2191 | 7400 | 0.0078 | - |
| 0.2205 | 7450 | 0.0113 | - |
| 0.2220 | 7500 | 0.0153 | - |
| 0.2235 | 7550 | 0.059 | - |
| 0.2250 | 7600 | 0.0401 | - |
| 0.2265 | 7650 | 0.0015 | - |
| 0.2279 | 7700 | 0.0102 | - |
| 0.2294 | 7750 | 0.0489 | - |
| 0.2309 | 7800 | 0.1319 | - |
| 0.2324 | 7850 | 0.0128 | - |
| 0.2339 | 7900 | 0.0234 | - |
| 0.2353 | 7950 | 0.0105 | - |
| 0.2368 | 8000 | 0.0008 | - |
| 0.2383 | 8050 | 0.1118 | - |
| 0.2398 | 8100 | 0.0076 | - |
| 0.2413 | 8150 | 0.1399 | - |
| 0.2427 | 8200 | 0.0042 | - |
| 0.2442 | 8250 | 0.0579 | - |
| 0.2457 | 8300 | 0.0533 | - |
| 0.2472 | 8350 | 0.0271 | - |
| 0.2487 | 8400 | 0.0461 | - |
| 0.2501 | 8450 | 0.0052 | - |
| 0.2516 | 8500 | 0.0661 | - |
| 0.2531 | 8550 | 0.0407 | - |
| 0.2546 | 8600 | 0.0208 | - |
| 0.2561 | 8650 | 0.0527 | - |
| 0.2575 | 8700 | 0.0065 | - |
| 0.2590 | 8750 | 0.0051 | - |
| 0.2605 | 8800 | 0.0179 | - |
| 0.2620 | 8850 | 0.0332 | - |
| 0.2635 | 8900 | 0.0625 | - |
| 0.2649 | 8950 | 0.1035 | - |
| 0.2664 | 9000 | 0.129 | - |
| 0.2679 | 9050 | 0.0988 | - |
| 0.2694 | 9100 | 0.0035 | - |
| 0.2709 | 9150 | 0.0045 | - |
| 0.2724 | 9200 | 0.0277 | - |
| 0.2738 | 9250 | 0.0291 | - |
| 0.2753 | 9300 | 0.0307 | - |
| 0.2768 | 9350 | 0.0844 | - |
| 0.2783 | 9400 | 0.0036 | - |
| 0.2798 | 9450 | 0.0807 | - |
| 0.2812 | 9500 | 0.0619 | - |
| 0.2827 | 9550 | 0.0675 | - |
| 0.2842 | 9600 | 0.0008 | - |
| 0.2857 | 9650 | 0.0134 | - |
| 0.2872 | 9700 | 0.0027 | - |
| 0.2886 | 9750 | 0.0009 | - |
| 0.2901 | 9800 | 0.0119 | - |
| 0.2916 | 9850 | 0.0165 | - |
| 0.2931 | 9900 | 0.0242 | - |
| 0.2946 | 9950 | 0.1022 | - |
| 0.2960 | 10000 | 0.0288 | - |
| 0.2975 | 10050 | 0.0016 | - |
| 0.2990 | 10100 | 0.0027 | - |
| 0.3005 | 10150 | 0.0237 | - |
| 0.3020 | 10200 | 0.0014 | - |
| 0.3034 | 10250 | 0.0129 | - |
| 0.3049 | 10300 | 0.0023 | - |
| 0.3064 | 10350 | 0.0038 | - |
| 0.3079 | 10400 | 0.0005 | - |
| 0.3094 | 10450 | 0.0448 | - |
| 0.3108 | 10500 | 0.0334 | - |
| 0.3123 | 10550 | 0.1215 | - |
| 0.3138 | 10600 | 0.0021 | - |
| 0.3153 | 10650 | 0.0433 | - |
| 0.3168 | 10700 | 0.0106 | - |
| 0.3182 | 10750 | 0.0574 | - |
| 0.3197 | 10800 | 0.0421 | - |
| 0.3212 | 10850 | 0.0676 | - |
| 0.3227 | 10900 | 0.0358 | - |
| 0.3242 | 10950 | 0.1207 | - |
| 0.3256 | 11000 | 0.0154 | - |
| 0.3271 | 11050 | 0.0078 | - |
| 0.3286 | 11100 | 0.0475 | - |
| 0.3301 | 11150 | 0.0697 | - |
| 0.3316 | 11200 | 0.0016 | - |
