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blueCarbon

Text Classification
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mpnet
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: physiological metabolisms of seaweeds usually suffered climate changes in
the field. gracilariopsis lemaneiformis and ulva lactuca, collected from nan ao
island, shantou, china, were cultured under ambient and elevated co2 supply ,
with low and high temperatures for weeks, aiming to compare the difference of
the main physiological metabolism between two seaweed species in response to the
elevated co2 and high temperature. at 15 , the ph reduction in the culture medium
caused by elevated co2 was larger in . lemaneiformis than in . lactuca. at 25
, elevated co2 significantly increased photosynthetic rates and maintained constant
respiratory rates in . lemaneiformis. however, for 25 grown . lactuca, the increment
of co2 did not enhance the pn rates but rapidly decreased the rd rates itself.
with the higher rd pg ratios in . lemaneiformis than . lactuca, the warming thereby
promoted more allocation of photosynthetic products to respiratory consumption
in . lemaneiformis. both pg and rd rates exhibited lower temperature acclimation
in two seaweeds. in addition, elevated co2 markedly increased the relative growth
rate and phycobiliprotein contents at 25 , but exhibited no enhancement of chlorophyll
, carotenoids , soluble carbohydrate , and soluble protein contents in . lemaneiformis,
with the reduction of sc when temperature increased only. we suggested that climate
changes were probably more benefit to . lactuca than to . lemaneiformis, inherently
justifying the metabolism during . lemaneiformis maricultivation. 2018, springer
verlag gmbh germany, part of springer nature.
- text: blue carbon is vital aspect of climate change mitigation, which necessitates
the identification of stocks and drivers for implementing mitigation strategies.
however, reclamation may be among the most invasive forms, and the question of
its influence has not been addressed well in blue carbon research. therefore,
the effects of reclamation on carbon stocks and the interaction of crucial drivers
from reclamation time areas were evaluated in the liaohe river delta and compared
with natural reserves . carbon stocks based on invest model were lower than preexisting
conditions . one way analysis of variance showed that average carbon stocks accumulated
55 years after reclamation and reached the lowest value in 85 years. the interaction
analysis of dominant drivers affecting carbon showed the difference between reclaimed
areas and reserves regarding potential effect pathways. in the 1930s and 1960s
reclamation time areas, crop yield and industrial output determined blue carbon
by changing no3 and ap. in the 1990s reclamation time area, population density
played an important role. in defining the impact of vegetation cover on carbon
within the reserves, the distance to the coast and residence were significant
factors. this study demonstrated that coastal
- text: multiple techniques, including thermal infrared aerial remote sensing, geophysical
and geological data, geochemical characterization and radium isotopes, were used
to evaluate the role of groundwater as source of dissolved nutrients, carbon,
and trace gases to the okatee river estuary, south carolina. thermal infrared
aerial remote sensing surveys illustrated the presence of multiple submarine groundwater
discharge sites in okatee headwaters. significant relationships were observed
between groundwater geochemical constituents and ra 226 activity in groundwater
with higher ra 226 activity correlated to higher concentrations of organics, dissolved
inorganic carbon, nutrients, and trace gases to the okatee system. system level
radium mass balance confirmed substantial submarine groundwater discharge contribution
of these constituents to the okatee river. diffusive benthic flux measurements
and potential denitrification rate assays tracked the fate of constituents in
creek bank sediments. diffusive benthic fluxes were substantially lower than calculated
radium based submarine groundwater discharge inputs, showing that advection of
groundwater derived nutrients dominated fluxes in the system. while considerable
potential for denitrification in tidal creek bank sediments was noted, in situ
denitrification rates were nitrate limited, making intertidal sediments an inefficient
nitrogen sink in this system. groundwater geochemical data indicated significant
differences in groundwater chemical composition and radium activity ratios between
the eastern and western sides of the river; these likely arose from the distinct
hydrological regimes observed in each area. groundwater from the western side
of the okatee headwaters was characterized by higher concentrations of dissolved
organic and inorganic carbon, dissolved organic nitrogen, inorganic nutrients
and reduced metabolites and trace gases, .. methane and nitrous oxide, than groundwater
from the eastern side. differences in microbial sulfate reduction, organic matter
supply, and or groundwater residence time likely contributed to this pattern.
the contrasting features of the east and west sub marsh zones highlight the need
for multiple techniques for characterization of submarine groundwater discharge
sources and the impact of biogeochemical processes on the delivery of nutrients
and carbon to coastal areas via submarine groundwater discharge. 2014 elsevier
ltd. all rights reserved.
- text: blue carbon ecosystem initiatives in the coral triangle region are increasing
due to their amplified recognition in mitigating global climate change. although
transdisciplinary approaches in the blue carbon discourse and collaborative actions
are gaining momentum in the international and national arenas, more work is still
needed at the local level. the study pursues how bce initiatives permeate through
the local communities in the philippines and indonesia, as part of ctr. using
perception surveys, the coastal residents from busuanga, philippines, and karimunjawa,
indonesia were interviewed on their awareness, utilization, perceived threats,
and management strategies for bces. potential factors affecting residents perceptions
were explored using multivariate regression and correlation analyses. also, comparative
analysis was done to determine distinctions and commonalities in perceptions as
influenced by site specific scenarios. results show that, despite respondents
presenting relatively high awareness of bce services, levels of utilization are
low with 42. 92. and 23. 85. respondents in busuanga and karimunjawa, respectively,
not directly utilizing bce resources. regression analysis showed that respondents
occupation significantly influenced their utilization rate and observed opposite
correlations in busuanga and karimunjawa . perceived threats are found to be driven
by personal experiences occurrence of natural disasters in busuanga whereas discerned
anthropogenic activities in karimunjawa. meanwhile, recognized management strategies
are influenced by the strong presence of relevant agencies like non government
and people organizations in busuanga and the local government in karimunjawa.
these results can be translated as useful metrics in contextualizing and or enhancing
bce management plans specifically in strategizing advocacy campaigns and engagement
of local stakeholders across the ctr.
- text: mangrove wetlands are important ecosystems, yet human development coupled
with climate change threatens mangroves and their large carbon stores. this study
seeks to understand the soil carbon dynamics in hydrologically altered mangrove
swamps by studying aboveground biomass estimates and belowground soil carbon concentrations
in mangrove swamps with high, medium, and low levels of disturbance in catano,
jobos bay, and vieques, puerto rico. all three sites were affected by hurricane
maria in 2017, one year prior to the study. as result of being hit by the saffir
simpson category hurricane, the low disturbance site had almost no living mangroves
left during sampling. there was no correlation between level of hydrologic alteration
and carbon storage, rather different patterns emerged for each of the three sites.
