---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: ketika Leguizamo akhirnya memasang karakter yang menjengkelkan di akhir film.
- text: ini adalah perjalanan yang menawan dan sering kali memberi kesan.
- text: hanya sedikit film yang menangkap dengan sempurna harapan dan impian anak-anak
lelaki di lapangan bisbol serta para lelaki dewasa yang duduk di tribun.
- text: holden caulfield melakukannya dengan lebih baik.
- text: tapi jika diambil sebagai one-shot yang penuh gaya dan energik, ratu terkutuk
ini tidak bisa dikatakan payah.
pipeline_tag: text-classification
inference: true
base_model: firqaaa/indo-sentence-bert-base
model-index:
- name: SetFit with firqaaa/indo-sentence-bert-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8171334431630972
name: Accuracy
---
# SetFit with firqaaa/indo-sentence-bert-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) 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:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:--------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positif | - 'secara implisit mengakui dan merayakan kelicikan dan khayalan diri yang luar biasa dari sebagian besar pebisnis Amerika ini, dan oleh karena itu, dokumen ini mungkin merupakan dokumen Hollywood yang paling jujur \u200b\u200bdan aneh dari semuanya.'
- 'sebuah potret menarik dari para seniman tanpa kompromi yang mencoba menciptakan sesuatu yang orisinal dengan latar belakang industri musik korporat yang tampaknya hanya peduli pada keuntungan.'
- 'mengerikan dalam potret obyektif Amerika abad kedua puluh satu yang suram dan hilang.'
|
| negatif | - 'dengan hari-hari anjing di bulan Agustus yang akan datang, anggaplah film anjing ini setara dengan sinematik dengan kelembapan tinggi.'
- 'itu kelam dan mudah ditebak, dan tidak banyak yang bisa tertawa.'
- 'pencapaian film yang paling mustahil?'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8171 |
## 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("firqaaa/indo-setfit-bert-base-p1")
# Run inference
preds = model("holden caulfield melakukannya dengan lebih baik.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 16.073 | 45 |
| Label | Training Sample Count |
|:--------|:----------------------|
| negatif | 500 |
| positif | 500 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.3943 | - |
| 0.0032 | 50 | 0.3398 | - |
| 0.0064 | 100 | 0.2628 | - |
| 0.0096 | 150 | 0.2842 | - |
| 0.0128 | 200 | 0.2317 | - |
| 0.0160 | 250 | 0.2703 | - |
| 0.0192 | 300 | 0.2272 | - |
| 0.0224 | 350 | 0.2496 | - |
| 0.0255 | 400 | 0.2076 | - |
| 0.0287 | 450 | 0.207 | - |
| 0.0319 | 500 | 0.232 | - |
| 0.0351 | 550 | 0.1439 | - |
| 0.0383 | 600 | 0.1578 | - |
| 0.0415 | 650 | 0.0821 | - |
| 0.0447 | 700 | 0.0628 | - |
| 0.0479 | 750 | 0.0315 | - |
| 0.0511 | 800 | 0.0089 | - |
| 0.0543 | 850 | 0.0106 | - |
| 0.0575 | 900 | 0.0026 | - |
| 0.0607 | 950 | 0.0025 | - |
| 0.0639 | 1000 | 0.0028 | - |
| 0.0671 | 1050 | 0.0093 | - |
| 0.0703 | 1100 | 0.0008 | - |
| 0.0734 | 1150 | 0.0008 | - |
| 0.0766 | 1200 | 0.0003 | - |
| 0.0798 | 1250 | 0.0006 | - |
