---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: cointegrated/rubert-tiny2
metrics:
- accuracy
widget:
- text: плюсов -:Еще из плюсов - при заказе банкета есть специальное предложение по
алкоголю ( можно приобрети вино , шампанское и водку по ценам производителя )
.
- text: телятины:Заказала я салат , большую порцию , как ни странно его принесли в
большом количестве , из горячего заказала стейк из телятины , мясо было мягким
и сочным , и конечно же мое самое любимое это десерт , заказала тирамису , и правильно
сделала , очень вкусный десерт .
- text: бекона:Салат цезарь вся тарелка это листья салата , немного бекона по кругу
и все это в соусе , сверху сыр ( цезарь готовится с курицей ) .
- text: ресторан:По моей рекомендации этот ресторан посетили несколько пар моих друзей
и также остались довольны .
- text: блюда:Для меня же минус был в том , что сами блюда слишком специфические .
pipeline_tag: text-classification
inference: false
---
# SetFit Aspect Model with cointegrated/rubert-tiny2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [cointegrated/rubert-tiny2](https://huggingface.co./cointegrated/rubert-tiny2) 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. In particular, this model is in charge of filtering aspect span candidates.
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.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [cointegrated/rubert-tiny2](https://huggingface.co./cointegrated/rubert-tiny2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** ru_core_news_lg
- **SetFitABSA Aspect Model:** [isolation-forest/setfit-absa-aspect](https://huggingface.co./isolation-forest/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [isolation-forest/setfit-absa-polarity](https://huggingface.co./isolation-forest/setfit-absa-polarity)
- **Maximum Sequence Length:** 2048 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 |
|:----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | - 'порции:И порции " достойные " .'
- 'официантка:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'
- 'Обслуживание:Обслуживание не впечатлило .'
|
| no aspect | - 'итоге:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'
- 'счет:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'
- 'пункта:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'
|
## 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 AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"isolation-forest/setfit-absa-aspect",
"isolation-forest/setfit-absa-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 31.9677 | 88 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 797 |
| aspect | 256 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.25 | - |
| 0.0011 | 50 | 0.1976 | - |
| 0.0023 | 100 | 0.2289 | - |
| 0.0034 | 150 | 0.2826 | - |
| 0.0046 | 200 | 0.2361 | - |
| 0.0057 | 250 | 0.2766 | - |
| 0.0068 | 300 | 0.2723 | - |
| 0.0080 | 350 | 0.2402 | - |
| 0.0091 | 400 | 0.2678 | - |
| 0.0103 | 450 | 0.2511 | - |
| 0.0114 | 500 | 0.21 | - |
| 0.0125 | 550 | 0.2503 | - |
| 0.0137 | 600 | 0.2614 | - |
| 0.0148 | 650 | 0.218 | - |
| 0.0160 | 700 | 0.2482 | - |
| 0.0171 | 750 | 0.2091 | - |
| 0.0182 | 800 | 0.2477 | - |
| 0.0194 | 850 | 0.2531 | - |
| 0.0205 | 900 | 0.1878 | - |
| 0.0217 | 950 | 0.2416 | - |
| 0.0228 | 1000 | 0.2245 | - |
| 0.0239 | 1050 | 0.2367 | - |
| 0.0251 | 1100 | 0.2376 | - |
| 0.0262 | 1150 | 0.2271 | - |
| 0.0274 | 1200 | 0.228 | - |
| 0.0285 | 1250 | 0.2362 | - |
| 0.0296 | 1300 | 0.2308 | - |
| 0.0308 | 1350 | 0.2326 | - |
| 0.0319 | 1400 | 0.2535 | - |
| 0.0331 | 1450 | 0.177 | - |
| 0.0342 | 1500 | 0.2595 | - |
| 0.0353 | 1550 | 0.2289 | - |
| 0.0365 | 1600 | 0.2378 | - |
| 0.0376 | 1650 | 0.2111 | - |
| 0.0388 | 1700 | 0.2556 | - |
| 0.0399 | 1750 | 0.2054 | - |
| 0.0410 | 1800 | 0.1949 | - |
| 0.0422 | 1850 | 0.2065 | - |
