---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-roberta-large-v1
metrics:
- accuracy
widget:
- text: ' I''m a runner and I have to say you''re wrong'
- text: ' I walk everywhere on foot and I feel like the greatest loser in my city
I have tendonitis in both knees from long distance running (my last favorite hobby
which I can''t even do anymore) I''ve wasted my prime years'
- text: I can't water the garden because my neighbours are watching
- text: ' That''s kind of the nature of my volunteer work, but you could volunteer
with a food bank or boys and girls club, which would involve more social interaction
Just breaking that cycle by going for a short walk around the neighbourhood is
a good idea'
- text: ' Like jumping into a pool, or starting an assignment'
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/all-roberta-large-v1
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.4766666666666667
name: Accuracy
---
# SetFit with sentence-transformers/all-roberta-large-v1
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3.0 | - "It's whenever I look someone in the eye, when I'm on my bike or when I'm walking somewhere, I feel like they look at me and ridicule me in their headsIt's whenever I look someone in the eye, when I'm on my bike or when I'm walking somewhere, I feel like they look at me and ridicule me in their heads"
- " While I ended up making progress, it wasn't as fast as I had hoped and I still had a lot of trouble doing some things (such as jogging in public)"
- ' My social anxiety has got a lot worse as there’s been a lot of recent trauma and I can’t even go outside anymore, a social worker cane to visit and agreed that she’d help me get counselling at home, I’m hoping soon I’ll start getting better'
|
| 0.0 | - ' holy shit the food runs with people visiting lol'
- " The problem with doing this is you're just laying there and not focusing on any outside activities and thus you become prey to hell lot of negative thoughts"
- ' You might feel relaxed not having to deal with people, but trust me humans are social creatures and in the long run you’re going to become miserable from loneliness'
|
| 1.0 | - ' I run 8'
- " Is there anything actually wrong with your legs, or are you just imagining things? Are they really staring at you, or is it in your head? If you can't deal with going out on your own, try going outside with someone you're comfortable with"
- ' Being outside is an extra plus!! Keep going :) '
|
| 2.0 | - ' It summer time and I have plans to hike and do other fun stuff but i cant do them myself '
- ' She knows that I barely go outside and tries to get me to go to out but only with muslims cause she believes that other kids are bad for me'
- ' It depends on if you live in an area where getting around on a bike is a viable option I’ve heard of people delivering Doordash or Postmates on bicycles but it generally is only more viable if you live in an urban area where restaurants and delivery addresses are closer together, in a suburban area that is more car-focused it’s difficult Though if deliveries are slow they will usually make you do other tasks around the restaurant, whereas with apps you can either run multiple apps at once or just fuck around between deliveries if it’s slow '
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4767 |
## 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("Omar-Nasr/setfitmodel")
# Run inference
preds = model(" I'm a runner and I have to say you're wrong")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:-----|
| Word count | 3 | 40.3722 | 1083 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 90 |
| 1.0 | 90 |
| 2.0 | 90 |
| 3.0 | 90 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (4, 4)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2936 | - |
| 0.0041 | 50 | 0.3404 | - |
| 0.0082 | 100 | 0.2465 | - |
| 0.0123 | 150 | 0.287 | - |
| 0.0165 | 200 | 0.1955 | - |
| 0.0206 | 250 | 0.2187 | - |
| 0.0247 | 300 | 0.2239 | - |