| 0.3330 | 11250 | 0.012 | - |
| 0.3345 | 11300 | 0.0252 | - |
| 0.3360 | 11350 | 0.003 | - |
| 0.3375 | 11400 | 0.0323 | - |
| 0.3390 | 11450 | 0.0782 | - |
| 0.3404 | 11500 | 0.0661 | - |
| 0.3419 | 11550 | 0.0473 | - |
| 0.3434 | 11600 | 0.1388 | - |
| 0.3449 | 11650 | 0.0092 | - |
| 0.3464 | 11700 | 0.0055 | - |
| 0.3478 | 11750 | 0.0636 | - |
| 0.3493 | 11800 | 0.0301 | - |
| 0.3508 | 11850 | 0.02 | - |
| 0.3523 | 11900 | 0.091 | - |
| 0.3538 | 11950 | 0.0645 | - |
| 0.3552 | 12000 | 0.0131 | - |
| 0.3567 | 12050 | 0.0302 | - |
| 0.3582 | 12100 | 0.0434 | - |
| 0.3597 | 12150 | 0.0007 | - |
| 0.3612 | 12200 | 0.0195 | - |
| 0.3626 | 12250 | 0.0779 | - |
| 0.3641 | 12300 | 0.0794 | - |
| 0.3656 | 12350 | 0.0586 | - |
| 0.3671 | 12400 | 0.0966 | - |
| 0.3686 | 12450 | 0.0289 | - |
| 0.3700 | 12500 | 0.0014 | - |
| 0.3715 | 12550 | 0.0008 | - |
| 0.3730 | 12600 | 0.0174 | - |
| 0.3745 | 12650 | 0.0151 | - |
| 0.3760 | 12700 | 0.0223 | - |
| 0.3774 | 12750 | 0.0034 | - |
| 0.3789 | 12800 | 0.0621 | - |
| 0.3804 | 12850 | 0.0585 | - |
| 0.3819 | 12900 | 0.1385 | - |
| 0.3834 | 12950 | 0.1086 | - |
| 0.3848 | 13000 | 0.0005 | - |
| 0.3863 | 13050 | 0.0178 | - |
| 0.3878 | 13100 | 0.1447 | - |
| 0.3893 | 13150 | 0.1267 | - |
| 0.3908 | 13200 | 0.0823 | - |
| 0.3922 | 13250 | 0.0223 | - |
| 0.3937 | 13300 | 0.0029 | - |
| 0.3952 | 13350 | 0.0273 | - |
| 0.3967 | 13400 | 0.0807 | - |
| 0.3982 | 13450 | 0.0042 | - |
| 0.3996 | 13500 | 0.0023 | - |
| 0.4011 | 13550 | 0.0528 | - |
| 0.4026 | 13600 | 0.0013 | - |
| 0.4041 | 13650 | 0.0413 | - |
| 0.4056 | 13700 | 0.1404 | - |
| 0.4070 | 13750 | 0.1508 | - |
| 0.4085 | 13800 | 0.0214 | - |
| 0.4100 | 13850 | 0.0737 | - |
| 0.4115 | 13900 | 0.0962 | - |
| 0.4130 | 13950 | 0.0536 | - |
| 0.4144 | 14000 | 0.0075 | - |
| 0.4159 | 14050 | 0.0401 | - |
| 0.4174 | 14100 | 0.0268 | - |
| 0.4189 | 14150 | 0.0104 | - |
| 0.4204 | 14200 | 0.0066 | - |
| 0.4218 | 14250 | 0.006 | - |
| 0.4233 | 14300 | 0.0053 | - |
| 0.4248 | 14350 | 0.0367 | - |
| 0.4263 | 14400 | 0.0041 | - |
| 0.4278 | 14450 | 0.0245 | - |
| 0.4292 | 14500 | 0.0351 | - |
| 0.4307 | 14550 | 0.0794 | - |
| 0.4322 | 14600 | 0.0771 | - |
| 0.4337 | 14650 | 0.0172 | - |
| 0.4352 | 14700 | 0.0137 | - |
| 0.4366 | 14750 | 0.044 | - |
| 0.4381 | 14800 | 0.0042 | - |
| 0.4396 | 14850 | 0.0554 | - |
| 0.4411 | 14900 | 0.0794 | - |
| 0.4426 | 14950 | 0.0404 | - |