| 0.0433 | 1900 | 0.1907 | - |
| 0.0445 | 1950 | 0.2325 | - |
| 0.0456 | 2000 | 0.2313 | - |
| 0.0467 | 2050 | 0.1713 | - |
| 0.0479 | 2100 | 0.1786 | - |
| 0.0490 | 2150 | 0.2258 | - |
| 0.0502 | 2200 | 0.1102 | - |
| 0.0513 | 2250 | 0.1714 | - |
| 0.0524 | 2300 | 0.2325 | - |
| 0.0536 | 2350 | 0.2287 | - |
| 0.0547 | 2400 | 0.2901 | - |
| 0.0559 | 2450 | 0.1763 | - |
| 0.0570 | 2500 | 0.223 | - |
| 0.0581 | 2550 | 0.0784 | - |
| 0.0593 | 2600 | 0.2069 | - |
| 0.0604 | 2650 | 0.1353 | - |
| 0.0616 | 2700 | 0.1729 | - |
| 0.0627 | 2750 | 0.1753 | - |
| 0.0638 | 2800 | 0.2243 | - |
| 0.0650 | 2850 | 0.1151 | - |
| 0.0661 | 2900 | 0.2547 | - |
| 0.0673 | 2950 | 0.1414 | - |
| 0.0684 | 3000 | 0.1771 | - |
| 0.0695 | 3050 | 0.1275 | - |
| 0.0707 | 3100 | 0.0541 | - |
| 0.0718 | 3150 | 0.0962 | - |
| 0.0730 | 3200 | 0.1953 | - |
| 0.0741 | 3250 | 0.0787 | - |
| 0.0752 | 3300 | 0.0766 | - |
| 0.0764 | 3350 | 0.1537 | - |
| 0.0775 | 3400 | 0.0957 | - |
| 0.0787 | 3450 | 0.0975 | - |
| 0.0798 | 3500 | 0.0359 | - |
| 0.0809 | 3550 | 0.0402 | - |
| 0.0821 | 3600 | 0.0377 | - |
| 0.0832 | 3650 | 0.0486 | - |
| 0.0844 | 3700 | 0.1206 | - |
| 0.0855 | 3750 | 0.0504 | - |
| 0.0866 | 3800 | 0.1072 | - |
| 0.0878 | 3850 | 0.0838 | - |
| 0.0889 | 3900 | 0.1222 | - |
| 0.0901 | 3950 | 0.0463 | - |
| 0.0912 | 4000 | 0.0781 | - |
| 0.0923 | 4050 | 0.031 | - |
| 0.0935 | 4100 | 0.1063 | - |
| 0.0946 | 4150 | 0.0643 | - |
| 0.0958 | 4200 | 0.0624 | - |
| 0.0969 | 4250 | 0.0283 | - |
| 0.0980 | 4300 | 0.0527 | - |
| 0.0992 | 4350 | 0.0153 | - |
| 0.1003 | 4400 | 0.0765 | - |
| 0.1015 | 4450 | 0.0245 | - |
| 0.1026 | 4500 | 0.0494 | - |
| 0.1037 | 4550 | 0.0218 | - |
| 0.1049 | 4600 | 0.0086 | - |
| 0.1060 | 4650 | 0.0245 | - |
| 0.1072 | 4700 | 0.0047 | - |
| 0.1083 | 4750 | 0.0284 | - |
| 0.1094 | 4800 | 0.0045 | - |
| 0.1106 | 4850 | 0.0683 | - |
| 0.1117 | 4900 | 0.0234 | - |
| 0.1129 | 4950 | 0.0584 | - |
| 0.1140 | 5000 | 0.1212 | - |
| 0.1151 | 5050 | 0.0052 | - |
| 0.1163 | 5100 | 0.065 | - |
| 0.1174 | 5150 | 0.003 | - |
| 0.1186 | 5200 | 0.0937 | - |
| 0.1197 | 5250 | 0.0038 | - |
| 0.1208 | 5300 | 0.0061 | - |
| 0.1220 | 5350 | 0.0038 | - |
| 0.1231 | 5400 | 0.0674 | - |
| 0.1243 | 5450 | 0.0039 | - |
| 0.1254 | 5500 | 0.0088 | - |
| 0.1265 | 5550 | 0.0028 | - |
| 0.1277 | 5600 | 0.0031 | - |
| 0.1288 | 5650 | 0.0035 | - |
| 0.1300 | 5700 | 0.0545 | - |
| 0.1311 | 5750 | 0.0021 | - |
| 0.1322 | 5800 | 0.0056 | - |
| 0.1334 | 5850 | 0.0019 | - |
| 0.1345 | 5900 | 0.0023 | - |
| 0.1356 | 5950 | 0.0595 | - |
| 0.1368 | 6000 | 0.0019 | - |
| 0.1379 | 6050 | 0.0031 | - |
| 0.1391 | 6100 | 0.0025 | - |
| 0.1402 | 6150 | 0.0026 | - |
| 0.1413 | 6200 | 0.0032 | - |
| 0.1425 | 6250 | 0.0019 | - |
| 0.1436 | 6300 | 0.0761 | - |
| 0.1448 | 6350 | 0.0446 | - |
| 0.1459 | 6400 | 0.002 | - |
| 0.1470 | 6450 | 0.008 | - |
| 0.1482 | 6500 | 0.0044 | - |
| 0.1493 | 6550 | 0.0024 | - |
| 0.1505 | 6600 | 0.0026 | - |
| 0.1516 | 6650 | 0.0477 | - |
| 0.1527 | 6700 | 0.0023 | - |
| 0.1539 | 6750 | 0.0024 | - |
| 0.1550 | 6800 | 0.0016 | - |
| 0.1562 | 6850 | 0.0023 | - |
| 0.1573 | 6900 | 0.0017 | - |
| 0.1584 | 6950 | 0.0026 | - |
| 0.1596 | 7000 | 0.0602 | - |
| 0.1607 | 7050 | 0.002 | - |
| 0.1619 | 7100 | 0.0014 | - |
| 0.1630 | 7150 | 0.0019 | - |
| 0.1641 | 7200 | 0.0019 | - |
| 0.1653 | 7250 | 0.0021 | - |
| 0.1664 | 7300 | 0.0563 | - |
| 0.1676 | 7350 | 0.0017 | - |
| 0.1687 | 7400 | 0.0019 | - |
| 0.1698 | 7450 | 0.0017 | - |
| 0.1710 | 7500 | 0.0014 | - |
| 0.1721 | 7550 | 0.002 | - |
| 0.1733 | 7600 | 0.0028 | - |
| 0.1744 | 7650 | 0.002 | - |
| 0.1755 | 7700 | 0.0021 | - |
| 0.1767 | 7750 | 0.002 | - |
| 0.1778 | 7800 | 0.0017 | - |
| 0.1790 | 7850 | 0.0579 | - |
| 0.1801 | 7900 | 0.0089 | - |
| 0.1812 | 7950 | 0.0016 | - |
| 0.1824 | 8000 | 0.104 | - |
| 0.1835 | 8050 | 0.0241 | - |