| 0.0288 | 350 | 0.2709 | - |
| 0.0329 | 400 | 0.2919 | - |
| 0.0370 | 450 | 0.2689 | - |
| 0.0412 | 500 | 0.176 | - |
| 0.0453 | 550 | 0.2699 | - |
| 0.0494 | 600 | 0.2538 | - |
| 0.0535 | 650 | 0.2029 | - |
| 0.0576 | 700 | 0.2566 | - |
| 0.0617 | 750 | 0.1106 | - |
| 0.0658 | 800 | 0.1625 | - |
| 0.0700 | 850 | 0.1276 | - |
| 0.0741 | 900 | 0.1603 | - |
| 0.0782 | 950 | 0.0294 | - |
| 0.0823 | 1000 | 0.011 | - |
| 0.0864 | 1050 | 0.1924 | - |
| 0.0905 | 1100 | 0.0149 | - |
| 0.0947 | 1150 | 0.1249 | - |
| 0.0988 | 1200 | 0.1251 | - |
| 0.1029 | 1250 | 0.008 | - |
| 0.1070 | 1300 | 0.0008 | - |
| 0.1111 | 1350 | 0.009 | - |
| 0.1152 | 1400 | 0.0007 | - |
| 0.1193 | 1450 | 0.0013 | - |
| 0.1235 | 1500 | 0.0016 | - |
| 0.1276 | 1550 | 0.0042 | - |
| 0.1317 | 1600 | 0.0003 | - |
| 0.1358 | 1650 | 0.0002 | - |
| 0.1399 | 1700 | 0.0003 | - |
| 0.1440 | 1750 | 0.0002 | - |
| 0.1481 | 1800 | 0.0002 | - |
| 0.1523 | 1850 | 0.0002 | - |
| 0.1564 | 1900 | 0.0003 | - |
| 0.1605 | 1950 | 0.0001 | - |
| 0.1646 | 2000 | 0.0001 | - |
| 0.1687 | 2050 | 0.0002 | - |
| 0.1728 | 2100 | 0.0001 | - |
| 0.1770 | 2150 | 0.0001 | - |
| 0.1811 | 2200 | 0.0001 | - |
| 0.1852 | 2250 | 0.0001 | - |
| 0.1893 | 2300 | 0.0001 | - |
| 0.1934 | 2350 | 0.0002 | - |
| 0.1975 | 2400 | 0.0001 | - |
| 0.2016 | 2450 | 0.0002 | - |
| 0.2058 | 2500 | 0.0001 | - |
| 0.2099 | 2550 | 0.0001 | - |
| 0.2140 | 2600 | 0.0001 | - |
| 0.2181 | 2650 | 0.0 | - |
| 0.2222 | 2700 | 0.0001 | - |
| 0.2263 | 2750 | 0.0001 | - |
| 0.2305 | 2800 | 0.0001 | - |
| 0.2346 | 2850 | 0.0 | - |
| 0.2387 | 2900 | 0.0 | - |
| 0.2428 | 2950 | 0.0001 | - |
| 0.2469 | 3000 | 0.0001 | - |
| 0.2510 | 3050 | 0.0002 | - |
| 0.2551 | 3100 | 0.0001 | - |
| 0.2593 | 3150 | 0.0 | - |
| 0.2634 | 3200 | 0.0 | - |
| 0.2675 | 3250 | 0.0001 | - |
| 0.2716 | 3300 | 0.0001 | - |
| 0.2757 | 3350 | 0.0 | - |
| 0.2798 | 3400 | 0.0 | - |
| 0.2840 | 3450 | 0.0 | - |
| 0.2881 | 3500 | 0.0 | - |
| 0.2922 | 3550 | 0.0 | - |
| 0.2963 | 3600 | 0.0 | - |
| 0.3004 | 3650 | 0.0 | - |
| 0.3045 | 3700 | 0.0 | - |
| 0.3086 | 3750 | 0.0001 | - |
| 0.3128 | 3800 | 0.0 | - |
| 0.3169 | 3850 | 0.0 | - |
| 0.3210 | 3900 | 0.0001 | - |
| 0.3251 | 3950 | 0.0001 | - |
| 0.3292 | 4000 | 0.0 | - |
| 0.3333 | 4050 | 0.0001 | - |
| 0.3374 | 4100 | 0.0 | - |
| 0.3416 | 4150 | 0.0 | - |
| 0.3457 | 4200 | 0.482 | - |
| 0.3498 | 4250 | 0.0384 | - |
| 0.3539 | 4300 | 0.1998 | - |
| 0.3580 | 4350 | 0.2232 | - |
| 0.3621 | 4400 | 0.1794 | - |
| 0.3663 | 4450 | 0.0911 | - |
| 0.3704 | 4500 | 0.1016 | - |
| 0.3745 | 4550 | 0.1266 | - |
| 0.3786 | 4600 | 0.0004 | - |
| 0.3827 | 4650 | 0.0005 | - |
| 0.3868 | 4700 | 0.0001 | - |
| 0.3909 | 4750 | 0.0002 | - |
| 0.3951 | 4800 | 0.0005 | - |
| 0.3992 | 4850 | 0.0001 | - |
| 0.4033 | 4900 | 0.0003 | - |
| 0.4074 | 4950 | 0.0002 | - |
| 0.4115 | 5000 | 0.0001 | - |
| 0.4156 | 5050 | 0.0001 | - |
| 0.4198 | 5100 | 0.0001 | - |
| 0.4239 | 5150 | 0.0 | - |
| 0.4280 | 5200 | 0.0 | - |
| 0.4321 | 5250 | 0.0 | - |
| 0.4362 | 5300 | 0.0001 | - |
| 0.4403 | 5350 | 0.0 | - |
| 0.4444 | 5400 | 0.0 | - |
| 0.4486 | 5450 | 0.0 | - |
| 0.4527 | 5500 | 0.0 | - |
| 0.4568 | 5550 | 0.0001 | - |
| 0.4609 | 5600 | 0.0 | - |
| 0.4650 | 5650 | 0.0 | - |
| 0.4691 | 5700 | 0.0 | - |
| 0.4733 | 5750 | 0.0001 | - |
| 0.4774 | 5800 | 0.0 | - |
| 0.4815 | 5850 | 0.0 | - |
| 0.4856 | 5900 | 0.0 | - |
| 0.4897 | 5950 | 0.0 | - |
| 0.4938 | 6000 | 0.0 | - |
| 0.4979 | 6050 | 0.0001 | - |
| 0.5021 | 6100 | 0.0 | - |
| 0.5062 | 6150 | 0.0 | - |
| 0.5103 | 6200 | 0.0 | - |
| 0.5144 | 6250 | 0.0 | - |
| 0.5185 | 6300 | 0.0 | - |
| 0.5226 | 6350 | 0.0 | - |
| 0.5267 | 6400 | 0.0 | - |
| 0.5309 | 6450 | 0.0 | - |
| 0.5350 | 6500 | 0.0 | - |
| 0.5391 | 6550 | 0.0 | - |
| 0.5432 | 6600 | 0.0 | - |
| 0.5473 | 6650 | 0.0 | - |
| 0.5514 | 6700 | 0.0 | - |
| 0.5556 | 6750 | 0.0 | - |
| 0.5597 | 6800 | 0.0 | - |
| 0.5638 | 6850 | 0.0 | - |
| 0.5679 | 6900 | 0.0 | - |
| 0.5720 | 6950 | 0.0 | - |
| 0.5761 | 7000 | 0.0 | - |