| 0.4440 | 15000 | 0.0461 | - |
| 0.4455 | 15050 | 0.0176 | - |
| 0.4470 | 15100 | 0.0973 | - |
| 0.4485 | 15150 | 0.0034 | - |
| 0.4500 | 15200 | 0.0056 | - |
| 0.4515 | 15250 | 0.039 | - |
| 0.4529 | 15300 | 0.0136 | - |
| 0.4544 | 15350 | 0.0292 | - |
| 0.4559 | 15400 | 0.0023 | - |
| 0.4574 | 15450 | 0.0709 | - |
| 0.4589 | 15500 | 0.1226 | - |
| 0.4603 | 15550 | 0.0847 | - |
| 0.4618 | 15600 | 0.1088 | - |
| 0.4633 | 15650 | 0.0605 | - |
| 0.4648 | 15700 | 0.0151 | - |
| 0.4663 | 15750 | 0.0475 | - |
| 0.4677 | 15800 | 0.0173 | - |
| 0.4692 | 15850 | 0.0085 | - |
| 0.4707 | 15900 | 0.0491 | - |
| 0.4722 | 15950 | 0.0349 | - |
| 0.4737 | 16000 | 0.0571 | - |
| 0.4751 | 16050 | 0.0867 | - |
| 0.4766 | 16100 | 0.0138 | - |
| 0.4781 | 16150 | 0.015 | - |
| 0.4796 | 16200 | 0.0556 | - |
| 0.4811 | 16250 | 0.0149 | - |
| 0.4825 | 16300 | 0.0598 | - |
| 0.4840 | 16350 | 0.0032 | - |
| 0.4855 | 16400 | 0.0006 | - |
| 0.4870 | 16450 | 0.0479 | - |
| 0.4885 | 16500 | 0.0491 | - |
| 0.4899 | 16550 | 0.1069 | - |
| 0.4914 | 16600 | 0.0164 | - |
| 0.4929 | 16650 | 0.013 | - |
| 0.4944 | 16700 | 0.0123 | - |
| 0.4959 | 16750 | 0.0151 | - |
| 0.4973 | 16800 | 0.0014 | - |
| 0.4988 | 16850 | 0.0028 | - |
| 0.5003 | 16900 | 0.0108 | - |
| 0.5018 | 16950 | 0.0023 | - |
| 0.5033 | 17000 | 0.0495 | - |
| 0.5047 | 17050 | 0.0171 | - |
| 0.5062 | 17100 | 0.0014 | - |
| 0.5077 | 17150 | 0.1108 | - |
| 0.5092 | 17200 | 0.0309 | - |
| 0.5107 | 17250 | 0.0085 | - |
| 0.5121 | 17300 | 0.1128 | - |
| 0.5136 | 17350 | 0.0548 | - |
| 0.5151 | 17400 | 0.034 | - |
| 0.5166 | 17450 | 0.0788 | - |
| 0.5181 | 17500 | 0.072 | - |
| 0.5195 | 17550 | 0.0498 | - |
| 0.5210 | 17600 | 0.0109 | - |
| 0.5225 | 17650 | 0.0738 | - |
| 0.5240 | 17700 | 0.021 | - |
| 0.5255 | 17750 | 0.0364 | - |
| 0.5269 | 17800 | 0.0611 | - |
| 0.5284 | 17850 | 0.0138 | - |
| 0.5299 | 17900 | 0.0109 | - |
| 0.5314 | 17950 | 0.0572 | - |
| 0.5329 | 18000 | 0.0095 | - |
| 0.5343 | 18050 | 0.0501 | - |
| 0.5358 | 18100 | 0.0546 | - |
| 0.5373 | 18150 | 0.0446 | - |
| 0.5388 | 18200 | 0.0645 | - |
| 0.5403 | 18250 | 0.0107 | - |
| 0.5417 | 18300 | 0.0069 | - |
| 0.5432 | 18350 | 0.0235 | - |
| 0.5447 | 18400 | 0.0014 | - |
| 0.5462 | 18450 | 0.0337 | - |
| 0.5477 | 18500 | 0.0142 | - |
| 0.5491 | 18550 | 0.0142 | - |
| 0.5506 | 18600 | 0.0503 | - |
| 0.5521 | 18650 | 0.0015 | - |
| 0.5536 | 18700 | 0.0242 | - |
| 0.5551 | 18750 | 0.0007 | - |