| 0.1847 | 8100 | 0.0015 | - |
| 0.1858 | 8150 | 0.0039 | - |
| 0.1869 | 8200 | 0.0018 | - |
| 0.1881 | 8250 | 0.0018 | - |
| 0.1892 | 8300 | 0.0012 | - |
| 0.1904 | 8350 | 0.0015 | - |
| 0.1915 | 8400 | 0.0016 | - |
| 0.1926 | 8450 | 0.0017 | - |
| 0.1938 | 8500 | 0.0647 | - |
| 0.1949 | 8550 | 0.0013 | - |
| 0.1961 | 8600 | 0.0014 | - |
| 0.1972 | 8650 | 0.1705 | - |
| 0.1983 | 8700 | 0.0036 | - |
| 0.1995 | 8750 | 0.0014 | - |
| 0.2006 | 8800 | 0.0021 | - |
| 0.2018 | 8850 | 0.0019 | - |
| 0.2029 | 8900 | 0.0018 | - |
| 0.2040 | 8950 | 0.0018 | - |
| 0.2052 | 9000 | 0.001 | - |
| 0.2063 | 9050 | 0.0012 | - |
| 0.2075 | 9100 | 0.0013 | - |
| 0.2086 | 9150 | 0.0014 | - |
| 0.2097 | 9200 | 0.0609 | - |
| 0.2109 | 9250 | 0.0026 | - |
| 0.2120 | 9300 | 0.0012 | - |
| 0.2132 | 9350 | 0.0023 | - |
| 0.2143 | 9400 | 0.0043 | - |
| 0.2154 | 9450 | 0.0511 | - |
| 0.2166 | 9500 | 0.0012 | - |
| 0.2177 | 9550 | 0.002 | - |
| 0.2189 | 9600 | 0.0016 | - |
| 0.2200 | 9650 | 0.0124 | - |
| 0.2211 | 9700 | 0.0046 | - |
| 0.2223 | 9750 | 0.0012 | - |
| 0.2234 | 9800 | 0.0014 | - |
| 0.2246 | 9850 | 0.0016 | - |
| 0.2257 | 9900 | 0.0596 | - |
| 0.2268 | 9950 | 0.0013 | - |
| 0.2280 | 10000 | 0.0021 | - |
| 0.2291 | 10050 | 0.0012 | - |
| 0.2303 | 10100 | 0.057 | - |
| 0.2314 | 10150 | 0.0028 | - |
| 0.2325 | 10200 | 0.0014 | - |
| 0.2337 | 10250 | 0.0014 | - |
| 0.2348 | 10300 | 0.0019 | - |
| 0.2360 | 10350 | 0.0014 | - |
| 0.2371 | 10400 | 0.0015 | - |
| 0.2382 | 10450 | 0.0569 | - |
| 0.2394 | 10500 | 0.0012 | - |
| 0.2405 | 10550 | 0.0023 | - |
| 0.2417 | 10600 | 0.0013 | - |
| 0.2428 | 10650 | 0.0011 | - |
| 0.2439 | 10700 | 0.0191 | - |
| 0.2451 | 10750 | 0.0015 | - |
| 0.2462 | 10800 | 0.0022 | - |
| 0.2474 | 10850 | 0.0547 | - |
| 0.2485 | 10900 | 0.003 | - |
| 0.2496 | 10950 | 0.0013 | - |
| 0.2508 | 11000 | 0.0018 | - |
| 0.2519 | 11050 | 0.0016 | - |
| 0.2531 | 11100 | 0.0013 | - |
| 0.2542 | 11150 | 0.0019 | - |
| 0.2553 | 11200 | 0.0011 | - |
| 0.2565 | 11250 | 0.0555 | - |
| 0.2576 | 11300 | 0.0012 | - |
| 0.2588 | 11350 | 0.0016 | - |
| 0.2599 | 11400 | 0.004 | - |
| 0.2610 | 11450 | 0.0014 | - |
| 0.2622 | 11500 | 0.0016 | - |
| 0.2633 | 11550 | 0.0037 | - |
| 0.2645 | 11600 | 0.0014 | - |
| 0.2656 | 11650 | 0.0252 | - |
| 0.2667 | 11700 | 0.0011 | - |
| 0.2679 | 11750 | 0.0013 | - |
| 0.2690 | 11800 | 0.0552 | - |
| 0.2702 | 11850 | 0.0019 | - |
| 0.2713 | 11900 | 0.0009 | - |
| 0.2724 | 11950 | 0.0015 | - |
| 0.2736 | 12000 | 0.0362 | - |
| 0.2747 | 12050 | 0.001 | - |
| 0.2759 | 12100 | 0.0022 | - |
| 0.2770 | 12150 | 0.0013 | - |
| 0.2781 | 12200 | 0.0013 | - |
| 0.2793 | 12250 | 0.001 | - |
| 0.2804 | 12300 | 0.0027 | - |
| 0.2816 | 12350 | 0.0013 | - |
| 0.2827 | 12400 | 0.0014 | - |
| 0.2838 | 12450 | 0.001 | - |
| 0.2850 | 12500 | 0.0014 | - |
| 0.2861 | 12550 | 0.0014 | - |
| 0.2873 | 12600 | 0.0407 | - |
| 0.2884 | 12650 | 0.0009 | - |
| 0.2895 | 12700 | 0.0014 | - |
| 0.2907 | 12750 | 0.001 | - |
| 0.2918 | 12800 | 0.0011 | - |
| 0.2930 | 12850 | 0.0012 | - |
| 0.2941 | 12900 | 0.0011 | - |
| 0.2952 | 12950 | 0.0016 | - |
| 0.2964 | 13000 | 0.0012 | - |
| 0.2975 | 13050 | 0.001 | - |
| 0.2987 | 13100 | 0.0026 | - |
| 0.2998 | 13150 | 0.0015 | - |
| 0.3009 | 13200 | 0.0022 | - |
| 0.3021 | 13250 | 0.0007 | - |
| 0.3032 | 13300 | 0.001 | - |
| 0.3044 | 13350 | 0.0012 | - |
| 0.3055 | 13400 | 0.0019 | - |
| 0.3066 | 13450 | 0.0016 | - |
| 0.3078 | 13500 | 0.0938 | - |
| 0.3089 | 13550 | 0.0009 | - |
| 0.3101 | 13600 | 0.0016 | - |
| 0.3112 | 13650 | 0.0014 | - |
| 0.3123 | 13700 | 0.032 | - |
| 0.3135 | 13750 | 0.0013 | - |
| 0.3146 | 13800 | 0.0219 | - |
| 0.3158 | 13850 | 0.0012 | - |
| 0.3169 | 13900 | 0.0012 | - |
| 0.3180 | 13950 | 0.0214 | - |
| 0.3192 | 14000 | 0.001 | - |
| 0.3203 | 14050 | 0.0033 | - |
| 0.3215 | 14100 | 0.0009 | - |
| 0.3226 | 14150 | 0.001 | - |