at the highly disturbed location, belowground carbon mass averaged .048 .001 cm
which increased with increased aboveground biomass. at the moderately disturbed
location, belowground carbon mass averaged .047 .003 cm and corresponded to distance
from open water. at the low disturbed location, organic carbon was consistent
between all sites and inorganic carbon concentrations controlled total carbon
mass which averaged .048 .002 cm. these results suggest that mangroves are adaptive
and resilient and have the potential to retain their carbon storage capacities
despite hydrologic alterations, but mass carbon storage within mangrove forests
can be spatially variable in hydrologically altered conditions.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# 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 MultiOutputClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ignaciosg/blueCarbon")
# Run inference
preds = model("blue carbon is vital aspect of climate change mitigation, which necessitates the identification of stocks and drivers for implementing mitigation strategies. however, reclamation may be among the most invasive forms, and the question of its influence has not been addressed well in blue carbon research. therefore, the effects of reclamation on carbon stocks and the interaction of crucial drivers from reclamation time areas were evaluated in the liaohe river delta and compared with natural reserves . carbon stocks based on invest model were lower than preexisting conditions . one way analysis of variance showed that average carbon stocks accumulated 55 years after reclamation and reached the lowest value in 85 years. the interaction analysis of dominant drivers affecting carbon showed the difference between reclaimed areas and reserves regarding potential effect pathways. in the 1930s and 1960s reclamation time areas, crop yield and industrial output determined blue carbon by changing no3 and ap. in the 1990s reclamation time area, population density played an important role. in defining the impact of vegetation cover on carbon within the reserves, the distance to the coast and residence were significant factors. this study demonstrated that coastal")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 105 | 229.475 | 432 |
### Training Hyperparameters
- batch_size: (1, 1)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.0006155918397454662
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- max_length: 1000
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.1819 | - |
| 0.0011 | 50 | 0.201 | - |
| 0.0023 | 100 | 0.3533 | - |
| 0.0034 | 150 | 0.0788 | - |
| 0.0046 | 200 | 0.1445 | - |
| 0.0057 | 250 | 0.1584 | - |
| 0.0069 | 300 | 0.3425 | - |
| 0.0080 | 350 | 0.1203 | - |
| 0.0092 | 400 | 0.2045 | - |
| 0.0103 | 450 | 0.0287 | - |
| 0.0115 | 500 | 0.1784 | - |
| 0.0126 | 550 | 0.2521 | - |
| 0.0138 | 600 | 0.1285 | - |
| 0.0149 | 650 | 0.2292 | - |
| 0.0161 | 700 | 0.0943 | - |
| 0.0172 | 750 | 0.1753 | - |
| 0.0184 | 800 | 0.3433 | - |
| 0.0195 | 850 | 0.262 | - |
| 0.0207 | 900 | 0.1097 | - |
| 0.0218 | 950 | 0.0015 | - |
| 0.0230 | 1000 | 0.5522 | - |
| 0.0241 | 1050 | 0.5939 | - |
| 0.0253 | 1100 | 0.1134 | - |
| 0.0264 | 1150 | 0.1258 | - |
| 0.0276 | 1200 | 0.0146 | - |
| 0.0287 | 1250 | 0.0467 | - |
| 0.0299 | 1300 | 0.3501 | - |
| 0.0310 | 1350 | 0.291 | - |
| 0.0322 | 1400 | 0.0569 | - |
| 0.0333 | 1450 | 0.0812 | - |
| 0.0345 | 1500 | 0.3397 | - |
| 0.0356 | 1550 | 0.1664 | - |
| 0.0368 | 1600 | 0.3841 | - |
| 0.0379 | 1650 | 0.1659 | - |
| 0.0391 | 1700 | 0.0809 | - |
| 0.0402 | 1750 | 0.3604 | - |
| 0.0414 | 1800 | 0.0056 | - |
| 0.0425 | 1850 | 0.3335 | - |
| 0.0437 | 1900 | 0.0005 | - |
| 0.0448 | 1950 | 0.1624 | - |
| 0.0460 | 2000 | 0.8162 | - |
| 0.0471 | 2050 | 0.0097 | - |
| 0.0483 | 2100 | 0.2561 | - |
| 0.0494 | 2150 | 0.0003 | - |
| 0.0506 | 2200 | 0.4198 | - |
| 0.0517 | 2250 | 0.0002 | - |
| 0.0529 | 2300 | 0.2793 | - |
| 0.0540 | 2350 | 0.6288 | - |
| 0.0552 | 2400 | 0.6944 | - |
| 0.0563 | 2450 | 0.7394 | - |
| 0.0575 | 2500 | 0.011 | - |
| 0.0586 | 2550 | 0.8041 | - |
| 0.0598 | 2600 | 0.0041 | - |
| 0.0609 | 2650 | 0.2446 | - |
| 0.0621 | 2700 | 0.2759 | - |
| 0.0632 | 2750 | 0.151 | - |
| 0.0644 | 2800 | 0.0651 | - |
| 0.0655 | 2850 | 0.0026 | - |
| 0.0666 | 2900 | 0.0845 | - |