| 0.0830 | 1300 | 0.0005 | - |
| 0.0862 | 1350 | 0.0005 | - |
| 0.0894 | 1400 | 0.0002 | - |
| 0.0926 | 1450 | 0.0003 | - |
| 0.0958 | 1500 | 0.0003 | - |
| 0.0990 | 1550 | 0.0003 | - |
| 0.1022 | 1600 | 0.0002 | - |
| 0.1054 | 1650 | 0.0002 | - |
| 0.1086 | 1700 | 0.0001 | - |
| 0.1118 | 1750 | 0.0002 | - |
| 0.1150 | 1800 | 0.0001 | - |
| 0.1182 | 1850 | 0.0001 | - |
| 0.1214 | 1900 | 0.0001 | - |
| 0.1245 | 1950 | 0.0001 | - |
| 0.1277 | 2000 | 0.0001 | - |
| 0.1309 | 2050 | 0.0001 | - |
| 0.1341 | 2100 | 0.0001 | - |
| 0.1373 | 2150 | 0.0001 | - |
| 0.1405 | 2200 | 0.0001 | - |
| 0.1437 | 2250 | 0.0001 | - |
| 0.1469 | 2300 | 0.0001 | - |
| 0.1501 | 2350 | 0.0001 | - |
| 0.1533 | 2400 | 0.0001 | - |
| 0.1565 | 2450 | 0.0002 | - |
| 0.1597 | 2500 | 0.0001 | - |
| 0.1629 | 2550 | 0.0001 | - |
| 0.1661 | 2600 | 0.0134 | - |
| 0.1693 | 2650 | 0.0001 | - |
| 0.1724 | 2700 | 0.0001 | - |
| 0.1756 | 2750 | 0.0016 | - |
| 0.1788 | 2800 | 0.0001 | - |
| 0.1820 | 2850 | 0.0001 | - |
| 0.1852 | 2900 | 0.0002 | - |
| 0.1884 | 2950 | 0.0001 | - |
| 0.1916 | 3000 | 0.0066 | - |
| 0.1948 | 3050 | 0.0001 | - |
| 0.1980 | 3100 | 0.0001 | - |
| 0.2012 | 3150 | 0.0005 | - |
| 0.2044 | 3200 | 0.0001 | - |
| 0.2076 | 3250 | 0.0001 | - |
| 0.2108 | 3300 | 0.0001 | - |
| 0.2140 | 3350 | 0.0001 | - |
| 0.2172 | 3400 | 0.0001 | - |
| 0.2203 | 3450 | 0.0 | - |
| 0.2235 | 3500 | 0.0001 | - |
| 0.2267 | 3550 | 0.0 | - |
| 0.2299 | 3600 | 0.0 | - |
| 0.2331 | 3650 | 0.021 | - |
| 0.2363 | 3700 | 0.0001 | - |
| 0.2395 | 3750 | 0.0 | - |
| 0.2427 | 3800 | 0.0 | - |
| 0.2459 | 3850 | 0.0 | - |
| 0.2491 | 3900 | 0.0 | - |
| 0.2523 | 3950 | 0.0 | - |
| 0.2555 | 4000 | 0.0 | - |
| 0.2587 | 4050 | 0.0001 | - |
| 0.2619 | 4100 | 0.0 | - |
| 0.2651 | 4150 | 0.0 | - |
| 0.2683 | 4200 | 0.0016 | - |
| 0.2714 | 4250 | 0.0 | - |
| 0.2746 | 4300 | 0.001 | - |
| 0.2778 | 4350 | 0.0001 | - |
| 0.2810 | 4400 | 0.0002 | - |
| 0.2842 | 4450 | 0.0 | - |
| 0.2874 | 4500 | 0.0001 | - |
| 0.2906 | 4550 | 0.0001 | - |
| 0.2938 | 4600 | 0.0002 | - |
| 0.2970 | 4650 | 0.0 | - |
| 0.3002 | 4700 | 0.0305 | - |
| 0.3034 | 4750 | 0.0 | - |
| 0.3066 | 4800 | 0.0 | - |
| 0.3098 | 4850 | 0.0 | - |
| 0.3130 | 4900 | 0.0 | - |
| 0.3162 | 4950 | 0.0 | - |
| 0.3193 | 5000 | 0.0 | - |
| 0.3225 | 5050 | 0.0 | - |
| 0.3257 | 5100 | 0.0 | - |
| 0.3289 | 5150 | 0.0 | - |
| 0.3321 | 5200 | 0.0 | - |
| 0.3353 | 5250 | 0.0 | - |
| 0.3385 | 5300 | 0.0 | - |
| 0.3417 | 5350 | 0.0 | - |
| 0.3449 | 5400 | 0.0 | - |
| 0.3481 | 5450 | 0.0 | - |
| 0.3513 | 5500 | 0.0 | - |
| 0.3545 | 5550 | 0.0 | - |
| 0.3577 | 5600 | 0.0 | - |
| 0.3609 | 5650 | 0.0 | - |
| 0.3641 | 5700 | 0.0 | - |
| 0.3672 | 5750 | 0.0 | - |
| 0.3704 | 5800 | 0.0 | - |
| 0.3736 | 5850 | 0.0001 | - |
| 0.3768 | 5900 | 0.0 | - |
| 0.3800 | 5950 | 0.0 | - |
| 0.3832 | 6000 | 0.0 | - |
| 0.3864 | 6050 | 0.0 | - |
| 0.3896 | 6100 | 0.0 | - |
| 0.3928 | 6150 | 0.0001 | - |
| 0.3960 | 6200 | 0.0002 | - |
| 0.3992 | 6250 | 0.0 | - |
| 0.4024 | 6300 | 0.0 | - |
| 0.4056 | 6350 | 0.0 | - |
| 0.4088 | 6400 | 0.0 | - |
| 0.4120 | 6450 | 0.0 | - |