| 0.3237 | 14200 | 0.001 | - |
| 0.3249 | 14250 | 0.0014 | - |
| 0.3260 | 14300 | 0.0075 | - |
| 0.3272 | 14350 | 0.0015 | - |
| 0.3283 | 14400 | 0.0018 | - |
| 0.3294 | 14450 | 0.0011 | - |
| 0.3306 | 14500 | 0.0008 | - |
| 0.3317 | 14550 | 0.0381 | - |
| 0.3329 | 14600 | 0.0007 | - |
| 0.3340 | 14650 | 0.0009 | - |
| 0.3351 | 14700 | 0.001 | - |
| 0.3363 | 14750 | 0.0011 | - |
| 0.3374 | 14800 | 0.0304 | - |
| 0.3386 | 14850 | 0.0008 | - |
| 0.3397 | 14900 | 0.0007 | - |
| 0.3408 | 14950 | 0.0013 | - |
| 0.3420 | 15000 | 0.0135 | - |
| 0.3431 | 15050 | 0.001 | - |
| 0.3443 | 15100 | 0.0007 | - |
| 0.3454 | 15150 | 0.0008 | - |
| 0.3465 | 15200 | 0.0018 | - |
| 0.3477 | 15250 | 0.0009 | - |
| 0.3488 | 15300 | 0.0013 | - |
| 0.3500 | 15350 | 0.0018 | - |
| 0.3511 | 15400 | 0.0014 | - |
| 0.3522 | 15450 | 0.0051 | - |
| 0.3534 | 15500 | 0.0009 | - |
| 0.3545 | 15550 | 0.0007 | - |
| 0.3557 | 15600 | 0.0006 | - |
| 0.3568 | 15650 | 0.001 | - |
| 0.3579 | 15700 | 0.001 | - |
| 0.3591 | 15750 | 0.0015 | - |
| 0.3602 | 15800 | 0.0006 | - |
| 0.3614 | 15850 | 0.0005 | - |
| 0.3625 | 15900 | 0.0009 | - |
| 0.3636 | 15950 | 0.0052 | - |
| 0.3648 | 16000 | 0.0006 | - |
| 0.3659 | 16050 | 0.0013 | - |
| 0.3671 | 16100 | 0.001 | - |
| 0.3682 | 16150 | 0.0007 | - |
| 0.3693 | 16200 | 0.001 | - |
| 0.3705 | 16250 | 0.0008 | - |
| 0.3716 | 16300 | 0.0006 | - |
| 0.3728 | 16350 | 0.0026 | - |
| 0.3739 | 16400 | 0.0012 | - |
| 0.3750 | 16450 | 0.0008 | - |
| 0.3762 | 16500 | 0.0008 | - |
| 0.3773 | 16550 | 0.001 | - |
| 0.3785 | 16600 | 0.0289 | - |
| 0.3796 | 16650 | 0.0012 | - |
| 0.3807 | 16700 | 0.0007 | - |
| 0.3819 | 16750 | 0.0009 | - |
| 0.3830 | 16800 | 0.0006 | - |
| 0.3842 | 16850 | 0.0007 | - |
| 0.3853 | 16900 | 0.0008 | - |
| 0.3864 | 16950 | 0.0007 | - |
| 0.3876 | 17000 | 0.0011 | - |
| 0.3887 | 17050 | 0.0032 | - |
| 0.3899 | 17100 | 0.0009 | - |
| 0.3910 | 17150 | 0.0007 | - |
| 0.3921 | 17200 | 0.0008 | - |
| 0.3933 | 17250 | 0.0008 | - |
| 0.3944 | 17300 | 0.0007 | - |
| 0.3955 | 17350 | 0.0012 | - |
| 0.3967 | 17400 | 0.0044 | - |
| 0.3978 | 17450 | 0.0006 | - |
| 0.3990 | 17500 | 0.0006 | - |
| 0.4001 | 17550 | 0.0006 | - |
| 0.4012 | 17600 | 0.002 | - |
| 0.4024 | 17650 | 0.0007 | - |
| 0.4035 | 17700 | 0.0005 | - |
| 0.4047 | 17750 | 0.0005 | - |
| 0.4058 | 17800 | 0.0005 | - |
| 0.4069 | 17850 | 0.0013 | - |
| 0.4081 | 17900 | 0.0004 | - |
| 0.4092 | 17950 | 0.0005 | - |
| 0.4104 | 18000 | 0.0007 | - |
| 0.4115 | 18050 | 0.0007 | - |
| 0.4126 | 18100 | 0.0007 | - |
| 0.4138 | 18150 | 0.0006 | - |
| 0.4149 | 18200 | 0.0004 | - |
| 0.4161 | 18250 | 0.0005 | - |
| 0.4172 | 18300 | 0.0307 | - |
| 0.4183 | 18350 | 0.001 | - |
| 0.4195 | 18400 | 0.0012 | - |
| 0.4206 | 18450 | 0.0007 | - |
| 0.4218 | 18500 | 0.0007 | - |
| 0.4229 | 18550 | 0.001 | - |
| 0.4240 | 18600 | 0.0006 | - |
| 0.4252 | 18650 | 0.0195 | - |
| 0.4263 | 18700 | 0.0583 | - |
| 0.4275 | 18750 | 0.0005 | - |
| 0.4286 | 18800 | 0.0011 | - |
| 0.4297 | 18850 | 0.0006 | - |
| 0.4309 | 18900 | 0.0007 | - |
| 0.4320 | 18950 | 0.0005 | - |
| 0.4332 | 19000 | 0.0005 | - |
| 0.4343 | 19050 | 0.0007 | - |
| 0.4354 | 19100 | 0.0008 | - |
| 0.4366 | 19150 | 0.0006 | - |
| 0.4377 | 19200 | 0.0007 | - |
| 0.4389 | 19250 | 0.0005 | - |
| 0.4400 | 19300 | 0.0004 | - |
| 0.4411 | 19350 | 0.0005 | - |
| 0.4423 | 19400 | 0.0006 | - |
| 0.4434 | 19450 | 0.0006 | - |
| 0.4446 | 19500 | 0.0005 | - |
| 0.4457 | 19550 | 0.0006 | - |
| 0.4468 | 19600 | 0.0005 | - |
| 0.4480 | 19650 | 0.0013 | - |
| 0.4491 | 19700 | 0.0006 | - |
| 0.4503 | 19750 | 0.0006 | - |
| 0.4514 | 19800 | 0.0095 | - |
| 0.4525 | 19850 | 0.0066 | - |
| 0.4537 | 19900 | 0.0005 | - |
| 0.4548 | 19950 | 0.0008 | - |
| 0.4560 | 20000 | 0.0006 | - |
| 0.4571 | 20050 | 0.0005 | - |
| 0.4582 | 20100 | 0.0004 | - |
| 0.4594 | 20150 | 0.0151 | - |