| 0.0678 | 2950 | 0.7541 | - |
| 0.0689 | 3000 | 0.0993 | - |
| 0.0701 | 3050 | 0.7355 | - |
| 0.0712 | 3100 | 0.6959 | - |
| 0.0724 | 3150 | 0.1687 | - |
| 0.0735 | 3200 | 0.2048 | - |
| 0.0747 | 3250 | 0.0906 | - |
| 0.0758 | 3300 | 0.0582 | - |
| 0.0770 | 3350 | 0.9064 | - |
| 0.0781 | 3400 | 0.8038 | - |
| 0.0793 | 3450 | 0.2515 | - |
| 0.0804 | 3500 | 0.0196 | - |
| 0.0816 | 3550 | 0.0081 | - |
| 0.0827 | 3600 | 0.8483 | - |
| 0.0839 | 3650 | 0.0651 | - |
| 0.0850 | 3700 | 0.8224 | - |
| 0.0862 | 3750 | 0.2872 | - |
| 0.0873 | 3800 | 0.0506 | - |
| 0.0885 | 3850 | 0.6795 | - |
| 0.0896 | 3900 | 0.0126 | - |
| 0.0908 | 3950 | 0.5083 | - |
| 0.0919 | 4000 | 0.0215 | - |
| 0.0931 | 4050 | 0.8133 | - |
| 0.0942 | 4100 | 0.1534 | - |
| 0.0954 | 4150 | 0.2397 | - |
| 0.0965 | 4200 | 0.8576 | - |
| 0.0977 | 4250 | 0.0554 | - |
| 0.0988 | 4300 | 0.1018 | - |
| 0.1000 | 4350 | 0.3324 | - |
| 0.1011 | 4400 | 0.0221 | - |
| 0.1023 | 4450 | 0.0516 | - |
| 0.1034 | 4500 | 0.796 | - |
| 0.1046 | 4550 | 0.0903 | - |
| 0.1057 | 4600 | 0.1979 | - |
| 0.1069 | 4650 | 0.9194 | - |
| 0.1080 | 4700 | 0.2556 | - |
| 0.1092 | 4750 | 0.7224 | - |
| 0.1103 | 4800 | 0.0012 | - |
| 0.1115 | 4850 | 0.5042 | - |
| 0.1126 | 4900 | 0.5732 | - |
| 0.1138 | 4950 | 0.1041 | - |
| 0.1149 | 5000 | 0.0247 | - |
| 0.1161 | 5050 | 0.0265 | - |
| 0.1172 | 5100 | 0.0126 | - |
| 0.1184 | 5150 | 0.0098 | - |
| 0.1195 | 5200 | 0.0386 | - |
| 0.1207 | 5250 | 0.001 | - |
| 0.1218 | 5300 | 0.9248 | - |
| 0.1230 | 5350 | 0.4783 | - |
| 0.1241 | 5400 | 0.1841 | - |
| 0.1253 | 5450 | 0.4721 | - |
| 0.1264 | 5500 | 0.0601 | - |
| 0.1276 | 5550 | 0.0073 | - |
| 0.1287 | 5600 | 0.0028 | - |
| 0.1298 | 5650 | 0.012 | - |
| 0.1310 | 5700 | 0.0451 | - |
| 0.1321 | 5750 | 0.0125 | - |
| 0.1333 | 5800 | 0.5423 | - |
| 0.1344 | 5850 | 0.7545 | - |
| 0.1356 | 5900 | 0.0158 | - |
| 0.1367 | 5950 | 0.1388 | - |
| 0.1379 | 6000 | 0.0136 | - |
| 0.1390 | 6050 | 0.0043 | - |
| 0.1402 | 6100 | 0.4147 | - |
| 0.1413 | 6150 | 0.0503 | - |
| 0.1425 | 6200 | 0.0347 | - |
| 0.1436 | 6250 | 0.0465 | - |
| 0.1448 | 6300 | 0.0086 | - |
| 0.1459 | 6350 | 0.8752 | - |
| 0.1471 | 6400 | 0.5546 | - |
| 0.1482 | 6450 | 0.0348 | - |
| 0.1494 | 6500 | 0.0853 | - |
| 0.1505 | 6550 | 0.6107 | - |
| 0.1517 | 6600 | 0.005 | - |
| 0.1528 | 6650 | 0.3526 | - |
| 0.1540 | 6700 | 0.2429 | - |
| 0.1551 | 6750 | 0.6727 | - |
| 0.1563 | 6800 | 0.0019 | - |
| 0.1574 | 6850 | 0.6662 | - |
| 0.1586 | 6900 | 0.0068 | - |
| 0.1597 | 6950 | 0.0117 | - |
| 0.1609 | 7000 | 0.4718 | - |
| 0.1620 | 7050 | 0.0072 | - |
| 0.1632 | 7100 | 0.8174 | - |
| 0.1643 | 7150 | 0.0094 | - |
| 0.1655 | 7200 | 0.0241 | - |
| 0.1666 | 7250 | 0.1359 | - |
| 0.1678 | 7300 | 0.0528 | - |
| 0.1689 | 7350 | 0.0184 | - |
| 0.1701 | 7400 | 0.2204 | - |
| 0.1712 | 7450 | 0.3476 | - |
| 0.1724 | 7500 | 0.1153 | - |
| 0.1735 | 7550 | 0.0717 | - |
| 0.1747 | 7600 | 0.022 | - |
| 0.1758 | 7650 | 0.0311 | - |
| 0.1770 | 7700 | 0.4385 | - |
| 0.1781 | 7750 | 0.4274 | - |
| 0.1793 | 7800 | 0.4994 | - |
| 0.1804 | 7850 | 0.2518 | - |
| 0.1816 | 7900 | 0.8652 | - |
| 0.1827 | 7950 | 0.0019 | - |
| 0.1839 | 8000 | 0.01 | - |
| 0.1850 | 8050 | 0.0129 | - |
| 0.1862 | 8100 | 0.0001 | - |
| 0.1873 | 8150 | 0.0005 | - |
| 0.1885 | 8200 | 0.0199 | - |
| 0.1896 | 8250 | 0.1489 | - |
| 0.1908 | 8300 | 0.0016 | - |
| 0.1919 | 8350 | 0.5111 | - |
| 0.1931 | 8400 | 0.807 | - |
| 0.1942 | 8450 | 0.1489 | - |
| 0.1953 | 8500 | 0.29 | - |
| 0.1965 | 8550 | 0.0001 | - |
| 0.1976 | 8600 | 0.0043 | - |
| 0.1988 | 8650 | 0.0041 | - |
| 0.1999 | 8700 | 0.3061 | - |
| 0.2011 | 8750 | 0.0221 | - |
| 0.2022 | 8800 | 0.801 | - |
| 0.2034 | 8850 | 0.2316 | - |
| 0.2045 | 8900 | 0.2784 | - |
| 0.2057 | 8950 | 0.0957 | - |
| 0.2068 | 9000 | 0.611 | - |
| 0.2080 | 9050 | 0.7529 | - |
| 0.2091 | 9100 | 0.0565 | - |
| 0.2103 | 9150 | 0.0114 | - |
| 0.2114 | 9200 | 0.2864 | - |
| 0.2126 | 9250 | 0.1954 | - |
| 0.2137 | 9300 | 0.7993 | - |
| 0.2149 | 9350 | 0.0501 | - |
| 0.2160 | 9400 | 0.0051 | - |
| 0.2172 | 9450 | 0.6012 | - |
| 0.2183 | 9500 | 0.0131 | - |
| 0.2195 | 9550 | 0.0157 | - |
| 0.2206 | 9600 | 0.0606 | - |
| 0.2218 | 9650 | 0.9143 | - |
| 0.2229 | 9700 | 0.0001 | - |
| 0.2241 | 9750 | 0.0021 | - |
| 0.2252 | 9800 | 0.0004 | - |
| 0.2264 | 9850 | 0.0498 | - |
| 0.2275 | 9900 | 0.0021 | - |