| 0.5565 | 18800 | 0.0529 | - |
| 0.5580 | 18850 | 0.0313 | - |
| 0.5595 | 18900 | 0.0886 | - |
| 0.5610 | 18950 | 0.0335 | - |
| 0.5625 | 19000 | 0.0311 | - |
| 0.5639 | 19050 | 0.0105 | - |
| 0.5654 | 19100 | 0.0116 | - |
| 0.5669 | 19150 | 0.0559 | - |
| 0.5684 | 19200 | 0.0945 | - |
| 0.5699 | 19250 | 0.0826 | - |
| 0.5713 | 19300 | 0.0266 | - |
| 0.5728 | 19350 | 0.0769 | - |
| 0.5743 | 19400 | 0.0912 | - |
| 0.5758 | 19450 | 0.0641 | - |
| 0.5773 | 19500 | 0.0541 | - |
| 0.5787 | 19550 | 0.0769 | - |
| 0.5802 | 19600 | 0.0411 | - |
| 0.5817 | 19650 | 0.115 | - |
| 0.5832 | 19700 | 0.0819 | - |
| 0.5847 | 19750 | 0.071 | - |
| 0.5861 | 19800 | 0.0066 | - |
| 0.5876 | 19850 | 0.0659 | - |
| 0.5891 | 19900 | 0.07 | - |
| 0.5906 | 19950 | 0.0607 | - |
| 0.5921 | 20000 | 0.0474 | - |
| 0.5935 | 20050 | 0.016 | - |
| 0.5950 | 20100 | 0.0122 | - |
| 0.5965 | 20150 | 0.0333 | - |
| 0.5980 | 20200 | 0.0155 | - |
| 0.5995 | 20250 | 0.0005 | - |
| 0.6009 | 20300 | 0.015 | - |
| 0.6024 | 20350 | 0.0014 | - |
| 0.6039 | 20400 | 0.0459 | - |
| 0.6054 | 20450 | 0.0808 | - |
| 0.6069 | 20500 | 0.1034 | - |
| 0.6083 | 20550 | 0.0846 | - |
| 0.6098 | 20600 | 0.071 | - |
| 0.6113 | 20650 | 0.0486 | - |
| 0.6128 | 20700 | 0.022 | - |
| 0.6143 | 20750 | 0.0016 | - |
| 0.6157 | 20800 | 0.0666 | - |
| 0.6172 | 20850 | 0.0461 | - |
| 0.6187 | 20900 | 0.022 | - |
| 0.6202 | 20950 | 0.0449 | - |
| 0.6217 | 21000 | 0.0844 | - |
| 0.6231 | 21050 | 0.0888 | - |
| 0.6246 | 21100 | 0.0219 | - |
| 0.6261 | 21150 | 0.0005 | - |
| 0.6276 | 21200 | 0.025 | - |
| 0.6291 | 21250 | 0.1285 | - |
| 0.6306 | 21300 | 0.0224 | - |
| 0.6320 | 21350 | 0.0444 | - |
| 0.6335 | 21400 | 0.0133 | - |
| 0.6350 | 21450 | 0.0317 | - |
| 0.6365 | 21500 | 0.0457 | - |
| 0.6380 | 21550 | 0.0997 | - |
| 0.6394 | 21600 | 0.0689 | - |
| 0.6409 | 21650 | 0.0275 | - |
| 0.6424 | 21700 | 0.014 | - |
| 0.6439 | 21750 | 0.0304 | - |
| 0.6454 | 21800 | 0.072 | - |
| 0.6468 | 21850 | 0.0556 | - |
| 0.6483 | 21900 | 0.0902 | - |
| 0.6498 | 21950 | 0.0153 | - |
| 0.6513 | 22000 | 0.0759 | - |
| 0.6528 | 22050 | 0.0905 | - |
| 0.6542 | 22100 | 0.1058 | - |
| 0.6557 | 22150 | 0.0524 | - |
| 0.6572 | 22200 | 0.0711 | - |
| 0.6587 | 22250 | 0.0201 | - |
| 0.6602 | 22300 | 0.0367 | - |
| 0.6616 | 22350 | 0.0513 | - |
| 0.6631 | 22400 | 0.0187 | - |
| 0.6646 | 22450 | 0.027 | - |
| 0.6661 | 22500 | 0.0643 | - |