| 0.5802 | 7050 | 0.0 | - |
| 0.5844 | 7100 | 0.0 | - |
| 0.5885 | 7150 | 0.0 | - |
| 0.5926 | 7200 | 0.0 | - |
| 0.5967 | 7250 | 0.0001 | - |
| 0.6008 | 7300 | 0.0 | - |
| 0.6049 | 7350 | 0.0 | - |
| 0.6091 | 7400 | 0.0 | - |
| 0.6132 | 7450 | 0.0 | - |
| 0.6173 | 7500 | 0.0 | - |
| 0.6214 | 7550 | 0.0 | - |
| 0.6255 | 7600 | 0.0 | - |
| 0.6296 | 7650 | 0.0 | - |
| 0.6337 | 7700 | 0.0 | - |
| 0.6379 | 7750 | 0.0 | - |
| 0.6420 | 7800 | 0.0 | - |
| 0.6461 | 7850 | 0.0 | - |
| 0.6502 | 7900 | 0.001 | - |
| 0.6543 | 7950 | 0.0061 | - |
| 0.6584 | 8000 | 0.0966 | - |
| 0.6626 | 8050 | 0.0911 | - |
| 0.6667 | 8100 | 0.1791 | - |
| 0.6708 | 8150 | 0.0001 | - |
| 0.6749 | 8200 | 0.0354 | - |
| 0.6790 | 8250 | 0.0061 | - |
| 0.6831 | 8300 | 0.0 | - |
| 0.6872 | 8350 | 0.0 | - |
| 0.6914 | 8400 | 0.0001 | - |
| 0.6955 | 8450 | 0.0 | - |
| 0.6996 | 8500 | 0.0 | - |
| 0.7037 | 8550 | 0.0 | - |
| 0.7078 | 8600 | 0.0 | - |
| 0.7119 | 8650 | 0.0 | - |
| 0.7160 | 8700 | 0.0 | - |
| 0.7202 | 8750 | 0.0 | - |
| 0.7243 | 8800 | 0.0 | - |
| 0.7284 | 8850 | 0.0 | - |
| 0.7325 | 8900 | 0.0 | - |
| 0.7366 | 8950 | 0.0 | - |
| 0.7407 | 9000 | 0.0 | - |
| 0.7449 | 9050 | 0.0 | - |
| 0.7490 | 9100 | 0.0001 | - |
| 0.7531 | 9150 | 0.0 | - |
| 0.7572 | 9200 | 0.0 | - |
| 0.7613 | 9250 | 0.0 | - |
| 0.7654 | 9300 | 0.0 | - |
| 0.7695 | 9350 | 0.0 | - |
| 0.7737 | 9400 | 0.0 | - |
| 0.7778 | 9450 | 0.0 | - |
| 0.7819 | 9500 | 0.0 | - |
| 0.7860 | 9550 | 0.0 | - |
| 0.7901 | 9600 | 0.0 | - |
| 0.7942 | 9650 | 0.0 | - |
| 0.7984 | 9700 | 0.0 | - |
| 0.8025 | 9750 | 0.0 | - |
| 0.8066 | 9800 | 0.0 | - |
| 0.8107 | 9850 | 0.0 | - |
| 0.8148 | 9900 | 0.0 | - |
| 0.8189 | 9950 | 0.0 | - |
| 0.8230 | 10000 | 0.0 | - |
| 0.8272 | 10050 | 0.0 | - |
| 0.8313 | 10100 | 0.0 | - |
| 0.8354 | 10150 | 0.0 | - |
| 0.8395 | 10200 | 0.0 | - |
| 0.8436 | 10250 | 0.0 | - |
| 0.8477 | 10300 | 0.0 | - |
| 0.8519 | 10350 | 0.0 | - |
| 0.8560 | 10400 | 0.0 | - |
| 0.8601 | 10450 | 0.0 | - |
| 0.8642 | 10500 | 0.0 | - |
| 0.8683 | 10550 | 0.0 | - |
| 0.8724 | 10600 | 0.0 | - |
| 0.8765 | 10650 | 0.0 | - |
| 0.8807 | 10700 | 0.0 | - |
| 0.8848 | 10750 | 0.0 | - |
| 0.8889 | 10800 | 0.0 | - |
| 0.8930 | 10850 | 0.0 | - |
| 0.8971 | 10900 | 0.0 | - |
| 0.9012 | 10950 | 0.0 | - |
| 0.9053 | 11000 | 0.0 | - |
| 0.9095 | 11050 | 0.0 | - |
| 0.9136 | 11100 | 0.0 | - |
| 0.9177 | 11150 | 0.0 | - |
| 0.9218 | 11200 | 0.0 | - |
| 0.9259 | 11250 | 0.0 | - |
| 0.9300 | 11300 | 0.0 | - |
| 0.9342 | 11350 | 0.0 | - |
| 0.9383 | 11400 | 0.0 | - |
| 0.9424 | 11450 | 0.0 | - |
| 0.9465 | 11500 | 0.0 | - |
| 0.9506 | 11550 | 0.0 | - |
| 0.9547 | 11600 | 0.0 | - |
| 0.9588 | 11650 | 0.0 | - |
| 0.9630 | 11700 | 0.0 | - |
| 0.9671 | 11750 | 0.0 | - |
| 0.9712 | 11800 | 0.0 | - |
| 0.9753 | 11850 | 0.0 | - |
| 0.9794 | 11900 | 0.0 | - |
| 0.9835 | 11950 | 0.0 | - |
| 0.9877 | 12000 | 0.0 | - |
| 0.9918 | 12050 | 0.0 | - |
| 0.9959 | 12100 | 0.0 | - |
| 1.0 | 12150 | 0.0 | - |
| 1.0041 | 12200 | 0.0 | - |
| 1.0082 | 12250 | 0.0 | - |
| 1.0123 | 12300 | 0.0 | - |
| 1.0165 | 12350 | 0.0 | - |
| 1.0206 | 12400 | 0.0 | - |
| 1.0247 | 12450 | 0.0 | - |
| 1.0288 | 12500 | 0.0 | - |
| 1.0329 | 12550 | 0.0 | - |
| 1.0370 | 12600 | 0.0 | - |
| 1.0412 | 12650 | 0.0 | - |
| 1.0453 | 12700 | 0.0 | - |
| 1.0494 | 12750 | 0.0 | - |
| 1.0535 | 12800 | 0.0 | - |
| 1.0576 | 12850 | 0.0001 | - |
| 1.0617 | 12900 | 0.0 | - |
| 1.0658 | 12950 | 0.0 | - |
| 1.0700 | 13000 | 0.0 | - |
| 1.0741 | 13050 | 0.0 | - |
| 1.0782 | 13100 | 0.0 | - |
| 1.0823 | 13150 | 0.0 | - |
| 1.0864 | 13200 | 0.0 | - |
| 1.0905 | 13250 | 0.0 | - |
| 1.0947 | 13300 | 0.0 | - |
| 1.0988 | 13350 | 0.0 | - |
| 1.1029 | 13400 | 0.0 | - |
| 1.1070 | 13450 | 0.0 | - |
| 1.1111 | 13500 | 0.0 | - |
| 1.1152 | 13550 | 0.0 | - |
| 1.1193 | 13600 | 0.0 | - |
| 1.1235 | 13650 | 0.0 | - |
| 1.1276 | 13700 | 0.0 | - |
| 1.1317 | 13750 | 0.0 | - |
| 1.1358 | 13800 | 0.0 | - |
| 1.1399 | 13850 | 0.0 | - |
| 1.1440 | 13900 | 0.0 | - |