| 0.2287 | 9950 | 0.8591 | - |
| 0.2298 | 10000 | 0.2218 | - |
| 0.2310 | 10050 | 0.0065 | - |
| 0.2321 | 10100 | 0.0924 | - |
| 0.2333 | 10150 | 0.8866 | - |
| 0.2344 | 10200 | 0.0004 | - |
| 0.2356 | 10250 | 0.1434 | - |
| 0.2367 | 10300 | 0.0118 | - |
| 0.2379 | 10350 | 0.025 | - |
| 0.2390 | 10400 | 0.8472 | - |
| 0.2402 | 10450 | 0.0352 | - |
| 0.2413 | 10500 | 0.0105 | - |
| 0.2425 | 10550 | 0.0025 | - |
| 0.2436 | 10600 | 0.0042 | - |
| 0.2448 | 10650 | 0.3461 | - |
| 0.2459 | 10700 | 0.0314 | - |
| 0.2471 | 10750 | 0.1411 | - |
| 0.2482 | 10800 | 0.0006 | - |
| 0.2494 | 10850 | 0.0013 | - |
| 0.2505 | 10900 | 0.894 | - |
| 0.2517 | 10950 | 0.9961 | - |
| 0.2528 | 11000 | 0.9908 | - |
| 0.2540 | 11050 | 0.836 | - |
| 0.2551 | 11100 | 0.8847 | - |
| 0.2563 | 11150 | 0.8493 | - |
| 0.2574 | 11200 | 0.5851 | - |
| 0.2585 | 11250 | 0.9502 | - |
| 0.2597 | 11300 | 0.8396 | - |
| 0.2608 | 11350 | 0.1942 | - |
| 0.2620 | 11400 | 0.9298 | - |
| 0.2631 | 11450 | 0.742 | - |
| 0.2643 | 11500 | 0.8624 | - |
| 0.2654 | 11550 | 0.5423 | - |
| 0.2666 | 11600 | 0.8576 | - |
| 0.2677 | 11650 | 0.8042 | - |
| 0.2689 | 11700 | 0.7447 | - |
| 0.2700 | 11750 | 0.5319 | - |
| 0.2712 | 11800 | 0.451 | - |
| 0.2723 | 11850 | 0.4115 | - |
| 0.2735 | 11900 | 0.6772 | - |
| 0.2746 | 11950 | 0.4701 | - |
| 0.2758 | 12000 | 0.6101 | - |
| 0.2769 | 12050 | 0.4914 | - |
| 0.2781 | 12100 | 0.653 | - |
| 0.2792 | 12150 | 0.6205 | - |
| 0.2804 | 12200 | 0.651 | - |
| 0.2815 | 12250 | 0.2223 | - |
| 0.2827 | 12300 | 0.7124 | - |
| 0.2838 | 12350 | 0.6502 | - |
| 0.2850 | 12400 | 0.5812 | - |
| 0.2861 | 12450 | 0.6483 | - |
| 0.2873 | 12500 | 0.7335 | - |
| 0.2884 | 12550 | 0.239 | - |
| 0.2896 | 12600 | 0.6499 | - |
| 0.2907 | 12650 | 0.4453 | - |
| 0.2919 | 12700 | 0.7152 | - |
| 0.2930 | 12750 | 0.5551 | - |
| 0.2942 | 12800 | 0.6034 | - |
| 0.2953 | 12850 | 0.5714 | - |
| 0.2965 | 12900 | 0.5867 | - |
| 0.2976 | 12950 | 0.4249 | - |
| 0.2988 | 13000 | 0.7262 | - |
| 0.2999 | 13050 | 0.542 | - |
| 0.3011 | 13100 | 0.5301 | - |
| 0.3022 | 13150 | 0.7503 | - |
| 0.3034 | 13200 | 0.6918 | - |
| 0.3045 | 13250 | 0.5352 | - |
| 0.3057 | 13300 | 0.6065 | - |
| 0.3068 | 13350 | 0.373 | - |
| 0.3080 | 13400 | 0.7648 | - |
| 0.3091 | 13450 | 0.2762 | - |
| 0.3103 | 13500 | 0.708 | - |
| 0.3114 | 13550 | 0.1481 | - |
| 0.3126 | 13600 | 0.7231 | - |
| 0.3137 | 13650 | 0.6023 | - |
| 0.3149 | 13700 | 0.7021 | - |
| 0.3160 | 13750 | 0.5843 | - |
| 0.3172 | 13800 | 0.7361 | - |
| 0.3183 | 13850 | 0.7844 | - |
| 0.3195 | 13900 | 0.51 | - |
| 0.3206 | 13950 | 0.506 | - |
| 0.3218 | 14000 | 0.3072 | - |
| 0.3229 | 14050 | 0.5854 | - |
| 0.3240 | 14100 | 0.3553 | - |
| 0.3252 | 14150 | 0.6827 | - |
| 0.3263 | 14200 | 0.5342 | - |
| 0.3275 | 14250 | 0.6887 | - |
| 0.3286 | 14300 | 0.6007 | - |
| 0.3298 | 14350 | 0.4573 | - |
| 0.3309 | 14400 | 0.5979 | - |
| 0.3321 | 14450 | 0.5328 | - |
| 0.3332 | 14500 | 0.6814 | - |
| 0.3344 | 14550 | 0.6207 | - |
| 0.3355 | 14600 | 0.8189 | - |
| 0.3367 | 14650 | 0.5794 | - |
| 0.3378 | 14700 | 0.3987 | - |
| 0.3390 | 14750 | 0.5281 | - |
| 0.3401 | 14800 | 0.652 | - |
| 0.3413 | 14850 | 0.6811 | - |
| 0.3424 | 14900 | 0.3334 | - |
| 0.3436 | 14950 | 0.565 | - |
| 0.3447 | 15000 | 0.4956 | - |
| 0.3459 | 15050 | 0.7289 | - |
| 0.3470 | 15100 | 0.6103 | - |
| 0.3482 | 15150 | 0.4173 | - |
| 0.3493 | 15200 | 0.2138 | - |
| 0.3505 | 15250 | 0.893 | - |
| 0.3516 | 15300 | 0.5385 | - |
| 0.3528 | 15350 | 0.6386 | - |
| 0.3539 | 15400 | 0.7168 | - |
| 0.3551 | 15450 | 0.1189 | - |
| 0.3562 | 15500 | 0.3046 | - |
| 0.3574 | 15550 | 0.4776 | - |
| 0.3585 | 15600 | 0.7062 | - |
| 0.3597 | 15650 | 0.0972 | - |
| 0.3608 | 15700 | 0.4485 | - |
| 0.3620 | 15750 | 0.5843 | - |
| 0.3631 | 15800 | 0.5656 | - |
| 0.3643 | 15850 | 0.5682 | - |
| 0.3654 | 15900 | 0.416 | - |
| 0.3666 | 15950 | 0.2427 | - |
| 0.3677 | 16000 | 0.4942 | - |
| 0.3689 | 16050 | 0.4734 | - |
| 0.3700 | 16100 | 0.7099 | - |
| 0.3712 | 16150 | 0.5899 | - |
| 0.3723 | 16200 | 0.3502 | - |
| 0.3735 | 16250 | 0.3448 | - |
| 0.3746 | 16300 | 0.6606 | - |
| 0.3758 | 16350 | 0.5239 | - |
| 0.3769 | 16400 | 0.6872 | - |
| 0.3781 | 16450 | 0.2828 | - |
| 0.3792 | 16500 | 0.6973 | - |
| 0.3804 | 16550 | 0.6628 | - |
| 0.3815 | 16600 | 0.6429 | - |
| 0.3827 | 16650 | 0.4321 | - |