| 0.4151 | 6500 | 0.0 | - |
| 0.4183 | 6550 | 0.0 | - |
| 0.4215 | 6600 | 0.0 | - |
| 0.4247 | 6650 | 0.0 | - |
| 0.4279 | 6700 | 0.0 | - |
| 0.4311 | 6750 | 0.0 | - |
| 0.4343 | 6800 | 0.0 | - |
| 0.4375 | 6850 | 0.0 | - |
| 0.4407 | 6900 | 0.0 | - |
| 0.4439 | 6950 | 0.0 | - |
| 0.4471 | 7000 | 0.0 | - |
| 0.4503 | 7050 | 0.0 | - |
| 0.4535 | 7100 | 0.0 | - |
| 0.4567 | 7150 | 0.0 | - |
| 0.4599 | 7200 | 0.0 | - |
| 0.4631 | 7250 | 0.0 | - |
| 0.4662 | 7300 | 0.0 | - |
| 0.4694 | 7350 | 0.0 | - |
| 0.4726 | 7400 | 0.0 | - |
| 0.4758 | 7450 | 0.0 | - |
| 0.4790 | 7500 | 0.0 | - |
| 0.4822 | 7550 | 0.0 | - |
| 0.4854 | 7600 | 0.0 | - |
| 0.4886 | 7650 | 0.0 | - |
| 0.4918 | 7700 | 0.0 | - |
| 0.4950 | 7750 | 0.0 | - |
| 0.4982 | 7800 | 0.0 | - |
| 0.5014 | 7850 | 0.0 | - |
| 0.5046 | 7900 | 0.0 | - |
| 0.5078 | 7950 | 0.0 | - |
| 0.5110 | 8000 | 0.0 | - |
| 0.5141 | 8050 | 0.0 | - |
| 0.5173 | 8100 | 0.0 | - |
| 0.5205 | 8150 | 0.0 | - |
| 0.5237 | 8200 | 0.0 | - |
| 0.5269 | 8250 | 0.0 | - |
| 0.5301 | 8300 | 0.0 | - |
| 0.5333 | 8350 | 0.0 | - |
| 0.5365 | 8400 | 0.0 | - |
| 0.5397 | 8450 | 0.0 | - |
| 0.5429 | 8500 | 0.0 | - |
| 0.5461 | 8550 | 0.0 | - |
| 0.5493 | 8600 | 0.0 | - |
| 0.5525 | 8650 | 0.0 | - |
| 0.5557 | 8700 | 0.0 | - |
| 0.5589 | 8750 | 0.0 | - |
| 0.5620 | 8800 | 0.0 | - |
| 0.5652 | 8850 | 0.0 | - |
| 0.5684 | 8900 | 0.0 | - |
| 0.5716 | 8950 | 0.0 | - |
| 0.5748 | 9000 | 0.0 | - |
| 0.5780 | 9050 | 0.0 | - |
| 0.5812 | 9100 | 0.0 | - |
| 0.5844 | 9150 | 0.0 | - |
| 0.5876 | 9200 | 0.0 | - |
| 0.5908 | 9250 | 0.0 | - |
| 0.5940 | 9300 | 0.0 | - |
| 0.5972 | 9350 | 0.0 | - |
| 0.6004 | 9400 | 0.0 | - |
| 0.6036 | 9450 | 0.0 | - |
| 0.6068 | 9500 | 0.0 | - |
| 0.6100 | 9550 | 0.0 | - |
| 0.6131 | 9600 | 0.0 | - |
| 0.6163 | 9650 | 0.0 | - |
| 0.6195 | 9700 | 0.0 | - |
| 0.6227 | 9750 | 0.0 | - |
| 0.6259 | 9800 | 0.0 | - |
| 0.6291 | 9850 | 0.0 | - |
| 0.6323 | 9900 | 0.0 | - |
| 0.6355 | 9950 | 0.0 | - |
| 0.6387 | 10000 | 0.0 | - |
| 0.6419 | 10050 | 0.0 | - |
| 0.6451 | 10100 | 0.0 | - |
| 0.6483 | 10150 | 0.0 | - |
| 0.6515 | 10200 | 0.0 | - |
| 0.6547 | 10250 | 0.0 | - |
| 0.6579 | 10300 | 0.0 | - |
| 0.6610 | 10350 | 0.0 | - |
| 0.6642 | 10400 | 0.0 | - |
| 0.6674 | 10450 | 0.0 | - |
| 0.6706 | 10500 | 0.0 | - |
| 0.6738 | 10550 | 0.0 | - |
| 0.6770 | 10600 | 0.0 | - |
| 0.6802 | 10650 | 0.0 | - |
| 0.6834 | 10700 | 0.0 | - |
| 0.6866 | 10750 | 0.0 | - |
| 0.6898 | 10800 | 0.0 | - |
| 0.6930 | 10850 | 0.0 | - |
| 0.6962 | 10900 | 0.0 | - |
| 0.6994 | 10950 | 0.0 | - |
| 0.7026 | 11000 | 0.0 | - |
| 0.7058 | 11050 | 0.0 | - |
| 0.7089 | 11100 | 0.0 | - |
| 0.7121 | 11150 | 0.0 | - |
| 0.7153 | 11200 | 0.0 | - |
| 0.7185 | 11250 | 0.0 | - |
| 0.7217 | 11300 | 0.0 | - |
| 0.7249 | 11350 | 0.0 | - |
| 0.7281 | 11400 | 0.0 | - |
| 0.7313 | 11450 | 0.0 | - |
| 0.7345 | 11500 | 0.0 | - |
| 0.7377 | 11550 | 0.0 | - |
| 0.7409 | 11600 | 0.0 | - |
| 0.7441 | 11650 | 0.0 | - |
| 0.7473 | 11700 | 0.0 | - |
| 0.7505 | 11750 | 0.0 | - |
| 0.7537 | 11800 | 0.0 | - |
| 0.7568 | 11850 | 0.0 | - |
| 0.7600 | 11900 | 0.0 | - |