| 0.4605 | 20200 | 0.0004 | - |
| 0.4617 | 20250 | 0.001 | - |
| 0.4628 | 20300 | 0.0005 | - |
| 0.4639 | 20350 | 0.0007 | - |
| 0.4651 | 20400 | 0.0239 | - |
| 0.4662 | 20450 | 0.0009 | - |
| 0.4674 | 20500 | 0.0005 | - |
| 0.4685 | 20550 | 0.0008 | - |
| 0.4696 | 20600 | 0.0005 | - |
| 0.4708 | 20650 | 0.0006 | - |
| 0.4719 | 20700 | 0.0004 | - |
| 0.4731 | 20750 | 0.0005 | - |
| 0.4742 | 20800 | 0.0049 | - |
| 0.4753 | 20850 | 0.0007 | - |
| 0.4765 | 20900 | 0.0005 | - |
| 0.4776 | 20950 | 0.0018 | - |
| 0.4788 | 21000 | 0.0006 | - |
| 0.4799 | 21050 | 0.0008 | - |
| 0.4810 | 21100 | 0.0008 | - |
| 0.4822 | 21150 | 0.0225 | - |
| 0.4833 | 21200 | 0.0011 | - |
| 0.4845 | 21250 | 0.0005 | - |
| 0.4856 | 21300 | 0.0006 | - |
| 0.4867 | 21350 | 0.0004 | - |
| 0.4879 | 21400 | 0.0008 | - |
| 0.4890 | 21450 | 0.001 | - |
| 0.4902 | 21500 | 0.0004 | - |
| 0.4913 | 21550 | 0.0008 | - |
| 0.4924 | 21600 | 0.0008 | - |
| 0.4936 | 21650 | 0.0006 | - |
| 0.4947 | 21700 | 0.0008 | - |
| 0.4959 | 21750 | 0.0004 | - |
| 0.4970 | 21800 | 0.011 | - |
| 0.4981 | 21850 | 0.0007 | - |
| 0.4993 | 21900 | 0.0004 | - |
| 0.5004 | 21950 | 0.031 | - |
| 0.5016 | 22000 | 0.0102 | - |
| 0.5027 | 22050 | 0.0009 | - |
| 0.5038 | 22100 | 0.0089 | - |
| 0.5050 | 22150 | 0.0007 | - |
| 0.5061 | 22200 | 0.0006 | - |
| 0.5073 | 22250 | 0.0004 | - |
| 0.5084 | 22300 | 0.0004 | - |
| 0.5095 | 22350 | 0.0007 | - |
| 0.5107 | 22400 | 0.0004 | - |
| 0.5118 | 22450 | 0.0004 | - |
| 0.5130 | 22500 | 0.006 | - |
| 0.5141 | 22550 | 0.0008 | - |
| 0.5152 | 22600 | 0.0007 | - |
| 0.5164 | 22650 | 0.0007 | - |
| 0.5175 | 22700 | 0.0007 | - |
| 0.5187 | 22750 | 0.0003 | - |
| 0.5198 | 22800 | 0.0005 | - |
| 0.5209 | 22850 | 0.0006 | - |
| 0.5221 | 22900 | 0.0005 | - |
| 0.5232 | 22950 | 0.0324 | - |
| 0.5244 | 23000 | 0.017 | - |
| 0.5255 | 23050 | 0.0126 | - |
| 0.5266 | 23100 | 0.0005 | - |
| 0.5278 | 23150 | 0.0151 | - |
| 0.5289 | 23200 | 0.0005 | - |
| 0.5301 | 23250 | 0.0003 | - |
| 0.5312 | 23300 | 0.0004 | - |
| 0.5323 | 23350 | 0.011 | - |
| 0.5335 | 23400 | 0.0003 | - |
| 0.5346 | 23450 | 0.0006 | - |
| 0.5358 | 23500 | 0.0006 | - |
| 0.5369 | 23550 | 0.0007 | - |
| 0.5380 | 23600 | 0.0031 | - |
| 0.5392 | 23650 | 0.0005 | - |
| 0.5403 | 23700 | 0.0003 | - |
| 0.5415 | 23750 | 0.0003 | - |
| 0.5426 | 23800 | 0.0341 | - |
| 0.5437 | 23850 | 0.0004 | - |
| 0.5449 | 23900 | 0.0008 | - |
| 0.5460 | 23950 | 0.0008 | - |
| 0.5472 | 24000 | 0.0005 | - |
| 0.5483 | 24050 | 0.0003 | - |
| 0.5494 | 24100 | 0.0006 | - |
| 0.5506 | 24150 | 0.0007 | - |
| 0.5517 | 24200 | 0.001 | - |
| 0.5529 | 24250 | 0.0004 | - |
| 0.5540 | 24300 | 0.0004 | - |
| 0.5551 | 24350 | 0.0005 | - |
| 0.5563 | 24400 | 0.0004 | - |
| 0.5574 | 24450 | 0.0003 | - |
| 0.5586 | 24500 | 0.0007 | - |
| 0.5597 | 24550 | 0.0004 | - |
| 0.5608 | 24600 | 0.0005 | - |
| 0.5620 | 24650 | 0.0004 | - |
| 0.5631 | 24700 | 0.0004 | - |
| 0.5643 | 24750 | 0.0005 | - |
| 0.5654 | 24800 | 0.0008 | - |
| 0.5665 | 24850 | 0.0006 | - |
| 0.5677 | 24900 | 0.0006 | - |
| 0.5688 | 24950 | 0.0003 | - |
| 0.5700 | 25000 | 0.0005 | - |
| 0.5711 | 25050 | 0.0007 | - |
| 0.5722 | 25100 | 0.0004 | - |
| 0.5734 | 25150 | 0.0004 | - |
| 0.5745 | 25200 | 0.0303 | - |
| 0.5757 | 25250 | 0.0223 | - |
| 0.5768 | 25300 | 0.0008 | - |
| 0.5779 | 25350 | 0.0254 | - |
| 0.5791 | 25400 | 0.0006 | - |
| 0.5802 | 25450 | 0.0004 | - |
| 0.5814 | 25500 | 0.0003 | - |
| 0.5825 | 25550 | 0.0007 | - |
| 0.5836 | 25600 | 0.0425 | - |
| 0.5848 | 25650 | 0.0354 | - |
| 0.5859 | 25700 | 0.0006 | - |
| 0.5871 | 25750 | 0.0146 | - |
| 0.5882 | 25800 | 0.0006 | - |
| 0.5893 | 25850 | 0.0005 | - |
| 0.5905 | 25900 | 0.0004 | - |
| 0.5916 | 25950 | 0.0004 | - |
| 0.5928 | 26000 | 0.0007 | - |
| 0.5939 | 26050 | 0.0008 | - |
| 0.5950 | 26100 | 0.001 | - |