| 0.3838 | 16700 | 0.6626 | - |
| 0.3850 | 16750 | 0.5044 | - |
| 0.3861 | 16800 | 0.7683 | - |
| 0.3872 | 16850 | 0.6687 | - |
| 0.3884 | 16900 | 0.5821 | - |
| 0.3895 | 16950 | 0.6572 | - |
| 0.3907 | 17000 | 0.9609 | - |
| 0.3918 | 17050 | 0.0123 | - |
| 0.3930 | 17100 | 0.5649 | - |
| 0.3941 | 17150 | 0.1006 | - |
| 0.3953 | 17200 | 0.003 | - |
| 0.3964 | 17250 | 0.278 | - |
| 0.3976 | 17300 | 0.8632 | - |
| 0.3987 | 17350 | 0.5101 | - |
| 0.3999 | 17400 | 0.8753 | - |
| 0.4010 | 17450 | 0.3195 | - |
| 0.4022 | 17500 | 0.9436 | - |
| 0.4033 | 17550 | 0.9388 | - |
| 0.4045 | 17600 | 0.0097 | - |
| 0.4056 | 17650 | 0.6898 | - |
| 0.4068 | 17700 | 0.035 | - |
| 0.4079 | 17750 | 0.4828 | - |
| 0.4091 | 17800 | 0.1888 | - |
| 0.4102 | 17850 | 0.0354 | - |
| 0.4114 | 17900 | 0.0008 | - |
| 0.4125 | 17950 | 0.2885 | - |
| 0.4137 | 18000 | 0.0624 | - |
| 0.4148 | 18050 | 0.5545 | - |
| 0.4160 | 18100 | 0.5317 | - |
| 0.4171 | 18150 | 0.0207 | - |
| 0.4183 | 18200 | 0.0228 | - |
| 0.4194 | 18250 | 0.0168 | - |
| 0.4206 | 18300 | 0.0935 | - |
| 0.4217 | 18350 | 0.8391 | - |
| 0.4229 | 18400 | 0.0005 | - |
| 0.4240 | 18450 | 0.7018 | - |
| 0.4252 | 18500 | 0.0137 | - |
| 0.4263 | 18550 | 0.0053 | - |
| 0.4275 | 18600 | 0.0307 | - |
| 0.4286 | 18650 | 0.0127 | - |
| 0.4298 | 18700 | 0.2351 | - |
| 0.4309 | 18750 | 0.0047 | - |
| 0.4321 | 18800 | 0.0114 | - |
| 0.4332 | 18850 | 0.0153 | - |
| 0.4344 | 18900 | 0.3732 | - |
| 0.4355 | 18950 | 0.77 | - |
| 0.4367 | 19000 | 0.1298 | - |
| 0.4378 | 19050 | 0.7064 | - |
| 0.4390 | 19100 | 0.0 | - |
| 0.4401 | 19150 | 0.0044 | - |
| 0.4413 | 19200 | 0.7627 | - |
| 0.4424 | 19250 | 0.556 | - |
| 0.4436 | 19300 | 0.2105 | - |
| 0.4447 | 19350 | 0.8194 | - |
| 0.4459 | 19400 | 0.027 | - |
| 0.4470 | 19450 | 0.9308 | - |
| 0.4482 | 19500 | 0.0194 | - |
| 0.4493 | 19550 | 0.0144 | - |
| 0.4505 | 19600 | 0.584 | - |
| 0.4516 | 19650 | 0.0042 | - |
| 0.4527 | 19700 | 0.1354 | - |
| 0.4539 | 19750 | 0.2151 | - |
| 0.4550 | 19800 | 0.0006 | - |
| 0.4562 | 19850 | 0.3085 | - |
| 0.4573 | 19900 | 0.0543 | - |
| 0.4585 | 19950 | 0.0178 | - |
| 0.4596 | 20000 | 0.418 | - |
| 0.4608 | 20050 | 0.019 | - |
| 0.4619 | 20100 | 0.0001 | - |
| 0.4631 | 20150 | 0.5443 | - |
| 0.4642 | 20200 | 0.5111 | - |
| 0.4654 | 20250 | 0.0594 | - |
| 0.4665 | 20300 | 0.0086 | - |
| 0.4677 | 20350 | 0.0064 | - |
| 0.4688 | 20400 | 0.0577 | - |
| 0.4700 | 20450 | 0.0712 | - |
| 0.4711 | 20500 | 0.0271 | - |
| 0.4723 | 20550 | 0.5118 | - |
| 0.4734 | 20600 | 0.1834 | - |
| 0.4746 | 20650 | 0.0116 | - |
| 0.4757 | 20700 | 0.0052 | - |
| 0.4769 | 20750 | 0.7975 | - |
| 0.4780 | 20800 | 0.3037 | - |
| 0.4792 | 20850 | 0.0264 | - |
| 0.4803 | 20900 | 0.6911 | - |
| 0.4815 | 20950 | 0.008 | - |
| 0.4826 | 21000 | 0.0041 | - |
| 0.4838 | 21050 | 0.0379 | - |
| 0.4849 | 21100 | 0.0033 | - |
| 0.4861 | 21150 | 0.0297 | - |
| 0.4872 | 21200 | 0.0147 | - |
| 0.4884 | 21250 | 0.0001 | - |
| 0.4895 | 21300 | 0.0047 | - |
| 0.4907 | 21350 | 0.0247 | - |
| 0.4918 | 21400 | 0.0059 | - |
| 0.4930 | 21450 | 0.5724 | - |
| 0.4941 | 21500 | 0.3113 | - |
| 0.4953 | 21550 | 0.0026 | - |
| 0.4964 | 21600 | 0.835 | - |
| 0.4976 | 21650 | 0.0007 | - |
| 0.4987 | 21700 | 0.029 | - |
| 0.4999 | 21750 | 0.707 | - |
| 0.5010 | 21800 | 0.0211 | - |
| 0.5022 | 21850 | 0.0071 | - |
| 0.5033 | 21900 | 0.0009 | - |
| 0.5045 | 21950 | 0.0319 | - |
| 0.5056 | 22000 | 0.2219 | - |
| 0.5068 | 22050 | 0.0244 | - |
| 0.5079 | 22100 | 0.0341 | - |
| 0.5091 | 22150 | 0.0372 | - |
| 0.5102 | 22200 | 0.3981 | - |
| 0.5114 | 22250 | 0.0627 | - |
| 0.5125 | 22300 | 0.0559 | - |
| 0.5137 | 22350 | 0.5366 | - |
| 0.5148 | 22400 | 0.6952 | - |
| 0.5159 | 22450 | 0.0504 | - |
| 0.5171 | 22500 | 0.5098 | - |
| 0.5182 | 22550 | 0.6538 | - |
| 0.5194 | 22600 | 0.0015 | - |
| 0.5205 | 22650 | 0.0005 | - |
| 0.5217 | 22700 | 0.0974 | - |
| 0.5228 | 22750 | 0.009 | - |
| 0.5240 | 22800 | 0.6559 | - |
| 0.5251 | 22850 | 0.026 | - |
| 0.5263 | 22900 | 0.0049 | - |
| 0.5274 | 22950 | 0.0104 | - |
| 0.5286 | 23000 | 0.7918 | - |
| 0.5297 | 23050 | 0.0007 | - |
| 0.5309 | 23100 | 0.0015 | - |
| 0.5320 | 23150 | 0.2873 | - |
| 0.5332 | 23200 | 0.002 | - |
| 0.5343 | 23250 | 0.0067 | - |
| 0.5355 | 23300 | 0.2943 | - |
| 0.5366 | 23350 | 0.0029 | - |
| 0.5378 | 23400 | 0.0 | - |
| 0.5389 | 23450 | 0.0727 | - |