| 0.6676 | 22550 | 0.0334 | - |
| 0.6690 | 22600 | 0.023 | - |
| 0.6705 | 22650 | 0.1438 | - |
| 0.6720 | 22700 | 0.0051 | - |
| 0.6735 | 22750 | 0.0335 | - |
| 0.6750 | 22800 | 0.0879 | - |
| 0.6764 | 22850 | 0.003 | - |
| 0.6779 | 22900 | 0.0061 | - |
| 0.6794 | 22950 | 0.104 | - |
| 0.6809 | 23000 | 0.0575 | - |
| 0.6824 | 23050 | 0.0009 | - |
| 0.6838 | 23100 | 0.001 | - |
| 0.6853 | 23150 | 0.01 | - |
| 0.6868 | 23200 | 0.0018 | - |
| 0.6883 | 23250 | 0.036 | - |
| 0.6898 | 23300 | 0.0011 | - |
| 0.6912 | 23350 | 0.0033 | - |
| 0.6927 | 23400 | 0.117 | - |
| 0.6942 | 23450 | 0.0177 | - |
| 0.6957 | 23500 | 0.0547 | - |
| 0.6972 | 23550 | 0.061 | - |
| 0.6986 | 23600 | 0.06 | - |
| 0.7001 | 23650 | 0.0259 | - |
| 0.7016 | 23700 | 0.1325 | - |
| 0.7031 | 23750 | 0.0298 | - |
| 0.7046 | 23800 | 0.0046 | - |
| 0.7060 | 23850 | 0.0129 | - |
| 0.7075 | 23900 | 0.0085 | - |
| 0.7090 | 23950 | 0.015 | - |
| 0.7105 | 24000 | 0.0205 | - |
| 0.7120 | 24050 | 0.0135 | - |
| 0.7134 | 24100 | 0.0408 | - |
| 0.7149 | 24150 | 0.0014 | - |
| 0.7164 | 24200 | 0.0305 | - |
| 0.7179 | 24250 | 0.0241 | - |
| 0.7194 | 24300 | 0.0621 | - |
| 0.7208 | 24350 | 0.0014 | - |
| 0.7223 | 24400 | 0.0522 | - |
| 0.7238 | 24450 | 0.1001 | - |
| 0.7253 | 24500 | 0.0007 | - |
| 0.7268 | 24550 | 0.0045 | - |
| 0.7282 | 24600 | 0.0282 | - |
| 0.7297 | 24650 | 0.022 | - |
| 0.7312 | 24700 | 0.107 | - |
| 0.7327 | 24750 | 0.0363 | - |
| 0.7342 | 24800 | 0.0943 | - |
| 0.7356 | 24850 | 0.0015 | - |
| 0.7371 | 24900 | 0.0266 | - |
| 0.7386 | 24950 | 0.0113 | - |
| 0.7401 | 25000 | 0.0283 | - |
| 0.7416 | 25050 | 0.1304 | - |
| 0.7430 | 25100 | 0.0199 | - |
| 0.7445 | 25150 | 0.0014 | - |
| 0.7460 | 25200 | 0.0594 | - |
| 0.7475 | 25250 | 0.1188 | - |
| 0.7490 | 25300 | 0.0325 | - |
| 0.7504 | 25350 | 0.0715 | - |
| 0.7519 | 25400 | 0.0352 | - |
| 0.7534 | 25450 | 0.0165 | - |
| 0.7549 | 25500 | 0.0025 | - |
| 0.7564 | 25550 | 0.0017 | - |
| 0.7578 | 25600 | 0.0584 | - |
| 0.7593 | 25650 | 0.0251 | - |
| 0.7608 | 25700 | 0.0155 | - |
| 0.7623 | 25750 | 0.0304 | - |
| 0.7638 | 25800 | 0.0461 | - |
| 0.7652 | 25850 | 0.0347 | - |
| 0.7667 | 25900 | 0.1044 | - |
| 0.7682 | 25950 | 0.0174 | - |
| 0.7697 | 26000 | 0.0077 | - |
| 0.7712 | 26050 | 0.0264 | - |
| 0.7726 | 26100 | 0.0437 | - |
| 0.7741 | 26150 | 0.053 | - |
| 0.7756 | 26200 | 0.0721 | - |
| 0.7771 | 26250 | 0.0278 | - |
| 0.7786 | 26300 | 0.0107 | - |