| 0.5962 | 26150 | 0.0003 | - |
| 0.5973 | 26200 | 0.0006 | - |
| 0.5985 | 26250 | 0.0003 | - |
| 0.5996 | 26300 | 0.0006 | - |
| 0.6007 | 26350 | 0.0007 | - |
| 0.6019 | 26400 | 0.0007 | - |
| 0.6030 | 26450 | 0.0287 | - |
| 0.6042 | 26500 | 0.0003 | - |
| 0.6053 | 26550 | 0.0323 | - |
| 0.6064 | 26600 | 0.0007 | - |
| 0.6076 | 26650 | 0.0002 | - |
| 0.6087 | 26700 | 0.0004 | - |
| 0.6099 | 26750 | 0.0003 | - |
| 0.6110 | 26800 | 0.0041 | - |
| 0.6121 | 26850 | 0.0005 | - |
| 0.6133 | 26900 | 0.0003 | - |
| 0.6144 | 26950 | 0.0003 | - |
| 0.6156 | 27000 | 0.0003 | - |
| 0.6167 | 27050 | 0.0007 | - |
| 0.6178 | 27100 | 0.0003 | - |
| 0.6190 | 27150 | 0.0003 | - |
| 0.6201 | 27200 | 0.0008 | - |
| 0.6213 | 27250 | 0.0004 | - |
| 0.6224 | 27300 | 0.0003 | - |
| 0.6235 | 27350 | 0.0003 | - |
| 0.6247 | 27400 | 0.0007 | - |
| 0.6258 | 27450 | 0.0006 | - |
| 0.6270 | 27500 | 0.0008 | - |
| 0.6281 | 27550 | 0.0004 | - |
| 0.6292 | 27600 | 0.0004 | - |
| 0.6304 | 27650 | 0.0006 | - |
| 0.6315 | 27700 | 0.0004 | - |
| 0.6327 | 27750 | 0.0003 | - |
| 0.6338 | 27800 | 0.0008 | - |
| 0.6349 | 27850 | 0.0005 | - |
| 0.6361 | 27900 | 0.0121 | - |
| 0.6372 | 27950 | 0.0006 | - |
| 0.6384 | 28000 | 0.0004 | - |
| 0.6395 | 28050 | 0.001 | - |
| 0.6406 | 28100 | 0.0002 | - |
| 0.6418 | 28150 | 0.0006 | - |
| 0.6429 | 28200 | 0.0004 | - |
| 0.6441 | 28250 | 0.0005 | - |
| 0.6452 | 28300 | 0.0004 | - |
| 0.6463 | 28350 | 0.0006 | - |
| 0.6475 | 28400 | 0.001 | - |
| 0.6486 | 28450 | 0.0004 | - |
| 0.6498 | 28500 | 0.0337 | - |
| 0.6509 | 28550 | 0.0009 | - |
| 0.6520 | 28600 | 0.0003 | - |
| 0.6532 | 28650 | 0.0003 | - |
| 0.6543 | 28700 | 0.0005 | - |
| 0.6554 | 28750 | 0.0003 | - |
| 0.6566 | 28800 | 0.0008 | - |
| 0.6577 | 28850 | 0.0002 | - |
| 0.6589 | 28900 | 0.0038 | - |
| 0.6600 | 28950 | 0.0007 | - |
| 0.6611 | 29000 | 0.0003 | - |
| 0.6623 | 29050 | 0.0003 | - |
| 0.6634 | 29100 | 0.0003 | - |
| 0.6646 | 29150 | 0.0003 | - |
| 0.6657 | 29200 | 0.0422 | - |
| 0.6668 | 29250 | 0.0004 | - |
| 0.6680 | 29300 | 0.0002 | - |
| 0.6691 | 29350 | 0.0006 | - |
| 0.6703 | 29400 | 0.0006 | - |
| 0.6714 | 29450 | 0.0004 | - |
| 0.6725 | 29500 | 0.0004 | - |
| 0.6737 | 29550 | 0.0003 | - |
| 0.6748 | 29600 | 0.0004 | - |
| 0.6760 | 29650 | 0.0003 | - |
| 0.6771 | 29700 | 0.0008 | - |
| 0.6782 | 29750 | 0.0003 | - |
| 0.6794 | 29800 | 0.0005 | - |
| 0.6805 | 29850 | 0.0007 | - |
| 0.6817 | 29900 | 0.0004 | - |
| 0.6828 | 29950 | 0.0003 | - |
| 0.6839 | 30000 | 0.0002 | - |
| 0.6851 | 30050 | 0.0004 | - |
| 0.6862 | 30100 | 0.0005 | - |
| 0.6874 | 30150 | 0.0007 | - |
| 0.6885 | 30200 | 0.0005 | - |
| 0.6896 | 30250 | 0.0002 | - |
| 0.6908 | 30300 | 0.0004 | - |
| 0.6919 | 30350 | 0.0007 | - |
| 0.6931 | 30400 | 0.0012 | - |
| 0.6942 | 30450 | 0.0006 | - |
| 0.6953 | 30500 | 0.0006 | - |
| 0.6965 | 30550 | 0.0004 | - |
| 0.6976 | 30600 | 0.0004 | - |
| 0.6988 | 30650 | 0.0003 | - |
| 0.6999 | 30700 | 0.0005 | - |
| 0.7010 | 30750 | 0.0007 | - |
| 0.7022 | 30800 | 0.0003 | - |
| 0.7033 | 30850 | 0.0005 | - |
| 0.7045 | 30900 | 0.0003 | - |
| 0.7056 | 30950 | 0.0002 | - |
| 0.7067 | 31000 | 0.0002 | - |
| 0.7079 | 31050 | 0.0005 | - |
| 0.7090 | 31100 | 0.0003 | - |
| 0.7102 | 31150 | 0.0002 | - |
| 0.7113 | 31200 | 0.0006 | - |
| 0.7124 | 31250 | 0.0004 | - |
| 0.7136 | 31300 | 0.0003 | - |
| 0.7147 | 31350 | 0.0003 | - |
| 0.7159 | 31400 | 0.0002 | - |
| 0.7170 | 31450 | 0.0003 | - |
| 0.7181 | 31500 | 0.0002 | - |
| 0.7193 | 31550 | 0.0004 | - |
| 0.7204 | 31600 | 0.0006 | - |
| 0.7216 | 31650 | 0.0007 | - |
| 0.7227 | 31700 | 0.0004 | - |
| 0.7238 | 31750 | 0.0003 | - |
| 0.7250 | 31800 | 0.0002 | - |
| 0.7261 | 31850 | 0.0004 | - |
| 0.7273 | 31900 | 0.0006 | - |
| 0.7284 | 31950 | 0.0004 | - |
| 0.7295 | 32000 | 0.0005 | - |
| 0.7307 | 32050 | 0.0011 | - |
| 0.7318 | 32100 | 0.0003 | - |