| 0.5401 | 23500 | 0.0084 | - |
| 0.5412 | 23550 | 0.0 | - |
| 0.5424 | 23600 | 0.0054 | - |
| 0.5435 | 23650 | 0.0004 | - |
| 0.5447 | 23700 | 0.5525 | - |
| 0.5458 | 23750 | 0.0251 | - |
| 0.5470 | 23800 | 0.0269 | - |
| 0.5481 | 23850 | 0.7426 | - |
| 0.5493 | 23900 | 0.0016 | - |
| 0.5504 | 23950 | 0.8143 | - |
| 0.5516 | 24000 | 0.5158 | - |
| 0.5527 | 24050 | 0.0047 | - |
| 0.5539 | 24100 | 0.0067 | - |
| 0.5550 | 24150 | 0.0 | - |
| 0.5562 | 24200 | 0.0045 | - |
| 0.5573 | 24250 | 0.0021 | - |
| 0.5585 | 24300 | 0.0012 | - |
| 0.5596 | 24350 | 0.3501 | - |
| 0.5608 | 24400 | 0.0101 | - |
| 0.5619 | 24450 | 0.0008 | - |
| 0.5631 | 24500 | 0.0112 | - |
| 0.5642 | 24550 | 0.0148 | - |
| 0.5654 | 24600 | 0.2246 | - |
| 0.5665 | 24650 | 0.1538 | - |
| 0.5677 | 24700 | 0.0001 | - |
| 0.5688 | 24750 | 0.0001 | - |
| 0.5700 | 24800 | 0.1296 | - |
| 0.5711 | 24850 | 0.0101 | - |
| 0.5723 | 24900 | 0.0032 | - |
| 0.5734 | 24950 | 0.0714 | - |
| 0.5746 | 25000 | 0.0 | - |
| 0.5757 | 25050 | 0.0886 | - |
| 0.5769 | 25100 | 0.0003 | - |
| 0.5780 | 25150 | 0.0041 | - |
| 0.5792 | 25200 | 0.0151 | - |
| 0.5803 | 25250 | 0.0099 | - |
| 0.5814 | 25300 | 0.0008 | - |
| 0.5826 | 25350 | 0.028 | - |
| 0.5837 | 25400 | 0.1064 | - |
| 0.5849 | 25450 | 0.0373 | - |
| 0.5860 | 25500 | 0.5589 | - |
| 0.5872 | 25550 | 0.2522 | - |
| 0.5883 | 25600 | 0.8553 | - |
| 0.5895 | 25650 | 0.0004 | - |
| 0.5906 | 25700 | 0.6575 | - |
| 0.5918 | 25750 | 0.0034 | - |
| 0.5929 | 25800 | 0.7313 | - |
| 0.5941 | 25850 | 0.8363 | - |
| 0.5952 | 25900 | 0.0156 | - |
| 0.5964 | 25950 | 0.0044 | - |
| 0.5975 | 26000 | 0.1387 | - |
| 0.5987 | 26050 | 0.0487 | - |
| 0.5998 | 26100 | 0.001 | - |
| 0.6010 | 26150 | 0.0004 | - |
| 0.6021 | 26200 | 0.0071 | - |
| 0.6033 | 26250 | 0.0012 | - |
| 0.6044 | 26300 | 0.021 | - |
| 0.6056 | 26350 | 0.0212 | - |
| 0.6067 | 26400 | 0.8472 | - |
| 0.6079 | 26450 | 0.5686 | - |
| 0.6090 | 26500 | 0.0721 | - |
| 0.6102 | 26550 | 0.0235 | - |
| 0.6113 | 26600 | 0.0 | - |
| 0.6125 | 26650 | 0.0098 | - |
| 0.6136 | 26700 | 0.3805 | - |
| 0.6148 | 26750 | 0.0525 | - |
| 0.6159 | 26800 | 0.0139 | - |
| 0.6171 | 26850 | 0.0011 | - |
| 0.6182 | 26900 | 0.0013 | - |
| 0.6194 | 26950 | 0.0058 | - |
| 0.6205 | 27000 | 0.0581 | - |
| 0.6217 | 27050 | 0.477 | - |
| 0.6228 | 27100 | 0.0073 | - |
| 0.6240 | 27150 | 0.0033 | - |
| 0.6251 | 27200 | 0.0082 | - |
| 0.6263 | 27250 | 0.0028 | - |
| 0.6274 | 27300 | 0.0001 | - |
| 0.6286 | 27350 | 0.0265 | - |
| 0.6297 | 27400 | 0.097 | - |
| 0.6309 | 27450 | 0.2339 | - |
| 0.6320 | 27500 | 0.5429 | - |
| 0.6332 | 27550 | 0.3859 | - |
| 0.6343 | 27600 | 0.0116 | - |
| 0.6355 | 27650 | 0.0006 | - |
| 0.6366 | 27700 | 0.0018 | - |
| 0.6378 | 27750 | 0.0197 | - |
| 0.6389 | 27800 | 0.0085 | - |
| 0.6401 | 27850 | 0.0 | - |
| 0.6412 | 27900 | 0.0141 | - |
| 0.6424 | 27950 | 0.1121 | - |
| 0.6435 | 28000 | 0.0123 | - |
| 0.6446 | 28050 | 0.3018 | - |
| 0.6458 | 28100 | 0.7669 | - |
| 0.6469 | 28150 | 0.6745 | - |
| 0.6481 | 28200 | 0.4283 | - |
| 0.6492 | 28250 | 0.0237 | - |
| 0.6504 | 28300 | 0.8327 | - |
| 0.6515 | 28350 | 0.1052 | - |
| 0.6527 | 28400 | 0.4264 | - |
| 0.6538 | 28450 | 0.6714 | - |
| 0.6550 | 28500 | 0.0039 | - |
| 0.6561 | 28550 | 0.0065 | - |
| 0.6573 | 28600 | 0.0178 | - |
| 0.6584 | 28650 | 0.3817 | - |
| 0.6596 | 28700 | 0.0584 | - |
| 0.6607 | 28750 | 0.0217 | - |
| 0.6619 | 28800 | 0.0019 | - |
| 0.6630 | 28850 | 0.4605 | - |
| 0.6642 | 28900 | 0.0049 | - |
| 0.6653 | 28950 | 0.0011 | - |
| 0.6665 | 29000 | 0.569 | - |
| 0.6676 | 29050 | 0.0 | - |
| 0.6688 | 29100 | 0.0874 | - |
| 0.6699 | 29150 | 0.5388 | - |
| 0.6711 | 29200 | 0.4093 | - |
| 0.6722 | 29250 | 0.3076 | - |
| 0.6734 | 29300 | 0.4542 | - |
| 0.6745 | 29350 | 0.2569 | - |
| 0.6757 | 29400 | 0.0155 | - |
| 0.6768 | 29450 | 0.1146 | - |
| 0.6780 | 29500 | 0.1341 | - |
| 0.6791 | 29550 | 0.0304 | - |
| 0.6803 | 29600 | 0.0095 | - |
| 0.6814 | 29650 | 0.443 | - |
| 0.6826 | 29700 | 0.5068 | - |
| 0.6837 | 29750 | 0.024 | - |
| 0.6849 | 29800 | 0.0079 | - |
| 0.6860 | 29850 | 0.1769 | - |
| 0.6872 | 29900 | 0.0001 | - |
| 0.6883 | 29950 | 0.0104 | - |
| 0.6895 | 30000 | 0.4234 | - |
| 0.6906 | 30050 | 0.0042 | - |
| 0.6918 | 30100 | 0.3934 | - |
| 0.6929 | 30150 | 0.0119 | - |
| 0.6941 | 30200 | 0.0012 | - |