| 0.7632 | 11950 | 0.0 | - |
| 0.7664 | 12000 | 0.0 | - |
| 0.7696 | 12050 | 0.0 | - |
| 0.7728 | 12100 | 0.0 | - |
| 0.7760 | 12150 | 0.0 | - |
| 0.7792 | 12200 | 0.0 | - |
| 0.7824 | 12250 | 0.0 | - |
| 0.7856 | 12300 | 0.0 | - |
| 0.7888 | 12350 | 0.0 | - |
| 0.7920 | 12400 | 0.0 | - |
| 0.7952 | 12450 | 0.0 | - |
| 0.7984 | 12500 | 0.0 | - |
| 0.8016 | 12550 | 0.0 | - |
| 0.8048 | 12600 | 0.0 | - |
| 0.8079 | 12650 | 0.0 | - |
| 0.8111 | 12700 | 0.0 | - |
| 0.8143 | 12750 | 0.0 | - |
| 0.8175 | 12800 | 0.0 | - |
| 0.8207 | 12850 | 0.0 | - |
| 0.8239 | 12900 | 0.0 | - |
| 0.8271 | 12950 | 0.0 | - |
| 0.8303 | 13000 | 0.0 | - |
| 0.8335 | 13050 | 0.0 | - |
| 0.8367 | 13100 | 0.0 | - |
| 0.8399 | 13150 | 0.0 | - |
| 0.8431 | 13200 | 0.0 | - |
| 0.8463 | 13250 | 0.0 | - |
| 0.8495 | 13300 | 0.0 | - |
| 0.8527 | 13350 | 0.0 | - |
| 0.8558 | 13400 | 0.0 | - |
| 0.8590 | 13450 | 0.0 | - |
| 0.8622 | 13500 | 0.0 | - |
| 0.8654 | 13550 | 0.0 | - |
| 0.8686 | 13600 | 0.0 | - |
| 0.8718 | 13650 | 0.0 | - |
| 0.8750 | 13700 | 0.0 | - |
| 0.8782 | 13750 | 0.0 | - |
| 0.8814 | 13800 | 0.0 | - |
| 0.8846 | 13850 | 0.0 | - |
| 0.8878 | 13900 | 0.0 | - |
| 0.8910 | 13950 | 0.0 | - |
| 0.8942 | 14000 | 0.0 | - |
| 0.8974 | 14050 | 0.0 | - |
| 0.9006 | 14100 | 0.0 | - |
| 0.9037 | 14150 | 0.0 | - |
| 0.9069 | 14200 | 0.0 | - |
| 0.9101 | 14250 | 0.0 | - |
| 0.9133 | 14300 | 0.0 | - |
| 0.9165 | 14350 | 0.0 | - |
| 0.9197 | 14400 | 0.0 | - |
| 0.9229 | 14450 | 0.0 | - |
| 0.9261 | 14500 | 0.0 | - |
| 0.9293 | 14550 | 0.0 | - |
| 0.9325 | 14600 | 0.0 | - |
| 0.9357 | 14650 | 0.0 | - |
| 0.9389 | 14700 | 0.0 | - |
| 0.9421 | 14750 | 0.0 | - |
| 0.9453 | 14800 | 0.0 | - |
| 0.9485 | 14850 | 0.0 | - |
| 0.9517 | 14900 | 0.0 | - |
| 0.9548 | 14950 | 0.0 | - |
| 0.9580 | 15000 | 0.0 | - |
| 0.9612 | 15050 | 0.0 | - |
| 0.9644 | 15100 | 0.0 | - |
| 0.9676 | 15150 | 0.0 | - |
| 0.9708 | 15200 | 0.0 | - |
| 0.9740 | 15250 | 0.0 | - |
| 0.9772 | 15300 | 0.0 | - |
| 0.9804 | 15350 | 0.0 | - |
| 0.9836 | 15400 | 0.0 | - |
| 0.9868 | 15450 | 0.0 | - |
| 0.9900 | 15500 | 0.0 | - |
| 0.9932 | 15550 | 0.0 | - |
| 0.9964 | 15600 | 0.0 | - |
| 0.9996 | 15650 | 0.0 | - |
| 1.0 | 15657 | - | 0.2641 |
| 1.0027 | 15700 | 0.0 | - |
| 1.0059 | 15750 | 0.0 | - |
| 1.0091 | 15800 | 0.0 | - |
| 1.0123 | 15850 | 0.0 | - |
| 1.0155 | 15900 | 0.0 | - |
| 1.0187 | 15950 | 0.0 | - |
| 1.0219 | 16000 | 0.0 | - |
| 1.0251 | 16050 | 0.0 | - |
| 1.0283 | 16100 | 0.0 | - |
| 1.0315 | 16150 | 0.0 | - |
| 1.0347 | 16200 | 0.0 | - |
| 1.0379 | 16250 | 0.0 | - |
| 1.0411 | 16300 | 0.0 | - |
| 1.0443 | 16350 | 0.0 | - |
| 1.0475 | 16400 | 0.0 | - |
| 1.0506 | 16450 | 0.0 | - |
| 1.0538 | 16500 | 0.0 | - |
| 1.0570 | 16550 | 0.0 | - |
| 1.0602 | 16600 | 0.0 | - |
| 1.0634 | 16650 | 0.0 | - |
| 1.0666 | 16700 | 0.0 | - |
| 1.0698 | 16750 | 0.0 | - |
| 1.0730 | 16800 | 0.0 | - |
| 1.0762 | 16850 | 0.0 | - |
| 1.0794 | 16900 | 0.0 | - |
| 1.0826 | 16950 | 0.0 | - |
| 1.0858 | 17000 | 0.0 | - |
| 1.0890 | 17050 | 0.0 | - |
| 1.0922 | 17100 | 0.0 | - |
| 1.0954 | 17150 | 0.0 | - |
| 1.0986 | 17200 | 0.0 | - |