| 0.7330 | 32150 | 0.0004 | - |
| 0.7341 | 32200 | 0.0551 | - |
| 0.7352 | 32250 | 0.0006 | - |
| 0.7364 | 32300 | 0.0004 | - |
| 0.7375 | 32350 | 0.0005 | - |
| 0.7387 | 32400 | 0.0004 | - |
| 0.7398 | 32450 | 0.0007 | - |
| 0.7409 | 32500 | 0.0003 | - |
| 0.7421 | 32550 | 0.0007 | - |
| 0.7432 | 32600 | 0.0003 | - |
| 0.7444 | 32650 | 0.0007 | - |
| 0.7455 | 32700 | 0.0006 | - |
| 0.7466 | 32750 | 0.0006 | - |
| 0.7478 | 32800 | 0.0003 | - |
| 0.7489 | 32850 | 0.0005 | - |
| 0.7501 | 32900 | 0.0004 | - |
| 0.7512 | 32950 | 0.0007 | - |
| 0.7523 | 33000 | 0.0002 | - |
| 0.7535 | 33050 | 0.0008 | - |
| 0.7546 | 33100 | 0.0004 | - |
| 0.7558 | 33150 | 0.0002 | - |
| 0.7569 | 33200 | 0.0006 | - |
| 0.7580 | 33250 | 0.0046 | - |
| 0.7592 | 33300 | 0.0005 | - |
| 0.7603 | 33350 | 0.0003 | - |
| 0.7615 | 33400 | 0.0125 | - |
| 0.7626 | 33450 | 0.0006 | - |
| 0.7637 | 33500 | 0.0063 | - |
| 0.7649 | 33550 | 0.0008 | - |
| 0.7660 | 33600 | 0.0004 | - |
| 0.7672 | 33650 | 0.0037 | - |
| 0.7683 | 33700 | 0.0005 | - |
| 0.7694 | 33750 | 0.0006 | - |
| 0.7706 | 33800 | 0.0006 | - |
| 0.7717 | 33850 | 0.012 | - |
| 0.7729 | 33900 | 0.0005 | - |
| 0.7740 | 33950 | 0.0005 | - |
| 0.7751 | 34000 | 0.0005 | - |
| 0.7763 | 34050 | 0.0003 | - |
| 0.7774 | 34100 | 0.0004 | - |
| 0.7786 | 34150 | 0.0003 | - |
| 0.7797 | 34200 | 0.0003 | - |
| 0.7808 | 34250 | 0.0088 | - |
| 0.7820 | 34300 | 0.0004 | - |
| 0.7831 | 34350 | 0.0002 | - |
| 0.7843 | 34400 | 0.0004 | - |
| 0.7854 | 34450 | 0.0082 | - |
| 0.7865 | 34500 | 0.0005 | - |
| 0.7877 | 34550 | 0.0005 | - |
| 0.7888 | 34600 | 0.0004 | - |
| 0.7900 | 34650 | 0.0003 | - |
| 0.7911 | 34700 | 0.0006 | - |
| 0.7922 | 34750 | 0.0006 | - |
| 0.7934 | 34800 | 0.0002 | - |
| 0.7945 | 34850 | 0.0003 | - |
| 0.7957 | 34900 | 0.0005 | - |
| 0.7968 | 34950 | 0.0003 | - |
| 0.7979 | 35000 | 0.0004 | - |
| 0.7991 | 35050 | 0.0003 | - |
| 0.8002 | 35100 | 0.0002 | - |
| 0.8014 | 35150 | 0.0094 | - |
| 0.8025 | 35200 | 0.0004 | - |
| 0.8036 | 35250 | 0.0004 | - |
| 0.8048 | 35300 | 0.0245 | - |
| 0.8059 | 35350 | 0.0006 | - |
| 0.8071 | 35400 | 0.0004 | - |
| 0.8082 | 35450 | 0.0004 | - |
| 0.8093 | 35500 | 0.0003 | - |
| 0.8105 | 35550 | 0.0007 | - |
| 0.8116 | 35600 | 0.0266 | - |
| 0.8128 | 35650 | 0.0005 | - |
| 0.8139 | 35700 | 0.0003 | - |
| 0.8150 | 35750 | 0.0092 | - |
| 0.8162 | 35800 | 0.0004 | - |
| 0.8173 | 35850 | 0.0002 | - |
| 0.8185 | 35900 | 0.0004 | - |
| 0.8196 | 35950 | 0.0003 | - |
| 0.8207 | 36000 | 0.0002 | - |
| 0.8219 | 36050 | 0.0003 | - |
| 0.8230 | 36100 | 0.0002 | - |
| 0.8242 | 36150 | 0.0006 | - |
| 0.8253 | 36200 | 0.0003 | - |
| 0.8264 | 36250 | 0.0002 | - |
| 0.8276 | 36300 | 0.0002 | - |
| 0.8287 | 36350 | 0.0002 | - |
| 0.8299 | 36400 | 0.0002 | - |
| 0.8310 | 36450 | 0.0004 | - |
| 0.8321 | 36500 | 0.001 | - |
| 0.8333 | 36550 | 0.0134 | - |
| 0.8344 | 36600 | 0.0007 | - |
| 0.8356 | 36650 | 0.0005 | - |
| 0.8367 | 36700 | 0.0004 | - |
| 0.8378 | 36750 | 0.0003 | - |
| 0.8390 | 36800 | 0.007 | - |
| 0.8401 | 36850 | 0.0002 | - |
| 0.8413 | 36900 | 0.0005 | - |
| 0.8424 | 36950 | 0.0002 | - |
| 0.8435 | 37000 | 0.0002 | - |
| 0.8447 | 37050 | 0.0003 | - |
| 0.8458 | 37100 | 0.0002 | - |
| 0.8470 | 37150 | 0.003 | - |
| 0.8481 | 37200 | 0.0003 | - |
| 0.8492 | 37250 | 0.0002 | - |
| 0.8504 | 37300 | 0.0011 | - |
| 0.8515 | 37350 | 0.0015 | - |
| 0.8527 | 37400 | 0.0002 | - |
| 0.8538 | 37450 | 0.0004 | - |
| 0.8549 | 37500 | 0.0005 | - |
| 0.8561 | 37550 | 0.0004 | - |
| 0.8572 | 37600 | 0.0085 | - |
| 0.8584 | 37650 | 0.0002 | - |
| 0.8595 | 37700 | 0.0003 | - |
| 0.8606 | 37750 | 0.0002 | - |
| 0.8618 | 37800 | 0.0002 | - |
| 0.8629 | 37850 | 0.0042 | - |
| 0.8641 | 37900 | 0.0006 | - |
| 0.8652 | 37950 | 0.0133 | - |
| 0.8663 | 38000 | 0.0003 | - |
| 0.8675 | 38050 | 0.0003 | - |
| 0.8686 | 38100 | 0.0003 | - |