| 0.6952 | 30250 | 0.4434 | - |
| 0.6964 | 30300 | 0.6101 | - |
| 0.6975 | 30350 | 0.3655 | - |
| 0.6987 | 30400 | 0.168 | - |
| 0.6998 | 30450 | 0.8202 | - |
| 0.7010 | 30500 | 0.0906 | - |
| 0.7021 | 30550 | 0.0287 | - |
| 0.7033 | 30600 | 0.3671 | - |
| 0.7044 | 30650 | 0.7084 | - |
| 0.7056 | 30700 | 0.3632 | - |
| 0.7067 | 30750 | 0.0027 | - |
| 0.7079 | 30800 | 0.0451 | - |
| 0.7090 | 30850 | 0.3421 | - |
| 0.7101 | 30900 | 0.0077 | - |
| 0.7113 | 30950 | 0.0404 | - |
| 0.7124 | 31000 | 0.7512 | - |
| 0.7136 | 31050 | 0.2898 | - |
| 0.7147 | 31100 | 0.0721 | - |
| 0.7159 | 31150 | 0.009 | - |
| 0.7170 | 31200 | 0.0474 | - |
| 0.7182 | 31250 | 0.0041 | - |
| 0.7193 | 31300 | 0.0249 | - |
| 0.7205 | 31350 | 0.3519 | - |
| 0.7216 | 31400 | 0.0936 | - |
| 0.7228 | 31450 | 0.0049 | - |
| 0.7239 | 31500 | 0.0035 | - |
| 0.7251 | 31550 | 0.0296 | - |
| 0.7262 | 31600 | 0.0264 | - |
| 0.7274 | 31650 | 0.5318 | - |
| 0.7285 | 31700 | 0.0029 | - |
| 0.7297 | 31750 | 0.7741 | - |
| 0.7308 | 31800 | 0.0807 | - |
| 0.7320 | 31850 | 0.0154 | - |
| 0.7331 | 31900 | 0.0181 | - |
| 0.7343 | 31950 | 0.7881 | - |
| 0.7354 | 32000 | 0.2723 | - |
| 0.7366 | 32050 | 0.0549 | - |
| 0.7377 | 32100 | 0.0198 | - |
| 0.7389 | 32150 | 0.0083 | - |
| 0.7400 | 32200 | 0.4985 | - |
| 0.7412 | 32250 | 0.0111 | - |
| 0.7423 | 32300 | 0.0057 | - |
| 0.7435 | 32350 | 0.0393 | - |
| 0.7446 | 32400 | 0.0786 | - |
| 0.7458 | 32450 | 0.1888 | - |
| 0.7469 | 32500 | 0.0382 | - |
| 0.7481 | 32550 | 0.5611 | - |
| 0.7492 | 32600 | 0.0749 | - |
| 0.7504 | 32650 | 0.0064 | - |
| 0.7515 | 32700 | 0.0002 | - |
| 0.7527 | 32750 | 0.0159 | - |
| 0.7538 | 32800 | 0.025 | - |
| 0.7550 | 32850 | 0.0271 | - |
| 0.7561 | 32900 | 0.251 | - |
| 0.7573 | 32950 | 0.0002 | - |
| 0.7584 | 33000 | 0.1407 | - |
| 0.7596 | 33050 | 0.1596 | - |
| 0.7607 | 33100 | 0.0069 | - |
| 0.7619 | 33150 | 0.0655 | - |
| 0.7630 | 33200 | 0.0435 | - |
| 0.7642 | 33250 | 0.0032 | - |
| 0.7653 | 33300 | 0.1908 | - |
| 0.7665 | 33350 | 0.4326 | - |
| 0.7676 | 33400 | 0.1699 | - |
| 0.7688 | 33450 | 0.005 | - |
| 0.7699 | 33500 | 0.4937 | - |
| 0.7711 | 33550 | 0.0635 | - |
| 0.7722 | 33600 | 0.0042 | - |
| 0.7733 | 33650 | 0.0001 | - |
| 0.7745 | 33700 | 0.0088 | - |
| 0.7756 | 33750 | 0.0313 | - |
| 0.7768 | 33800 | 0.0072 | - |
| 0.7779 | 33850 | 0.0291 | - |
| 0.7791 | 33900 | 0.0037 | - |
| 0.7802 | 33950 | 0.0192 | - |
| 0.7814 | 34000 | 0.0017 | - |
| 0.7825 | 34050 | 0.0006 | - |
| 0.7837 | 34100 | 0.0119 | - |
| 0.7848 | 34150 | 0.1647 | - |
| 0.7860 | 34200 | 0.009 | - |
| 0.7871 | 34250 | 0.0004 | - |
| 0.7883 | 34300 | 0.5268 | - |
| 0.7894 | 34350 | 0.0523 | - |
| 0.7906 | 34400 | 0.0537 | - |
| 0.7917 | 34450 | 0.1654 | - |
| 0.7929 | 34500 | 0.0003 | - |
| 0.7940 | 34550 | 0.0021 | - |
| 0.7952 | 34600 | 0.0016 | - |
| 0.7963 | 34650 | 0.0002 | - |
| 0.7975 | 34700 | 0.0001 | - |
| 0.7986 | 34750 | 0.0001 | - |
| 0.7998 | 34800 | 0.0204 | - |
| 0.8009 | 34850 | 0.0047 | - |
| 0.8021 | 34900 | 0.2942 | - |
| 0.8032 | 34950 | 0.0039 | - |
| 0.8044 | 35000 | 0.0237 | - |
| 0.8055 | 35050 | 0.0002 | - |
| 0.8067 | 35100 | 0.0009 | - |
| 0.8078 | 35150 | 0.7804 | - |
| 0.8090 | 35200 | 0.0012 | - |
| 0.8101 | 35250 | 0.0303 | - |
| 0.8113 | 35300 | 0.0265 | - |
| 0.8124 | 35350 | 0.0071 | - |
| 0.8136 | 35400 | 0.0053 | - |
| 0.8147 | 35450 | 0.068 | - |
| 0.8159 | 35500 | 0.0233 | - |
| 0.8170 | 35550 | 0.4748 | - |
| 0.8182 | 35600 | 0.0253 | - |
| 0.8193 | 35650 | 0.0 | - |
| 0.8205 | 35700 | 0.2029 | - |
| 0.8216 | 35750 | 0.0063 | - |
| 0.8228 | 35800 | 0.0179 | - |
| 0.8239 | 35850 | 0.0039 | - |
| 0.8251 | 35900 | 0.0123 | - |
| 0.8262 | 35950 | 0.3021 | - |
| 0.8274 | 36000 | 0.0096 | - |
| 0.8285 | 36050 | 0.3735 | - |
| 0.8297 | 36100 | 0.0281 | - |
| 0.8308 | 36150 | 0.0612 | - |
| 0.8320 | 36200 | 0.028 | - |
| 0.8331 | 36250 | 0.6296 | - |
| 0.8343 | 36300 | 0.1161 | - |
| 0.8354 | 36350 | 0.0249 | - |
| 0.8366 | 36400 | 0.0 | - |
| 0.8377 | 36450 | 0.4144 | - |
| 0.8388 | 36500 | 0.1574 | - |
| 0.8400 | 36550 | 0.0083 | - |
| 0.8411 | 36600 | 0.0385 | - |
| 0.8423 | 36650 | 0.4681 | - |
| 0.8434 | 36700 | 0.0628 | - |
| 0.8446 | 36750 | 0.0005 | - |
| 0.8457 | 36800 | 0.2092 | - |
| 0.8469 | 36850 | 0.009 | - |
| 0.8480 | 36900 | 0.031 | - |
| 0.8492 | 36950 | 0.3659 | - |
| 0.8503 | 37000 | 0.0003 | - |