| 1.1017 | 17250 | 0.0 | - |
| 1.1049 | 17300 | 0.0 | - |
| 1.1081 | 17350 | 0.0 | - |
| 1.1113 | 17400 | 0.0 | - |
| 1.1145 | 17450 | 0.0 | - |
| 1.1177 | 17500 | 0.0 | - |
| 1.1209 | 17550 | 0.0 | - |
| 1.1241 | 17600 | 0.0 | - |
| 1.1273 | 17650 | 0.0 | - |
| 1.1305 | 17700 | 0.0 | - |
| 1.1337 | 17750 | 0.0 | - |
| 1.1369 | 17800 | 0.0 | - |
| 1.1401 | 17850 | 0.0 | - |
| 1.1433 | 17900 | 0.0 | - |
| 1.1465 | 17950 | 0.0 | - |
| 1.1496 | 18000 | 0.0 | - |
| 1.1528 | 18050 | 0.0 | - |
| 1.1560 | 18100 | 0.0 | - |
| 1.1592 | 18150 | 0.0 | - |
| 1.1624 | 18200 | 0.0 | - |
| 1.1656 | 18250 | 0.0 | - |
| 1.1688 | 18300 | 0.0 | - |
| 1.1720 | 18350 | 0.0 | - |
| 1.1752 | 18400 | 0.0 | - |
| 1.1784 | 18450 | 0.0 | - |
| 1.1816 | 18500 | 0.0 | - |
| 1.1848 | 18550 | 0.0 | - |
| 1.1880 | 18600 | 0.0 | - |
| 1.1912 | 18650 | 0.0 | - |
| 1.1944 | 18700 | 0.0 | - |
| 1.1975 | 18750 | 0.0 | - |
| 1.2007 | 18800 | 0.0 | - |
| 1.2039 | 18850 | 0.0 | - |
| 1.2071 | 18900 | 0.0 | - |
| 1.2103 | 18950 | 0.0 | - |
| 1.2135 | 19000 | 0.0 | - |
| 1.2167 | 19050 | 0.0 | - |
| 1.2199 | 19100 | 0.0 | - |
| 1.2231 | 19150 | 0.0 | - |
| 1.2263 | 19200 | 0.0 | - |
| 1.2295 | 19250 | 0.0 | - |
| 1.2327 | 19300 | 0.0 | - |
| 1.2359 | 19350 | 0.0 | - |
| 1.2391 | 19400 | 0.0 | - |
| 1.2423 | 19450 | 0.0 | - |
| 1.2454 | 19500 | 0.0 | - |
| 1.2486 | 19550 | 0.0 | - |
| 1.2518 | 19600 | 0.0 | - |
| 1.2550 | 19650 | 0.0 | - |
| 1.2582 | 19700 | 0.0 | - |
| 1.2614 | 19750 | 0.0 | - |
| 1.2646 | 19800 | 0.0 | - |
| 1.2678 | 19850 | 0.0 | - |
| 1.2710 | 19900 | 0.0 | - |
| 1.2742 | 19950 | 0.0 | - |
| 1.2774 | 20000 | 0.0 | - |
| 1.2806 | 20050 | 0.0 | - |
| 1.2838 | 20100 | 0.0 | - |
| 1.2870 | 20150 | 0.0 | - |
| 1.2902 | 20200 | 0.0 | - |
| 1.2934 | 20250 | 0.0 | - |
| 1.2965 | 20300 | 0.0 | - |
| 1.2997 | 20350 | 0.0 | - |
| 1.3029 | 20400 | 0.0 | - |
| 1.3061 | 20450 | 0.0 | - |
| 1.3093 | 20500 | 0.0 | - |
| 1.3125 | 20550 | 0.0 | - |
| 1.3157 | 20600 | 0.0 | - |
| 1.3189 | 20650 | 0.0 | - |
| 1.3221 | 20700 | 0.0 | - |
| 1.3253 | 20750 | 0.0 | - |
| 1.3285 | 20800 | 0.0 | - |
| 1.3317 | 20850 | 0.0 | - |
| 1.3349 | 20900 | 0.0 | - |
| 1.3381 | 20950 | 0.0 | - |
| 1.3413 | 21000 | 0.0 | - |
| 1.3444 | 21050 | 0.0 | - |
| 1.3476 | 21100 | 0.0 | - |
| 1.3508 | 21150 | 0.0 | - |
| 1.3540 | 21200 | 0.0 | - |
| 1.3572 | 21250 | 0.0 | - |
| 1.3604 | 21300 | 0.0 | - |
| 1.3636 | 21350 | 0.0 | - |
| 1.3668 | 21400 | 0.0 | - |
| 1.3700 | 21450 | 0.0 | - |
| 1.3732 | 21500 | 0.0 | - |
| 1.3764 | 21550 | 0.0 | - |
| 1.3796 | 21600 | 0.0 | - |
| 1.3828 | 21650 | 0.0 | - |
| 1.3860 | 21700 | 0.0 | - |
| 1.3892 | 21750 | 0.0 | - |
| 1.3923 | 21800 | 0.0 | - |
| 1.3955 | 21850 | 0.0 | - |
| 1.3987 | 21900 | 0.0 | - |
| 1.4019 | 21950 | 0.0 | - |
| 1.4051 | 22000 | 0.0 | - |
| 1.4083 | 22050 | 0.0 | - |
| 1.4115 | 22100 | 0.0 | - |
| 1.4147 | 22150 | 0.0 | - |
| 1.4179 | 22200 | 0.0 | - |
| 1.4211 | 22250 | 0.0 | - |
| 1.4243 | 22300 | 0.0 | - |
| 1.4275 | 22350 | 0.0 | - |
| 1.4307 | 22400 | 0.0 | - |
| 1.4339 | 22450 | 0.0 | - |
| 1.4371 | 22500 | 0.0 | - |