| 1.1481 | 13950 | 0.0 | - |
| 1.1523 | 14000 | 0.0 | - |
| 1.1564 | 14050 | 0.0 | - |
| 1.1605 | 14100 | 0.0 | - |
| 1.1646 | 14150 | 0.0 | - |
| 1.1687 | 14200 | 0.0 | - |
| 1.1728 | 14250 | 0.0 | - |
| 1.1770 | 14300 | 0.0 | - |
| 1.1811 | 14350 | 0.0 | - |
| 1.1852 | 14400 | 0.0 | - |
| 1.1893 | 14450 | 0.0 | - |
| 1.1934 | 14500 | 0.0 | - |
| 1.1975 | 14550 | 0.0 | - |
| 1.2016 | 14600 | 0.0 | - |
| 1.2058 | 14650 | 0.0146 | - |
| 1.2099 | 14700 | 0.4649 | - |
| 1.2140 | 14750 | 0.0596 | - |
| 1.2181 | 14800 | 0.0007 | - |
| 1.2222 | 14850 | 0.0006 | - |
| 1.2263 | 14900 | 0.0001 | - |
| 1.2305 | 14950 | 0.0026 | - |
| 1.2346 | 15000 | 0.0 | - |
| 1.2387 | 15050 | 0.0 | - |
| 1.2428 | 15100 | 0.0001 | - |
| 1.2469 | 15150 | 0.0847 | - |
| 1.2510 | 15200 | 0.0 | - |
| 1.2551 | 15250 | 0.0475 | - |
| 1.2593 | 15300 | 0.0006 | - |
| 1.2634 | 15350 | 0.0001 | - |
| 1.2675 | 15400 | 0.0 | - |
| 1.2716 | 15450 | 0.0 | - |
| 1.2757 | 15500 | 0.0002 | - |
| 1.2798 | 15550 | 0.0001 | - |
| 1.2840 | 15600 | 0.0 | - |
| 1.2881 | 15650 | 0.0 | - |
| 1.2922 | 15700 | 0.0 | - |
| 1.2963 | 15750 | 0.0 | - |
| 1.3004 | 15800 | 0.0 | - |
| 1.3045 | 15850 | 0.0004 | - |
| 1.3086 | 15900 | 0.0 | - |
| 1.3128 | 15950 | 0.0 | - |
| 1.3169 | 16000 | 0.0 | - |
| 1.3210 | 16050 | 0.0 | - |
| 1.3251 | 16100 | 0.0 | - |
| 1.3292 | 16150 | 0.0 | - |
| 1.3333 | 16200 | 0.0 | - |
| 1.3374 | 16250 | 0.0 | - |
| 1.3416 | 16300 | 0.0 | - |
| 1.3457 | 16350 | 0.0 | - |
| 1.3498 | 16400 | 0.0 | - |
| 1.3539 | 16450 | 0.0 | - |
| 1.3580 | 16500 | 0.0 | - |
| 1.3621 | 16550 | 0.0 | - |
| 1.3663 | 16600 | 0.0 | - |
| 1.3704 | 16650 | 0.0001 | - |
| 1.3745 | 16700 | 0.0 | - |
| 1.3786 | 16750 | 0.0 | - |
| 1.3827 | 16800 | 0.0 | - |
| 1.3868 | 16850 | 0.0001 | - |
| 1.3909 | 16900 | 0.0 | - |
| 1.3951 | 16950 | 0.0 | - |
| 1.3992 | 17000 | 0.0 | - |
| 1.4033 | 17050 | 0.0 | - |
| 1.4074 | 17100 | 0.0 | - |
| 1.4115 | 17150 | 0.0 | - |
| 1.4156 | 17200 | 0.0 | - |
| 1.4198 | 17250 | 0.0 | - |
| 1.4239 | 17300 | 0.0 | - |
| 1.4280 | 17350 | 0.0 | - |
| 1.4321 | 17400 | 0.0 | - |
| 1.4362 | 17450 | 0.0 | - |
| 1.4403 | 17500 | 0.0 | - |
| 1.4444 | 17550 | 0.0 | - |
| 1.4486 | 17600 | 0.0 | - |
| 1.4527 | 17650 | 0.0 | - |
| 1.4568 | 17700 | 0.0 | - |
| 1.4609 | 17750 | 0.0 | - |
| 1.4650 | 17800 | 0.0 | - |
| 1.4691 | 17850 | 0.0 | - |
| 1.4733 | 17900 | 0.0 | - |
| 1.4774 | 17950 | 0.0002 | - |
| 1.4815 | 18000 | 0.0 | - |
| 1.4856 | 18050 | 0.0 | - |
| 1.4897 | 18100 | 0.0 | - |
| 1.4938 | 18150 | 0.0 | - |
| 1.4979 | 18200 | 0.0 | - |
| 1.5021 | 18250 | 0.0 | - |
| 1.5062 | 18300 | 0.0 | - |
| 1.5103 | 18350 | 0.0 | - |
| 1.5144 | 18400 | 0.0 | - |
| 1.5185 | 18450 | 0.0 | - |
| 1.5226 | 18500 | 0.0 | - |
| 1.5267 | 18550 | 0.0 | - |
| 1.5309 | 18600 | 0.0 | - |
| 1.5350 | 18650 | 0.0 | - |
| 1.5391 | 18700 | 0.0 | - |
| 1.5432 | 18750 | 0.0 | - |
| 1.5473 | 18800 | 0.0 | - |
| 1.5514 | 18850 | 0.0 | - |
| 1.5556 | 18900 | 0.0 | - |
| 1.5597 | 18950 | 0.0 | - |
| 1.5638 | 19000 | 0.0 | - |
| 1.5679 | 19050 | 0.0 | - |
| 1.5720 | 19100 | 0.0 | - |
| 1.5761 | 19150 | 0.0 | - |
| 1.5802 | 19200 | 0.0 | - |
| 1.5844 | 19250 | 0.0 | - |
| 1.5885 | 19300 | 0.0 | - |
| 1.5926 | 19350 | 0.0 | - |
| 1.5967 | 19400 | 0.0 | - |
| 1.6008 | 19450 | 0.0 | - |
| 1.6049 | 19500 | 0.0 | - |
| 1.6091 | 19550 | 0.0 | - |
| 1.6132 | 19600 | 0.0 | - |
| 1.6173 | 19650 | 0.0022 | - |
| 1.6214 | 19700 | 0.0981 | - |
| 1.6255 | 19750 | 0.0 | - |
| 1.6296 | 19800 | 0.0006 | - |
| 1.6337 | 19850 | 0.0 | - |
| 1.6379 | 19900 | 0.0 | - |
| 1.6420 | 19950 | 0.0 | - |
| 1.6461 | 20000 | 0.0 | - |
| 1.6502 | 20050 | 0.0 | - |
| 1.6543 | 20100 | 0.0 | - |
| 1.6584 | 20150 | 0.0 | - |
| 1.6626 | 20200 | 0.0 | - |
| 1.6667 | 20250 | 0.0173 | - |
| 1.6708 | 20300 | 0.0001 | - |
| 1.6749 | 20350 | 0.0 | - |
| 1.6790 | 20400 | 0.0 | - |
| 1.6831 | 20450 | 0.0001 | - |
| 1.6872 | 20500 | 0.0 | - |
| 1.6914 | 20550 | 0.0 | - |
| 1.6955 | 20600 | 0.0 | - |
| 1.6996 | 20650 | 0.0 | - |