| 0.8515 | 37050 | 0.0117 | - |
| 0.8526 | 37100 | 0.0061 | - |
| 0.8538 | 37150 | 0.0163 | - |
| 0.8549 | 37200 | 0.0 | - |
| 0.8561 | 37250 | 0.0668 | - |
| 0.8572 | 37300 | 0.0108 | - |
| 0.8584 | 37350 | 0.1344 | - |
| 0.8595 | 37400 | 0.0196 | - |
| 0.8607 | 37450 | 0.0006 | - |
| 0.8618 | 37500 | 0.0005 | - |
| 0.8630 | 37550 | 0.45 | - |
| 0.8641 | 37600 | 0.0002 | - |
| 0.8653 | 37650 | 0.0032 | - |
| 0.8664 | 37700 | 0.0035 | - |
| 0.8676 | 37750 | 0.1411 | - |
| 0.8687 | 37800 | 0.007 | - |
| 0.8699 | 37850 | 0.0015 | - |
| 0.8710 | 37900 | 0.6745 | - |
| 0.8722 | 37950 | 0.0002 | - |
| 0.8733 | 38000 | 0.2138 | - |
| 0.8745 | 38050 | 0.0092 | - |
| 0.8756 | 38100 | 0.4335 | - |
| 0.8768 | 38150 | 0.0011 | - |
| 0.8779 | 38200 | 0.0265 | - |
| 0.8791 | 38250 | 0.6394 | - |
| 0.8802 | 38300 | 0.3108 | - |
| 0.8814 | 38350 | 0.1918 | - |
| 0.8825 | 38400 | 0.0006 | - |
| 0.8837 | 38450 | 0.0075 | - |
| 0.8848 | 38500 | 0.5738 | - |
| 0.8860 | 38550 | 0.008 | - |
| 0.8871 | 38600 | 0.0043 | - |
| 0.8883 | 38650 | 0.7087 | - |
| 0.8894 | 38700 | 0.0044 | - |
| 0.8906 | 38750 | 0.0045 | - |
| 0.8917 | 38800 | 0.0009 | - |
| 0.8929 | 38850 | 0.0118 | - |
| 0.8940 | 38900 | 0.2812 | - |
| 0.8952 | 38950 | 0.0581 | - |
| 0.8963 | 39000 | 0.0016 | - |
| 0.8975 | 39050 | 0.0284 | - |
| 0.8986 | 39100 | 0.0061 | - |
| 0.8998 | 39150 | 0.13 | - |
| 0.9009 | 39200 | 0.0061 | - |
| 0.9021 | 39250 | 0.0508 | - |
| 0.9032 | 39300 | 0.214 | - |
| 0.9043 | 39350 | 0.0032 | - |
| 0.9055 | 39400 | 0.0234 | - |
| 0.9066 | 39450 | 0.0318 | - |
| 0.9078 | 39500 | 0.003 | - |
| 0.9089 | 39550 | 0.3719 | - |
| 0.9101 | 39600 | 0.0092 | - |
| 0.9112 | 39650 | 0.0027 | - |
| 0.9124 | 39700 | 0.3007 | - |
| 0.9135 | 39750 | 0.0535 | - |
| 0.9147 | 39800 | 0.0027 | - |
| 0.9158 | 39850 | 0.8316 | - |
| 0.9170 | 39900 | 0.3543 | - |
| 0.9181 | 39950 | 0.7228 | - |
| 0.9193 | 40000 | 0.4475 | - |
| 0.9204 | 40050 | 0.0044 | - |
| 0.9216 | 40100 | 0.0077 | - |
| 0.9227 | 40150 | 0.0668 | - |
| 0.9239 | 40200 | 0.0036 | - |
| 0.9250 | 40250 | 0.0032 | - |
| 0.9262 | 40300 | 0.035 | - |
| 0.9273 | 40350 | 0.011 | - |
| 0.9285 | 40400 | 0.0 | - |
| 0.9296 | 40450 | 0.5078 | - |
| 0.9308 | 40500 | 0.0003 | - |
| 0.9319 | 40550 | 0.0 | - |
| 0.9331 | 40600 | 0.0 | - |
| 0.9342 | 40650 | 0.0029 | - |
| 0.9354 | 40700 | 0.0001 | - |
| 0.9365 | 40750 | 0.0003 | - |
| 0.9377 | 40800 | 0.2938 | - |
| 0.9388 | 40850 | 0.0059 | - |
| 0.9400 | 40900 | 0.0646 | - |
| 0.9411 | 40950 | 0.0067 | - |
| 0.9423 | 41000 | 0.001 | - |
| 0.9434 | 41050 | 0.7928 | - |
| 0.9446 | 41100 | 0.0013 | - |
| 0.9457 | 41150 | 0.0271 | - |
| 0.9469 | 41200 | 0.0322 | - |
| 0.9480 | 41250 | 0.0127 | - |
| 0.9492 | 41300 | 0.0 | - |
| 0.9503 | 41350 | 0.4948 | - |
| 0.9515 | 41400 | 0.0185 | - |
| 0.9526 | 41450 | 0.4775 | - |
| 0.9538 | 41500 | 0.0046 | - |
| 0.9549 | 41550 | 0.0002 | - |
| 0.9561 | 41600 | 0.352 | - |
| 0.9572 | 41650 | 0.5607 | - |
| 0.9584 | 41700 | 0.0003 | - |
| 0.9595 | 41750 | 0.1911 | - |
| 0.9607 | 41800 | 0.0117 | - |
| 0.9618 | 41850 | 0.0008 | - |
| 0.9630 | 41900 | 0.0029 | - |
| 0.9641 | 41950 | 0.0034 | - |
| 0.9653 | 42000 | 0.0128 | - |
| 0.9664 | 42050 | 0.3599 | - |
| 0.9675 | 42100 | 0.5342 | - |
| 0.9687 | 42150 | 0.0333 | - |
| 0.9698 | 42200 | 0.0358 | - |
| 0.9710 | 42250 | 0.0039 | - |
| 0.9721 | 42300 | 0.0001 | - |
| 0.9733 | 42350 | 0.0066 | - |
| 0.9744 | 42400 | 0.0006 | - |
| 0.9756 | 42450 | 0.0005 | - |
| 0.9767 | 42500 | 0.5468 | - |
| 0.9779 | 42550 | 0.0121 | - |
| 0.9790 | 42600 | 0.0833 | - |
| 0.9802 | 42650 | 0.0152 | - |
| 0.9813 | 42700 | 0.001 | - |
| 0.9825 | 42750 | 0.0074 | - |
| 0.9836 | 42800 | 0.8221 | - |
| 0.9848 | 42850 | 0.0039 | - |
| 0.9859 | 42900 | 0.1647 | - |
| 0.9871 | 42950 | 0.0014 | - |
| 0.9882 | 43000 | 0.0006 | - |
| 0.9894 | 43050 | 0.0008 | - |
| 0.9905 | 43100 | 0.0 | - |
| 0.9917 | 43150 | 0.1409 | - |
| 0.9928 | 43200 | 0.0004 | - |
| 0.9940 | 43250 | 0.0006 | - |
| 0.9951 | 43300 | 0.0634 | - |
| 0.9963 | 43350 | 0.1843 | - |
| 0.9974 | 43400 | 0.0133 | - |
| 0.9986 | 43450 | 0.2553 | - |
| 0.9997 | 43500 | 0.0005 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.2
- PyTorch: 2.1.0+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}
}
```
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