| 1.4403 | 22550 | 0.0 | - |
| 1.4434 | 22600 | 0.0 | - |
| 1.4466 | 22650 | 0.0 | - |
| 1.4498 | 22700 | 0.0 | - |
| 1.4530 | 22750 | 0.0 | - |
| 1.4562 | 22800 | 0.0 | - |
| 1.4594 | 22850 | 0.0 | - |
| 1.4626 | 22900 | 0.0 | - |
| 1.4658 | 22950 | 0.0 | - |
| 1.4690 | 23000 | 0.0 | - |
| 1.4722 | 23050 | 0.0 | - |
| 1.4754 | 23100 | 0.0 | - |
| 1.4786 | 23150 | 0.0 | - |
| 1.4818 | 23200 | 0.0 | - |
| 1.4850 | 23250 | 0.0 | - |
| 1.4882 | 23300 | 0.0 | - |
| 1.4913 | 23350 | 0.0 | - |
| 1.4945 | 23400 | 0.0 | - |
| 1.4977 | 23450 | 0.0 | - |
| 1.5009 | 23500 | 0.0 | - |
| 1.5041 | 23550 | 0.0 | - |
| 1.5073 | 23600 | 0.0 | - |
| 1.5105 | 23650 | 0.0 | - |
| 1.5137 | 23700 | 0.0 | - |
| 1.5169 | 23750 | 0.0 | - |
| 1.5201 | 23800 | 0.0 | - |
| 1.5233 | 23850 | 0.0 | - |
| 1.5265 | 23900 | 0.0002 | - |
| 1.5297 | 23950 | 0.0003 | - |
| 1.5329 | 24000 | 0.0 | - |
| 1.5361 | 24050 | 0.0 | - |
| 1.5392 | 24100 | 0.0 | - |
| 1.5424 | 24150 | 0.0 | - |
| 1.5456 | 24200 | 0.0 | - |
| 1.5488 | 24250 | 0.0 | - |
| 1.5520 | 24300 | 0.0 | - |
| 1.5552 | 24350 | 0.0 | - |
| 1.5584 | 24400 | 0.0 | - |
| 1.5616 | 24450 | 0.0 | - |
| 1.5648 | 24500 | 0.0 | - |
| 1.5680 | 24550 | 0.0 | - |
| 1.5712 | 24600 | 0.0 | - |
| 1.5744 | 24650 | 0.0 | - |
| 1.5776 | 24700 | 0.0 | - |
| 1.5808 | 24750 | 0.0 | - |
| 1.5840 | 24800 | 0.0 | - |
| 1.5871 | 24850 | 0.0 | - |
| 1.5903 | 24900 | 0.0 | - |
| 1.5935 | 24950 | 0.0 | - |
| 1.5967 | 25000 | 0.0 | - |
| 1.5999 | 25050 | 0.0 | - |
| 1.6031 | 25100 | 0.0 | - |
| 1.6063 | 25150 | 0.0 | - |
| 1.6095 | 25200 | 0.0 | - |
| 1.6127 | 25250 | 0.0 | - |
| 1.6159 | 25300 | 0.0 | - |
| 1.6191 | 25350 | 0.0 | - |
| 1.6223 | 25400 | 0.0 | - |
| 1.6255 | 25450 | 0.0 | - |
| 1.6287 | 25500 | 0.0 | - |
| 1.6319 | 25550 | 0.0 | - |
| 1.6351 | 25600 | 0.0 | - |
| 1.6382 | 25650 | 0.0 | - |
| 1.6414 | 25700 | 0.0 | - |
| 1.6446 | 25750 | 0.0 | - |
| 1.6478 | 25800 | 0.0 | - |
| 1.6510 | 25850 | 0.0 | - |
| 1.6542 | 25900 | 0.0 | - |
| 1.6574 | 25950 | 0.0 | - |
| 1.6606 | 26000 | 0.0 | - |
| 1.6638 | 26050 | 0.0 | - |
| 1.6670 | 26100 | 0.0 | - |
| 1.6702 | 26150 | 0.0 | - |
| 1.6734 | 26200 | 0.0 | - |
| 1.6766 | 26250 | 0.0 | - |
| 1.6798 | 26300 | 0.0 | - |
| 1.6830 | 26350 | 0.0 | - |
| 1.6861 | 26400 | 0.0 | - |
| 1.6893 | 26450 | 0.0 | - |
| 1.6925 | 26500 | 0.0 | - |
| 1.6957 | 26550 | 0.0 | - |
| 1.6989 | 26600 | 0.0001 | - |
| 1.7021 | 26650 | 0.0 | - |
| 1.7053 | 26700 | 0.0 | - |
| 1.7085 | 26750 | 0.0 | - |
| 1.7117 | 26800 | 0.0 | - |
| 1.7149 | 26850 | 0.0 | - |
| 1.7181 | 26900 | 0.0 | - |
| 1.7213 | 26950 | 0.0 | - |
| 1.7245 | 27000 | 0.0 | - |
| 1.7277 | 27050 | 0.0 | - |
| 1.7309 | 27100 | 0.0 | - |
| 1.7340 | 27150 | 0.0 | - |
| 1.7372 | 27200 | 0.0 | - |
| 1.7404 | 27250 | 0.0 | - |
| 1.7436 | 27300 | 0.0 | - |
| 1.7468 | 27350 | 0.0 | - |
| 1.7500 | 27400 | 0.0 | - |
| 1.7532 | 27450 | 0.0 | - |
| 1.7564 | 27500 | 0.0 | - |
| 1.7596 | 27550 | 0.0 | - |
| 1.7628 | 27600 | 0.0 | - |
| 1.7660 | 27650 | 0.0 | - |
| 1.7692 | 27700 | 0.0 | - |
| 1.7724 | 27750 | 0.0 | - |
| 1.7756 | 27800 | 0.0 | - |