| 1.7037 | 20700 | 0.0 | - |
| 1.7078 | 20750 | 0.0001 | - |
| 1.7119 | 20800 | 0.0 | - |
| 1.7160 | 20850 | 0.0 | - |
| 1.7202 | 20900 | 0.0 | - |
| 1.7243 | 20950 | 0.0 | - |
| 1.7284 | 21000 | 0.0 | - |
| 1.7325 | 21050 | 0.0 | - |
| 1.7366 | 21100 | 0.0 | - |
| 1.7407 | 21150 | 0.0 | - |
| 1.7449 | 21200 | 0.0 | - |
| 1.7490 | 21250 | 0.0 | - |
| 1.7531 | 21300 | 0.0 | - |
| 1.7572 | 21350 | 0.0 | - |
| 1.7613 | 21400 | 0.0 | - |
| 1.7654 | 21450 | 0.0 | - |
| 1.7695 | 21500 | 0.0 | - |
| 1.7737 | 21550 | 0.0 | - |
| 1.7778 | 21600 | 0.0 | - |
| 1.7819 | 21650 | 0.0 | - |
| 1.7860 | 21700 | 0.0 | - |
| 1.7901 | 21750 | 0.0 | - |
| 1.7942 | 21800 | 0.0 | - |
| 1.7984 | 21850 | 0.0 | - |
| 1.8025 | 21900 | 0.0 | - |
| 1.8066 | 21950 | 0.0 | - |
| 1.8107 | 22000 | 0.0 | - |
| 1.8148 | 22050 | 0.0 | - |
| 1.8189 | 22100 | 0.0 | - |
| 1.8230 | 22150 | 0.0 | - |
| 1.8272 | 22200 | 0.0 | - |
| 1.8313 | 22250 | 0.0 | - |
| 1.8354 | 22300 | 0.0 | - |
| 1.8395 | 22350 | 0.0 | - |
| 1.8436 | 22400 | 0.0 | - |
| 1.8477 | 22450 | 0.0 | - |
| 1.8519 | 22500 | 0.0 | - |
| 1.8560 | 22550 | 0.0 | - |
| 1.8601 | 22600 | 0.0 | - |
| 1.8642 | 22650 | 0.0 | - |
| 1.8683 | 22700 | 0.0 | - |
| 1.8724 | 22750 | 0.0 | - |
| 1.8765 | 22800 | 0.0 | - |
| 1.8807 | 22850 | 0.0 | - |
| 1.8848 | 22900 | 0.0 | - |
| 1.8889 | 22950 | 0.0 | - |
| 1.8930 | 23000 | 0.0 | - |
| 1.8971 | 23050 | 0.0007 | - |
| 1.9012 | 23100 | 0.0 | - |
| 1.9053 | 23150 | 0.0 | - |
| 1.9095 | 23200 | 0.0 | - |
| 1.9136 | 23250 | 0.0 | - |
| 1.9177 | 23300 | 0.0 | - |
| 1.9218 | 23350 | 0.0 | - |
| 1.9259 | 23400 | 0.0 | - |
| 1.9300 | 23450 | 0.0 | - |
| 1.9342 | 23500 | 0.0 | - |
| 1.9383 | 23550 | 0.0 | - |
| 1.9424 | 23600 | 0.0 | - |
| 1.9465 | 23650 | 0.0 | - |
| 1.9506 | 23700 | 0.0 | - |
| 1.9547 | 23750 | 0.0 | - |
| 1.9588 | 23800 | 0.0 | - |
| 1.9630 | 23850 | 0.0 | - |
| 1.9671 | 23900 | 0.0 | - |
| 1.9712 | 23950 | 0.0 | - |
| 1.9753 | 24000 | 0.0 | - |
| 1.9794 | 24050 | 0.0 | - |
| 1.9835 | 24100 | 0.0 | - |
| 1.9877 | 24150 | 0.0 | - |
| 1.9918 | 24200 | 0.0 | - |
| 1.9959 | 24250 | 0.0 | - |
| 2.0 | 24300 | 0.0 | - |
| 2.0041 | 24350 | 0.0001 | - |
| 2.0082 | 24400 | 0.0 | - |
| 2.0123 | 24450 | 0.0004 | - |
| 2.0165 | 24500 | 0.0 | - |
| 2.0206 | 24550 | 0.0001 | - |
| 2.0247 | 24600 | 0.0 | - |
| 2.0288 | 24650 | 0.0 | - |
| 2.0329 | 24700 | 0.0 | - |
| 2.0370 | 24750 | 0.0 | - |
| 2.0412 | 24800 | 0.0 | - |
| 2.0453 | 24850 | 0.0 | - |
| 2.0494 | 24900 | 0.0 | - |
| 2.0535 | 24950 | 0.0 | - |
| 2.0576 | 25000 | 0.0 | - |
| 2.0617 | 25050 | 0.0 | - |
| 2.0658 | 25100 | 0.0 | - |
| 2.0700 | 25150 | 0.0 | - |
| 2.0741 | 25200 | 0.0 | - |
| 2.0782 | 25250 | 0.0 | - |
| 2.0823 | 25300 | 0.0 | - |
| 2.0864 | 25350 | 0.0 | - |
| 2.0905 | 25400 | 0.0 | - |
| 2.0947 | 25450 | 0.0 | - |
| 2.0988 | 25500 | 0.0 | - |
| 2.1029 | 25550 | 0.0 | - |
| 2.1070 | 25600 | 0.0 | - |
| 2.1111 | 25650 | 0.0 | - |
| 2.1152 | 25700 | 0.0 | - |
| 2.1193 | 25750 | 0.0 | - |
| 2.1235 | 25800 | 0.0 | - |
| 2.1276 | 25850 | 0.0 | - |
| 2.1317 | 25900 | 0.0 | - |
| 2.1358 | 25950 | 0.0 | - |
| 2.1399 | 26000 | 0.0 | - |
| 2.1440 | 26050 | 0.0 | - |
| 2.1481 | 26100 | 0.0 | - |
| 2.1523 | 26150 | 0.0 | - |
| 2.1564 | 26200 | 0.0 | - |
| 2.1605 | 26250 | 0.0 | - |
| 2.1646 | 26300 | 0.0 | - |
| 2.1687 | 26350 | 0.0 | - |
| 2.1728 | 26400 | 0.0 | - |
| 2.1770 | 26450 | 0.0 | - |
| 2.1811 | 26500 | 0.0 | - |
| 2.1852 | 26550 | 0.0 | - |
| 2.1893 | 26600 | 0.0 | - |
| 2.1934 | 26650 | 0.0 | - |
| 2.1975 | 26700 | 0.0 | - |
| 2.2016 | 26750 | 0.0 | - |
| 2.2058 | 26800 | 0.0 | - |
| 2.2099 | 26850 | 0.0 | - |
| 2.2140 | 26900 | 0.0 | - |
| 2.2181 | 26950 | 0.0 | - |
| 2.2222 | 27000 | 0.0 | - |
| 2.2263 | 27050 | 0.0 | - |
| 2.2305 | 27100 | 0.0 | - |
| 2.2346 | 27150 | 0.0 | - |
| 2.2387 | 27200 | 0.0 | - |
| 2.2428 | 27250 | 0.0 | - |
| 2.2469 | 27300 | 0.0 | - |
| 2.2510 | 27350 | 0.0 | - |
| 2.2551 | 27400 | 0.0 | - |
| 2.2593 | 27450 | 0.0 | - |