| 0.7800 | 26350 | 0.0237 | - |
| 0.7815 | 26400 | 0.035 | - |
| 0.7830 | 26450 | 0.0322 | - |
| 0.7845 | 26500 | 0.0641 | - |
| 0.7860 | 26550 | 0.0219 | - |
| 0.7874 | 26600 | 0.0256 | - |
| 0.7889 | 26650 | 0.0559 | - |
| 0.7904 | 26700 | 0.0463 | - |
| 0.7919 | 26750 | 0.0992 | - |
| 0.7934 | 26800 | 0.062 | - |
| 0.7948 | 26850 | 0.0038 | - |
| 0.7963 | 26900 | 0.0521 | - |
| 0.7978 | 26950 | 0.011 | - |
| 0.7993 | 27000 | 0.0109 | - |
| 0.8008 | 27050 | 0.0483 | - |
| 0.8022 | 27100 | 0.0379 | - |
| 0.8037 | 27150 | 0.0231 | - |
| 0.8052 | 27200 | 0.0888 | - |
| 0.8067 | 27250 | 0.0197 | - |
| 0.8082 | 27300 | 0.0003 | - |
| 0.8097 | 27350 | 0.0157 | - |
| 0.8111 | 27400 | 0.0192 | - |
| 0.8126 | 27450 | 0.0802 | - |
| 0.8141 | 27500 | 0.0407 | - |
| 0.8156 | 27550 | 0.0351 | - |
| 0.8171 | 27600 | 0.001 | - |
| 0.8185 | 27650 | 0.0007 | - |
| 0.8200 | 27700 | 0.021 | - |
| 0.8215 | 27750 | 0.0548 | - |
| 0.8230 | 27800 | 0.0442 | - |
| 0.8245 | 27850 | 0.0561 | - |
| 0.8259 | 27900 | 0.0181 | - |
| 0.8274 | 27950 | 0.0669 | - |
| 0.8289 | 28000 | 0.016 | - |
| 0.8304 | 28050 | 0.0817 | - |
| 0.8319 | 28100 | 0.0221 | - |
| 0.8333 | 28150 | 0.0014 | - |
| 0.8348 | 28200 | 0.0195 | - |
| 0.8363 | 28250 | 0.0735 | - |
| 0.8378 | 28300 | 0.002 | - |
| 0.8393 | 28350 | 0.0269 | - |
| 0.8407 | 28400 | 0.0365 | - |
| 0.8422 | 28450 | 0.0825 | - |
| 0.8437 | 28500 | 0.0382 | - |
| 0.8452 | 28550 | 0.0144 | - |
| 0.8467 | 28600 | 0.0529 | - |
| 0.8481 | 28650 | 0.0042 | - |
| 0.8496 | 28700 | 0.0532 | - |
| 0.8511 | 28750 | 0.0195 | - |
| 0.8526 | 28800 | 0.018 | - |
| 0.8541 | 28850 | 0.005 | - |
| 0.8555 | 28900 | 0.0694 | - |
| 0.8570 | 28950 | 0.0006 | - |
| 0.8585 | 29000 | 0.0169 | - |
| 0.8600 | 29050 | 0.0188 | - |
| 0.8615 | 29100 | 0.0002 | - |
| 0.8629 | 29150 | 0.0246 | - |
| 0.8644 | 29200 | 0.001 | - |
| 0.8659 | 29250 | 0.0017 | - |
| 0.8674 | 29300 | 0.0169 | - |
| 0.8689 | 29350 | 0.0621 | - |
| 0.8703 | 29400 | 0.0017 | - |
| 0.8718 | 29450 | 0.0008 | - |
| 0.8733 | 29500 | 0.0086 | - |
| 0.8748 | 29550 | 0.0214 | - |
| 0.8763 | 29600 | 0.0495 | - |
| 0.8777 | 29650 | 0.0864 | - |
| 0.8792 | 29700 | 0.0844 | - |
| 0.8807 | 29750 | 0.0738 | - |
| 0.8822 | 29800 | 0.0007 | - |
| 0.8837 | 29850 | 0.0408 | - |
| 0.8851 | 29900 | 0.0025 | - |
| 0.8866 | 29950 | 0.0313 | - |
| 0.8881 | 30000 | 0.0178 | - |
| 0.8896 | 30050 | 0.0123 | - |
| 0.8911 | 30100 | 0.0001 | - |