| 1.7788 | 27850 | 0.0 | - |
| 1.7820 | 27900 | 0.0 | - |
| 1.7851 | 27950 | 0.0 | - |
| 1.7883 | 28000 | 0.0 | - |
| 1.7915 | 28050 | 0.0 | - |
| 1.7947 | 28100 | 0.0 | - |
| 1.7979 | 28150 | 0.0 | - |
| 1.8011 | 28200 | 0.0 | - |
| 1.8043 | 28250 | 0.0 | - |
| 1.8075 | 28300 | 0.0 | - |
| 1.8107 | 28350 | 0.0 | - |
| 1.8139 | 28400 | 0.0 | - |
| 1.8171 | 28450 | 0.0 | - |
| 1.8203 | 28500 | 0.0 | - |
| 1.8235 | 28550 | 0.0 | - |
| 1.8267 | 28600 | 0.0 | - |
| 1.8299 | 28650 | 0.0 | - |
| 1.8330 | 28700 | 0.0 | - |
| 1.8362 | 28750 | 0.0 | - |
| 1.8394 | 28800 | 0.0 | - |
| 1.8426 | 28850 | 0.0 | - |
| 1.8458 | 28900 | 0.0 | - |
| 1.8490 | 28950 | 0.0 | - |
| 1.8522 | 29000 | 0.0 | - |
| 1.8554 | 29050 | 0.0 | - |
| 1.8586 | 29100 | 0.0 | - |
| 1.8618 | 29150 | 0.0 | - |
| 1.8650 | 29200 | 0.0 | - |
| 1.8682 | 29250 | 0.0 | - |
| 1.8714 | 29300 | 0.0 | - |
| 1.8746 | 29350 | 0.0 | - |
| 1.8778 | 29400 | 0.0 | - |
| 1.8809 | 29450 | 0.0 | - |
| 1.8841 | 29500 | 0.0 | - |
| 1.8873 | 29550 | 0.0 | - |
| 1.8905 | 29600 | 0.0 | - |
| 1.8937 | 29650 | 0.0 | - |
| 1.8969 | 29700 | 0.0 | - |
| 1.9001 | 29750 | 0.0 | - |
| 1.9033 | 29800 | 0.0 | - |
| 1.9065 | 29850 | 0.0 | - |
| 1.9097 | 29900 | 0.0 | - |
| 1.9129 | 29950 | 0.0 | - |
| 1.9161 | 30000 | 0.0 | - |
| 1.9193 | 30050 | 0.0 | - |
| 1.9225 | 30100 | 0.0 | - |
| 1.9257 | 30150 | 0.0 | - |
| 1.9288 | 30200 | 0.0 | - |
| 1.9320 | 30250 | 0.0 | - |
| 1.9352 | 30300 | 0.0 | - |
| 1.9384 | 30350 | 0.0 | - |
| 1.9416 | 30400 | 0.0 | - |
| 1.9448 | 30450 | 0.0 | - |
| 1.9480 | 30500 | 0.0 | - |
| 1.9512 | 30550 | 0.0 | - |
| 1.9544 | 30600 | 0.0 | - |
| 1.9576 | 30650 | 0.0 | - |
| 1.9608 | 30700 | 0.0 | - |
| 1.9640 | 30750 | 0.0 | - |
| 1.9672 | 30800 | 0.0 | - |
| 1.9704 | 30850 | 0.0 | - |
| 1.9736 | 30900 | 0.0 | - |
| 1.9768 | 30950 | 0.0 | - |
| 1.9799 | 31000 | 0.0 | - |
| 1.9831 | 31050 | 0.0 | - |
| 1.9863 | 31100 | 0.0 | - |
| 1.9895 | 31150 | 0.0 | - |
| 1.9927 | 31200 | 0.0 | - |
| 1.9959 | 31250 | 0.0 | - |
| 1.9991 | 31300 | 0.0 | - |
| **2.0** | **31314** | **-** | **0.2634** |
| 2.0023 | 31350 | 0.0 | - |
| 2.0055 | 31400 | 0.0 | - |
| 2.0087 | 31450 | 0.0 | - |
| 2.0119 | 31500 | 0.0 | - |
| 2.0151 | 31550 | 0.0 | - |
| 2.0183 | 31600 | 0.0 | - |
| 2.0215 | 31650 | 0.0 | - |
| 2.0247 | 31700 | 0.0 | - |
| 2.0278 | 31750 | 0.0 | - |
| 2.0310 | 31800 | 0.0 | - |
| 2.0342 | 31850 | 0.0 | - |
| 2.0374 | 31900 | 0.0 | - |
| 2.0406 | 31950 | 0.0 | - |
| 2.0438 | 32000 | 0.0 | - |
| 2.0470 | 32050 | 0.0 | - |
| 2.0502 | 32100 | 0.0 | - |
| 2.0534 | 32150 | 0.0 | - |
| 2.0566 | 32200 | 0.0 | - |
| 2.0598 | 32250 | 0.0 | - |
| 2.0630 | 32300 | 0.0 | - |
| 2.0662 | 32350 | 0.0 | - |
| 2.0694 | 32400 | 0.0 | - |
| 2.0726 | 32450 | 0.0 | - |
| 2.0757 | 32500 | 0.0 | - |
| 2.0789 | 32550 | 0.0 | - |
| 2.0821 | 32600 | 0.0 | - |
| 2.0853 | 32650 | 0.0 | - |
| 2.0885 | 32700 | 0.0 | - |
| 2.0917 | 32750 | 0.0 | - |
| 2.0949 | 32800 | 0.0 | - |
| 2.0981 | 32850 | 0.0 | - |
| 2.1013 | 32900 | 0.0 | - |
| 2.1045 | 32950 | 0.0 | - |
| 2.1077 | 33000 | 0.0 | - |
| 2.1109 | 33050 | 0.0 | - |