| 2.2634 | 27500 | 0.0 | - |
| 2.2675 | 27550 | 0.0 | - |
| 2.2716 | 27600 | 0.0 | - |
| 2.2757 | 27650 | 0.0 | - |
| 2.2798 | 27700 | 0.0 | - |
| 2.2840 | 27750 | 0.0 | - |
| 2.2881 | 27800 | 0.0 | - |
| 2.2922 | 27850 | 0.0 | - |
| 2.2963 | 27900 | 0.0 | - |
| 2.3004 | 27950 | 0.0 | - |
| 2.3045 | 28000 | 0.0 | - |
| 2.3086 | 28050 | 0.0 | - |
| 2.3128 | 28100 | 0.0 | - |
| 2.3169 | 28150 | 0.0 | - |
| 2.3210 | 28200 | 0.0 | - |
| 2.3251 | 28250 | 0.0 | - |
| 2.3292 | 28300 | 0.0 | - |
| 2.3333 | 28350 | 0.0 | - |
| 2.3374 | 28400 | 0.0 | - |
| 2.3416 | 28450 | 0.0 | - |
| 2.3457 | 28500 | 0.0 | - |
| 2.3498 | 28550 | 0.0 | - |
| 2.3539 | 28600 | 0.0 | - |
| 2.3580 | 28650 | 0.0 | - |
| 2.3621 | 28700 | 0.0 | - |
| 2.3663 | 28750 | 0.0 | - |
| 2.3704 | 28800 | 0.0 | - |
| 2.3745 | 28850 | 0.0 | - |
| 2.3786 | 28900 | 0.0 | - |
| 2.3827 | 28950 | 0.0 | - |
| 2.3868 | 29000 | 0.0 | - |
| 2.3909 | 29050 | 0.0 | - |
| 2.3951 | 29100 | 0.0 | - |
| 2.3992 | 29150 | 0.0 | - |
| 2.4033 | 29200 | 0.0 | - |
| 2.4074 | 29250 | 0.0 | - |
| 2.4115 | 29300 | 0.0 | - |
| 2.4156 | 29350 | 0.0 | - |
| 2.4198 | 29400 | 0.0 | - |
| 2.4239 | 29450 | 0.0 | - |
| 2.4280 | 29500 | 0.0 | - |
| 2.4321 | 29550 | 0.0 | - |
| 2.4362 | 29600 | 0.0 | - |
| 2.4403 | 29650 | 0.0 | - |
| 2.4444 | 29700 | 0.0 | - |
| 2.4486 | 29750 | 0.0 | - |
| 2.4527 | 29800 | 0.0 | - |
| 2.4568 | 29850 | 0.0 | - |
| 2.4609 | 29900 | 0.0 | - |
| 2.4650 | 29950 | 0.0 | - |
| 2.4691 | 30000 | 0.0 | - |
| 2.4733 | 30050 | 0.0 | - |
| 2.4774 | 30100 | 0.0 | - |
| 2.4815 | 30150 | 0.0 | - |
| 2.4856 | 30200 | 0.0 | - |
| 2.4897 | 30250 | 0.0 | - |
| 2.4938 | 30300 | 0.0 | - |
| 2.4979 | 30350 | 0.0 | - |
| 2.5021 | 30400 | 0.0 | - |
| 2.5062 | 30450 | 0.0 | - |
| 2.5103 | 30500 | 0.0 | - |
| 2.5144 | 30550 | 0.0 | - |
| 2.5185 | 30600 | 0.0 | - |
| 2.5226 | 30650 | 0.0 | - |
| 2.5267 | 30700 | 0.0 | - |
| 2.5309 | 30750 | 0.0 | - |
| 2.5350 | 30800 | 0.0 | - |
| 2.5391 | 30850 | 0.0 | - |
| 2.5432 | 30900 | 0.0 | - |
| 2.5473 | 30950 | 0.0 | - |
| 2.5514 | 31000 | 0.0 | - |
| 2.5556 | 31050 | 0.0 | - |
| 2.5597 | 31100 | 0.0 | - |
| 2.5638 | 31150 | 0.0 | - |
| 2.5679 | 31200 | 0.0 | - |
| 2.5720 | 31250 | 0.0 | - |
| 2.5761 | 31300 | 0.0 | - |
| 2.5802 | 31350 | 0.0 | - |
| 2.5844 | 31400 | 0.0 | - |
| 2.5885 | 31450 | 0.0 | - |
| 2.5926 | 31500 | 0.0 | - |
| 2.5967 | 31550 | 0.0 | - |
| 2.6008 | 31600 | 0.0 | - |
| 2.6049 | 31650 | 0.0 | - |
| 2.6091 | 31700 | 0.0 | - |
| 2.6132 | 31750 | 0.0 | - |
| 2.6173 | 31800 | 0.0 | - |
| 2.6214 | 31850 | 0.0 | - |
| 2.6255 | 31900 | 0.0 | - |
| 2.6296 | 31950 | 0.0 | - |
| 2.6337 | 32000 | 0.0 | - |
| 2.6379 | 32050 | 0.0 | - |
| 2.6420 | 32100 | 0.0 | - |
| 2.6461 | 32150 | 0.0 | - |
| 2.6502 | 32200 | 0.0 | - |
| 2.6543 | 32250 | 0.0 | - |
| 2.6584 | 32300 | 0.0 | - |
| 2.6626 | 32350 | 0.0 | - |
| 2.6667 | 32400 | 0.0 | - |
| 2.6708 | 32450 | 0.0 | - |
| 2.6749 | 32500 | 0.0 | - |
| 2.6790 | 32550 | 0.0 | - |
| 2.6831 | 32600 | 0.0 | - |
| 2.6872 | 32650 | 0.0 | - |
| 2.6914 | 32700 | 0.0 | - |
| 2.6955 | 32750 | 0.0 | - |
| 2.6996 | 32800 | 0.0 | - |
| 2.7037 | 32850 | 0.0 | - |
| 2.7078 | 32900 | 0.0 | - |
| 2.7119 | 32950 | 0.0 | - |
| 2.7160 | 33000 | 0.0 | - |
| 2.7202 | 33050 | 0.0 | - |
| 2.7243 | 33100 | 0.0 | - |
| 2.7284 | 33150 | 0.0 | - |
| 2.7325 | 33200 | 0.0 | - |
| 2.7366 | 33250 | 0.0 | - |
| 2.7407 | 33300 | 0.0 | - |
| 2.7449 | 33350 | 0.0 | - |
| 2.7490 | 33400 | 0.0 | - |
| 2.7531 | 33450 | 0.0 | - |
| 2.7572 | 33500 | 0.0 | - |
| 2.7613 | 33550 | 0.0967 | - |
| 2.7654 | 33600 | 0.0 | - |
| 2.7695 | 33650 | 0.0 | - |
| 2.7737 | 33700 | 0.0 | - |
| 2.7778 | 33750 | 0.0 | - |
| 2.7819 | 33800 | 0.0 | - |
| 2.7860 | 33850 | 0.0 | - |
| 2.7901 | 33900 | 0.0 | - |
| 2.7942 | 33950 | 0.0 | - |
| 2.7984 | 34000 | 0.0 | - |
| 2.8025 | 34050 | 0.0 | - |
| 2.8066 | 34100 | 0.0 | - |
| 2.8107 | 34150 | 0.0 | - |
| 2.8148 | 34200 | 0.0 | - |
| 2.8189 | 34250 | 0.0 | - |
| 2.8230 | 34300 | 0.0 | - |