| 0.8698 | 38150 | 0.0002 | - |
| 0.8709 | 38200 | 0.0359 | - |
| 0.8720 | 38250 | 0.001 | - |
| 0.8732 | 38300 | 0.0004 | - |
| 0.8743 | 38350 | 0.0002 | - |
| 0.8755 | 38400 | 0.0004 | - |
| 0.8766 | 38450 | 0.0005 | - |
| 0.8777 | 38500 | 0.0005 | - |
| 0.8789 | 38550 | 0.0003 | - |
| 0.8800 | 38600 | 0.0078 | - |
| 0.8812 | 38650 | 0.0002 | - |
| 0.8823 | 38700 | 0.0006 | - |
| 0.8834 | 38750 | 0.0002 | - |
| 0.8846 | 38800 | 0.0005 | - |
| 0.8857 | 38850 | 0.0355 | - |
| 0.8869 | 38900 | 0.0006 | - |
| 0.8880 | 38950 | 0.0003 | - |
| 0.8891 | 39000 | 0.0003 | - |
| 0.8903 | 39050 | 0.0002 | - |
| 0.8914 | 39100 | 0.0004 | - |
| 0.8926 | 39150 | 0.0002 | - |
| 0.8937 | 39200 | 0.0011 | - |
| 0.8948 | 39250 | 0.0003 | - |
| 0.8960 | 39300 | 0.0305 | - |
| 0.8971 | 39350 | 0.0002 | - |
| 0.8983 | 39400 | 0.0069 | - |
| 0.8994 | 39450 | 0.0002 | - |
| 0.9005 | 39500 | 0.0004 | - |
| 0.9017 | 39550 | 0.0003 | - |
| 0.9028 | 39600 | 0.0002 | - |
| 0.9040 | 39650 | 0.0002 | - |
| 0.9051 | 39700 | 0.0007 | - |
| 0.9062 | 39750 | 0.0002 | - |
| 0.9074 | 39800 | 0.0004 | - |
| 0.9085 | 39850 | 0.0008 | - |
| 0.9097 | 39900 | 0.0002 | - |
| 0.9108 | 39950 | 0.0004 | - |
| 0.9119 | 40000 | 0.0156 | - |
| 0.9131 | 40050 | 0.0007 | - |
| 0.9142 | 40100 | 0.0003 | - |
| 0.9154 | 40150 | 0.0006 | - |
| 0.9165 | 40200 | 0.0074 | - |
| 0.9176 | 40250 | 0.0075 | - |
| 0.9188 | 40300 | 0.0002 | - |
| 0.9199 | 40350 | 0.0006 | - |
| 0.9210 | 40400 | 0.0004 | - |
| 0.9222 | 40450 | 0.0004 | - |
| 0.9233 | 40500 | 0.0002 | - |
| 0.9245 | 40550 | 0.0008 | - |
| 0.9256 | 40600 | 0.0002 | - |
| 0.9267 | 40650 | 0.0003 | - |
| 0.9279 | 40700 | 0.0005 | - |
| 0.9290 | 40750 | 0.0104 | - |
| 0.9302 | 40800 | 0.0002 | - |
| 0.9313 | 40850 | 0.0003 | - |
| 0.9324 | 40900 | 0.0005 | - |
| 0.9336 | 40950 | 0.0003 | - |
| 0.9347 | 41000 | 0.0002 | - |
| 0.9359 | 41050 | 0.0002 | - |
| 0.9370 | 41100 | 0.0004 | - |
| 0.9381 | 41150 | 0.0285 | - |
| 0.9393 | 41200 | 0.0067 | - |
| 0.9404 | 41250 | 0.0004 | - |
| 0.9416 | 41300 | 0.0004 | - |
| 0.9427 | 41350 | 0.0002 | - |
| 0.9438 | 41400 | 0.0006 | - |
| 0.9450 | 41450 | 0.0003 | - |
| 0.9461 | 41500 | 0.0008 | - |
| 0.9473 | 41550 | 0.0004 | - |
| 0.9484 | 41600 | 0.0003 | - |
| 0.9495 | 41650 | 0.0005 | - |
| 0.9507 | 41700 | 0.0005 | - |
| 0.9518 | 41750 | 0.0002 | - |
| 0.9530 | 41800 | 0.0004 | - |
| 0.9541 | 41850 | 0.0003 | - |
| 0.9552 | 41900 | 0.0006 | - |
| 0.9564 | 41950 | 0.0006 | - |
| 0.9575 | 42000 | 0.0002 | - |
| 0.9587 | 42050 | 0.0002 | - |
| 0.9598 | 42100 | 0.0002 | - |
| 0.9609 | 42150 | 0.0068 | - |
| 0.9621 | 42200 | 0.007 | - |
| 0.9632 | 42250 | 0.0265 | - |
| 0.9644 | 42300 | 0.0004 | - |
| 0.9655 | 42350 | 0.0002 | - |
| 0.9666 | 42400 | 0.0005 | - |
| 0.9678 | 42450 | 0.0004 | - |
| 0.9689 | 42500 | 0.0063 | - |
| 0.9701 | 42550 | 0.0004 | - |
| 0.9712 | 42600 | 0.0002 | - |
| 0.9723 | 42650 | 0.0002 | - |
| 0.9735 | 42700 | 0.0003 | - |
| 0.9746 | 42750 | 0.0007 | - |
| 0.9758 | 42800 | 0.0004 | - |
| 0.9769 | 42850 | 0.0082 | - |
| 0.9780 | 42900 | 0.0004 | - |
| 0.9792 | 42950 | 0.031 | - |
| 0.9803 | 43000 | 0.0004 | - |
| 0.9815 | 43050 | 0.0047 | - |
| 0.9826 | 43100 | 0.0003 | - |
| 0.9837 | 43150 | 0.0003 | - |
| 0.9849 | 43200 | 0.0005 | - |
| 0.9860 | 43250 | 0.0003 | - |
| 0.9872 | 43300 | 0.0002 | - |
| 0.9883 | 43350 | 0.0005 | - |
| 0.9894 | 43400 | 0.0003 | - |
| 0.9906 | 43450 | 0.0007 | - |
| 0.9917 | 43500 | 0.0003 | - |
| 0.9929 | 43550 | 0.0003 | - |
| 0.9940 | 43600 | 0.0006 | - |
| 0.9951 | 43650 | 0.001 | - |
| 0.9963 | 43700 | 0.0006 | - |
| 0.9974 | 43750 | 0.0002 | - |
| 0.9986 | 43800 | 0.0003 | - |
| 0.9997 | 43850 | 0.0005 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.2
- Transformers: 4.39.3
- PyTorch: 2.1.2
- 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}
}
```