| 0.8925 | 30150 | 0.0031 | - |
| 0.8940 | 30200 | 0.0035 | - |
| 0.8955 | 30250 | 0.0278 | - |
| 0.8970 | 30300 | 0.034 | - |
| 0.8985 | 30350 | 0.0255 | - |
| 0.8999 | 30400 | 0.0012 | - |
| 0.9014 | 30450 | 0.0756 | - |
| 0.9029 | 30500 | 0.0813 | - |
| 0.9044 | 30550 | 0.0024 | - |
| 0.9059 | 30600 | 0.1491 | - |
| 0.9073 | 30650 | 0.0009 | - |
| 0.9088 | 30700 | 0.0299 | - |
| 0.9103 | 30750 | 0.0226 | - |
| 0.9118 | 30800 | 0.0198 | - |
| 0.9133 | 30850 | 0.0019 | - |
| 0.9147 | 30900 | 0.0406 | - |
| 0.9162 | 30950 | 0.0168 | - |
| 0.9177 | 31000 | 0.0409 | - |
| 0.9192 | 31050 | 0.0016 | - |
| 0.9207 | 31100 | 0.0172 | - |
| 0.9221 | 31150 | 0.0131 | - |
| 0.9236 | 31200 | 0.1433 | - |
| 0.9251 | 31250 | 0.0316 | - |
| 0.9266 | 31300 | 0.0774 | - |
| 0.9281 | 31350 | 0.1256 | - |
| 0.9295 | 31400 | 0.0257 | - |
| 0.9310 | 31450 | 0.2166 | - |
| 0.9325 | 31500 | 0.0023 | - |
| 0.9340 | 31550 | 0.0261 | - |
| 0.9355 | 31600 | 0.0143 | - |
| 0.9369 | 31650 | 0.0005 | - |
| 0.9384 | 31700 | 0.0522 | - |
| 0.9399 | 31750 | 0.024 | - |
| 0.9414 | 31800 | 0.0353 | - |
| 0.9429 | 31850 | 0.0022 | - |
| 0.9443 | 31900 | 0.0006 | - |
| 0.9458 | 31950 | 0.0321 | - |
| 0.9473 | 32000 | 0.0879 | - |
| 0.9488 | 32050 | 0.0007 | - |
| 0.9503 | 32100 | 0.003 | - |
| 0.9517 | 32150 | 0.0295 | - |
| 0.9532 | 32200 | 0.0817 | - |
| 0.9547 | 32250 | 0.0345 | - |
| 0.9562 | 32300 | 0.0004 | - |
| 0.9577 | 32350 | 0.0112 | - |
| 0.9591 | 32400 | 0.0284 | - |
| 0.9606 | 32450 | 0.0654 | - |
| 0.9621 | 32500 | 0.036 | - |
| 0.9636 | 32550 | 0.0181 | - |
| 0.9651 | 32600 | 0.0374 | - |
| 0.9665 | 32650 | 0.0022 | - |
| 0.9680 | 32700 | 0.0706 | - |
| 0.9695 | 32750 | 0.0009 | - |
| 0.9710 | 32800 | 0.0077 | - |
| 0.9725 | 32850 | 0.0016 | - |
| 0.9739 | 32900 | 0.0586 | - |
| 0.9754 | 32950 | 0.0134 | - |
| 0.9769 | 33000 | 0.0108 | - |
| 0.9784 | 33050 | 0.0839 | - |
| 0.9799 | 33100 | 0.0032 | - |
| 0.9813 | 33150 | 0.0152 | - |
| 0.9828 | 33200 | 0.049 | - |
| 0.9843 | 33250 | 0.038 | - |
| 0.9858 | 33300 | 0.0302 | - |
| 0.9873 | 33350 | 0.0193 | - |
| 0.9888 | 33400 | 0.0291 | - |
| 0.9902 | 33450 | 0.0083 | - |
| 0.9917 | 33500 | 0.0014 | - |
| 0.9932 | 33550 | 0.0223 | - |
| 0.9947 | 33600 | 0.0154 | - |
| 0.9962 | 33650 | 0.0788 | - |
| 0.9976 | 33700 | 0.0567 | - |
| 0.9991 | 33750 | 0.0207 | - |
### 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}
}
```