| 2.8272 | 34350 | 0.0 | - |
| 2.8313 | 34400 | 0.0 | - |
| 2.8354 | 34450 | 0.0 | - |
| 2.8395 | 34500 | 0.0 | - |
| 2.8436 | 34550 | 0.0 | - |
| 2.8477 | 34600 | 0.0 | - |
| 2.8519 | 34650 | 0.0 | - |
| 2.8560 | 34700 | 0.0 | - |
| 2.8601 | 34750 | 0.0 | - |
| 2.8642 | 34800 | 0.0 | - |
| 2.8683 | 34850 | 0.0 | - |
| 2.8724 | 34900 | 0.0 | - |
| 2.8765 | 34950 | 0.0 | - |
| 2.8807 | 35000 | 0.0 | - |
| 2.8848 | 35050 | 0.0 | - |
| 2.8889 | 35100 | 0.0 | - |
| 2.8930 | 35150 | 0.0 | - |
| 2.8971 | 35200 | 0.0 | - |
| 2.9012 | 35250 | 0.0 | - |
| 2.9053 | 35300 | 0.0 | - |
| 2.9095 | 35350 | 0.0 | - |
| 2.9136 | 35400 | 0.0 | - |
| 2.9177 | 35450 | 0.0 | - |
| 2.9218 | 35500 | 0.0 | - |
| 2.9259 | 35550 | 0.0 | - |
| 2.9300 | 35600 | 0.0 | - |
| 2.9342 | 35650 | 0.0 | - |
| 2.9383 | 35700 | 0.0 | - |
| 2.9424 | 35750 | 0.0 | - |
| 2.9465 | 35800 | 0.0 | - |
| 2.9506 | 35850 | 0.0 | - |
| 2.9547 | 35900 | 0.0 | - |
| 2.9588 | 35950 | 0.0012 | - |
| 2.9630 | 36000 | 0.0 | - |
| 2.9671 | 36050 | 0.0 | - |
| 2.9712 | 36100 | 0.0 | - |
| 2.9753 | 36150 | 0.0 | - |
| 2.9794 | 36200 | 0.0 | - |
| 2.9835 | 36250 | 0.0 | - |
| 2.9877 | 36300 | 0.0 | - |
| 2.9918 | 36350 | 0.0 | - |
| 2.9959 | 36400 | 0.0 | - |
| 3.0 | 36450 | 0.0 | - |
| 3.0041 | 36500 | 0.0 | - |
| 3.0082 | 36550 | 0.0 | - |
| 3.0123 | 36600 | 0.0 | - |
| 3.0165 | 36650 | 0.0 | - |
| 3.0206 | 36700 | 0.0 | - |
| 3.0247 | 36750 | 0.0 | - |
| 3.0288 | 36800 | 0.0 | - |
| 3.0329 | 36850 | 0.0 | - |
| 3.0370 | 36900 | 0.0 | - |
| 3.0412 | 36950 | 0.0 | - |
| 3.0453 | 37000 | 0.0 | - |
| 3.0494 | 37050 | 0.0 | - |
| 3.0535 | 37100 | 0.0 | - |
| 3.0576 | 37150 | 0.0 | - |
| 3.0617 | 37200 | 0.0 | - |
| 3.0658 | 37250 | 0.0 | - |
| 3.0700 | 37300 | 0.0 | - |
| 3.0741 | 37350 | 0.0 | - |
| 3.0782 | 37400 | 0.0 | - |
| 3.0823 | 37450 | 0.0 | - |
| 3.0864 | 37500 | 0.0 | - |
| 3.0905 | 37550 | 0.0 | - |
| 3.0947 | 37600 | 0.0 | - |
| 3.0988 | 37650 | 0.0 | - |
| 3.1029 | 37700 | 0.0 | - |
| 3.1070 | 37750 | 0.0 | - |
| 3.1111 | 37800 | 0.0 | - |
| 3.1152 | 37850 | 0.0 | - |
| 3.1193 | 37900 | 0.0 | - |
| 3.1235 | 37950 | 0.0 | - |
| 3.1276 | 38000 | 0.0 | - |
| 3.1317 | 38050 | 0.0 | - |
| 3.1358 | 38100 | 0.0 | - |
| 3.1399 | 38150 | 0.0 | - |
| 3.1440 | 38200 | 0.0 | - |
| 3.1481 | 38250 | 0.0 | - |
| 3.1523 | 38300 | 0.0 | - |
| 3.1564 | 38350 | 0.0 | - |
| 3.1605 | 38400 | 0.0 | - |
| 3.1646 | 38450 | 0.0 | - |
| 3.1687 | 38500 | 0.0 | - |
| 3.1728 | 38550 | 0.0 | - |
| 3.1770 | 38600 | 0.0 | - |
| 3.1811 | 38650 | 0.0 | - |
| 3.1852 | 38700 | 0.0 | - |
| 3.1893 | 38750 | 0.0 | - |
| 3.1934 | 38800 | 0.0 | - |
| 3.1975 | 38850 | 0.0 | - |
| 3.2016 | 38900 | 0.0 | - |
| 3.2058 | 38950 | 0.0 | - |
| 3.2099 | 39000 | 0.0 | - |
| 3.2140 | 39050 | 0.0 | - |
| 3.2181 | 39100 | 0.0 | - |
| 3.2222 | 39150 | 0.0 | - |
| 3.2263 | 39200 | 0.0 | - |
| 3.2305 | 39250 | 0.0 | - |
| 3.2346 | 39300 | 0.0 | - |
| 3.2387 | 39350 | 0.0 | - |
| 3.2428 | 39400 | 0.0 | - |
| 3.2469 | 39450 | 0.0 | - |
| 3.2510 | 39500 | 0.0 | - |
| 3.2551 | 39550 | 0.0 | - |
| 3.2593 | 39600 | 0.0 | - |
| 3.2634 | 39650 | 0.0 | - |
| 3.2675 | 39700 | 0.0 | - |
| 3.2716 | 39750 | 0.0 | - |
| 3.2757 | 39800 | 0.0 | - |
| 3.2798 | 39850 | 0.0 | - |
| 3.2840 | 39900 | 0.0 | - |
| 3.2881 | 39950 | 0.0 | - |
| 3.2922 | 40000 | 0.0 | - |
| 3.2963 | 40050 | 0.0 | - |
| 3.3004 | 40100 | 0.0 | - |
| 3.3045 | 40150 | 0.0 | - |
| 3.3086 | 40200 | 0.0 | - |
| 3.3128 | 40250 | 0.0 | - |
| 3.3169 | 40300 | 0.0 | - |
| 3.3210 | 40350 | 0.0 | - |
| 3.3251 | 40400 | 0.0 | - |
| 3.3292 | 40450 | 0.0 | - |
| 3.3333 | 40500 | 0.0 | - |
| 3.3374 | 40550 | 0.0 | - |
| 3.3416 | 40600 | 0.0 | - |
| 3.3457 | 40650 | 0.0 | - |
| 3.3498 | 40700 | 0.0 | - |
| 3.3539 | 40750 | 0.0 | - |
| 3.3580 | 40800 | 0.0 | - |
| 3.3621 | 40850 | 0.0 | - |
| 3.3663 | 40900 | 0.0002 | - |
| 3.3704 | 40950 | 0.0 | - |
| 3.3745 | 41000 | 0.0 | - |
| 3.3786 | 41050 | 0.0 | - |
| 3.3827 | 41100 | 0.0 | - |
| 3.3868 | 41150 | 0.0 | - |
| 3.3909 | 41200 | 0.0 | - |
| 3.3951 | 41250 | 0.0 | - |
| 3.3992 | 41300 | 0.0 | - |
| 3.4033 | 41350 | 0.0 | - |
| 3.4074 | 41400 | 0.0 | - |
| 3.4115 | 41450 | 0.0 | - |
| 3.4156 | 41500 | 0.0 | - |
| 3.4198 | 41550 | 0.0 | - |
| 3.4239 | 41600 | 0.0 | - |
| 3.4280 | 41650 | 0.0 | - |
| 3.4321 | 41700 | 0.0 | - |
| 3.4362 | 41750 | 0.0 | - |
| 3.4403 | 41800 | 0.0 | - |
| 3.4444 | 41850 | 0.0 | - |
| 3.4486 | 41900 | 0.0 | - |
| 3.4527 | 41950 | 0.0 | - |
| 3.4568 | 42000 | 0.0 | - |
| 3.4609 | 42050 | 0.0 | - |
| 3.4650 | 42100 | 0.0 | - |
| 3.4691 | 42150 | 0.0 | - |
| 3.4733 | 42200 | 0.0 | - |
| 3.4774 | 42250 | 0.0 | - |
| 3.4815 | 42300 | 0.0 | - |
| 3.4856 | 42350 | 0.0 | - |
| 3.4897 | 42400 | 0.0 | - |
| 3.4938 | 42450 | 0.0 | - |
| 3.4979 | 42500 | 0.0 | - |
| 3.5021 | 42550 | 0.0 | - |
| 3.5062 | 42600 | 0.0 | - |
| 3.5103 | 42650 | 0.0 | - |
| 3.5144 | 42700 | 0.0 | - |
| 3.5185 | 42750 | 0.0 | - |
| 3.5226 | 42800 | 0.0 | - |
| 3.5267 | 42850 | 0.0 | - |
| 3.5309 | 42900 | 0.0 | - |
| 3.5350 | 42950 | 0.0 | - |
| 3.5391 | 43000 | 0.0 | - |
| 3.5432 | 43050 | 0.0 | - |
| 3.5473 | 43100 | 0.0 | - |
| 3.5514 | 43150 | 0.0 | - |
| 3.5556 | 43200 | 0.0 | - |
| 3.5597 | 43250 | 0.0 | - |
| 3.5638 | 43300 | 0.0 | - |
| 3.5679 | 43350 | 0.0 | - |
| 3.5720 | 43400 | 0.0 | - |
| 3.5761 | 43450 | 0.0 | - |
| 3.5802 | 43500 | 0.0 | - |
| 3.5844 | 43550 | 0.0 | - |
| 3.5885 | 43600 | 0.0 | - |
| 3.5926 | 43650 | 0.0 | - |
| 3.5967 | 43700 | 0.0 | - |
| 3.6008 | 43750 | 0.0 | - |
| 3.6049 | 43800 | 0.0 | - |
| 3.6091 | 43850 | 0.0 | - |
| 3.6132 | 43900 | 0.0 | - |
| 3.6173 | 43950 | 0.0 | - |
| 3.6214 | 44000 | 0.0 | - |
| 3.6255 | 44050 | 0.0 | - |
| 3.6296 | 44100 | 0.0 | - |
| 3.6337 | 44150 | 0.0 | - |
| 3.6379 | 44200 | 0.0 | - |
| 3.6420 | 44250 | 0.0 | - |
| 3.6461 | 44300 | 0.0 | - |
| 3.6502 | 44350 | 0.0 | - |
| 3.6543 | 44400 | 0.0 | - |
| 3.6584 | 44450 | 0.0 | - |
| 3.6626 | 44500 | 0.0 | - |
| 3.6667 | 44550 | 0.0 | - |
| 3.6708 | 44600 | 0.0 | - |
| 3.6749 | 44650 | 0.0 | - |
| 3.6790 | 44700 | 0.0 | - |
| 3.6831 | 44750 | 0.0 | - |
| 3.6872 | 44800 | 0.0 | - |
| 3.6914 | 44850 | 0.0 | - |
| 3.6955 | 44900 | 0.0 | - |
| 3.6996 | 44950 | 0.0 | - |
| 3.7037 | 45000 | 0.0 | - |
| 3.7078 | 45050 | 0.0 | - |
| 3.7119 | 45100 | 0.0 | - |
| 3.7160 | 45150 | 0.0 | - |
| 3.7202 | 45200 | 0.0 | - |
| 3.7243 | 45250 | 0.0 | - |
| 3.7284 | 45300 | 0.0 | - |
| 3.7325 | 45350 | 0.0 | - |
| 3.7366 | 45400 | 0.0 | - |
| 3.7407 | 45450 | 0.0 | - |
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| 3.7490 | 45550 | 0.0 | - |
| 3.7531 | 45600 | 0.0 | - |
| 3.7572 | 45650 | 0.0 | - |
| 3.7613 | 45700 | 0.0 | - |
| 3.7654 | 45750 | 0.0 | - |
| 3.7695 | 45800 | 0.0 | - |
| 3.7737 | 45850 | 0.0 | - |
| 3.7778 | 45900 | 0.0 | - |
| 3.7819 | 45950 | 0.0 | - |
| 3.7860 | 46000 | 0.0 | - |
| 3.7901 | 46050 | 0.0 | - |
| 3.7942 | 46100 | 0.0 | - |
| 3.7984 | 46150 | 0.0 | - |
| 3.8025 | 46200 | 0.0 | - |
| 3.8066 | 46250 | 0.0 | - |
| 3.8107 | 46300 | 0.0 | - |
| 3.8148 | 46350 | 0.0 | - |
| 3.8189 | 46400 | 0.0 | - |
| 3.8230 | 46450 | 0.0 | - |
| 3.8272 | 46500 | 0.0 | - |
| 3.8313 | 46550 | 0.0 | - |
| 3.8354 | 46600 | 0.0 | - |
| 3.8395 | 46650 | 0.0 | - |
| 3.8436 | 46700 | 0.0 | - |
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| 3.8683 | 47000 | 0.0 | - |
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| 3.8765 | 47100 | 0.0 | - |
| 3.8807 | 47150 | 0.0 | - |
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| 3.9465 | 47950 | 0.0 | - |
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| 3.9588 | 48100 | 0.0 | - |
| 3.9630 | 48150 | 0.0 | - |
| 3.9671 | 48200 | 0.0 | - |
| 3.9712 | 48250 | 0.0 | - |
| 3.9753 | 48300 | 0.0 | - |
| 3.9794 | 48350 | 0.0 | - |
| 3.9835 | 48400 | 0.0 | - |
| 3.9877 | 48450 | 0.0 | - |
| 3.9918 | 48500 | 0.0 | - |
| 3.9959 | 48550 | 0.0 | - |
| 4.0 | 48600 | 0.0 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- 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}
}
```