| 2.1141 | 33100 | 0.0 | - |
| 2.1173 | 33150 | 0.0 | - |
| 2.1205 | 33200 | 0.0 | - |
| 2.1237 | 33250 | 0.0 | - |
| 2.1268 | 33300 | 0.0 | - |
| 2.1300 | 33350 | 0.0 | - |
| 2.1332 | 33400 | 0.0 | - |
| 2.1364 | 33450 | 0.0 | - |
| 2.1396 | 33500 | 0.0 | - |
| 2.1428 | 33550 | 0.0 | - |
| 2.1460 | 33600 | 0.0 | - |
| 2.1492 | 33650 | 0.0 | - |
| 2.1524 | 33700 | 0.0 | - |
| 2.1556 | 33750 | 0.0 | - |
| 2.1588 | 33800 | 0.0 | - |
| 2.1620 | 33850 | 0.0 | - |
| 2.1652 | 33900 | 0.0 | - |
| 2.1684 | 33950 | 0.0 | - |
| 2.1716 | 34000 | 0.0 | - |
| 2.1747 | 34050 | 0.0 | - |
| 2.1779 | 34100 | 0.0 | - |
| 2.1811 | 34150 | 0.0 | - |
| 2.1843 | 34200 | 0.0 | - |
| 2.1875 | 34250 | 0.0 | - |
| 2.1907 | 34300 | 0.0 | - |
| 2.1939 | 34350 | 0.0 | - |
| 2.1971 | 34400 | 0.0 | - |
| 2.2003 | 34450 | 0.0 | - |
| 2.2035 | 34500 | 0.0 | - |
| 2.2067 | 34550 | 0.0 | - |
| 2.2099 | 34600 | 0.0 | - |
| 2.2131 | 34650 | 0.0 | - |
| 2.2163 | 34700 | 0.0 | - |
| 2.2195 | 34750 | 0.0 | - |
| 2.2226 | 34800 | 0.0 | - |
| 2.2258 | 34850 | 0.0 | - |
| 2.2290 | 34900 | 0.0 | - |
| 2.2322 | 34950 | 0.0 | - |
| 2.2354 | 35000 | 0.0 | - |
| 2.2386 | 35050 | 0.0 | - |
| 2.2418 | 35100 | 0.0 | - |
| 2.2450 | 35150 | 0.0 | - |
| 2.2482 | 35200 | 0.0 | - |
| 2.2514 | 35250 | 0.0 | - |
| 2.2546 | 35300 | 0.0 | - |
| 2.2578 | 35350 | 0.0 | - |
| 2.2610 | 35400 | 0.0 | - |
| 2.2642 | 35450 | 0.0 | - |
| 2.2674 | 35500 | 0.0 | - |
| 2.2705 | 35550 | 0.0 | - |
| 2.2737 | 35600 | 0.0 | - |
| 2.2769 | 35650 | 0.0 | - |
| 2.2801 | 35700 | 0.0 | - |
| 2.2833 | 35750 | 0.0 | - |
| 2.2865 | 35800 | 0.0 | - |
| 2.2897 | 35850 | 0.0 | - |
| 2.2929 | 35900 | 0.0 | - |
| 2.2961 | 35950 | 0.0 | - |
| 2.2993 | 36000 | 0.0 | - |
| 2.3025 | 36050 | 0.0 | - |
| 2.3057 | 36100 | 0.0 | - |
| 2.3089 | 36150 | 0.0 | - |
| 2.3121 | 36200 | 0.0 | - |
| 2.3153 | 36250 | 0.0 | - |
| 2.3185 | 36300 | 0.0 | - |
| 2.3216 | 36350 | 0.0 | - |
| 2.3248 | 36400 | 0.0 | - |
| 2.3280 | 36450 | 0.0 | - |
| 2.3312 | 36500 | 0.0 | - |
| 2.3344 | 36550 | 0.0 | - |
| 2.3376 | 36600 | 0.0 | - |
| 2.3408 | 36650 | 0.0 | - |
| 2.3440 | 36700 | 0.0 | - |
| 2.3472 | 36750 | 0.0 | - |
| 2.3504 | 36800 | 0.0 | - |
| 2.3536 | 36850 | 0.0 | - |
| 2.3568 | 36900 | 0.0 | - |
| 2.3600 | 36950 | 0.0 | - |
| 2.3632 | 37000 | 0.0 | - |
| 2.3664 | 37050 | 0.0 | - |
| 2.3695 | 37100 | 0.0 | - |
| 2.3727 | 37150 | 0.0 | - |
| 2.3759 | 37200 | 0.0 | - |
| 2.3791 | 37250 | 0.0 | - |
| 2.3823 | 37300 | 0.0 | - |
| 2.3855 | 37350 | 0.0 | - |
| 2.3887 | 37400 | 0.0 | - |
| 2.3919 | 37450 | 0.0 | - |
| 2.3951 | 37500 | 0.0 | - |
| 2.3983 | 37550 | 0.0 | - |
| 2.4015 | 37600 | 0.0 | - |
| 2.4047 | 37650 | 0.0 | - |
| 2.4079 | 37700 | 0.0 | - |
| 2.4111 | 37750 | 0.0 | - |
| 2.4143 | 37800 | 0.0 | - |
| 2.4174 | 37850 | 0.0 | - |
| 2.4206 | 37900 | 0.0 | - |
| 2.4238 | 37950 | 0.0 | - |
| 2.4270 | 38000 | 0.0 | - |
| 2.4302 | 38050 | 0.0 | - |
| 2.4334 | 38100 | 0.0 | - |
| 2.4366 | 38150 | 0.0 | - |
| 2.4398 | 38200 | 0.0 | - |
| 2.4430 | 38250 | 0.0 | - |
| 2.4462 | 38300 | 0.0 | - |
| 2.4494 | 38350 | 0.0 | - |
| 2.4526 | 38400 | 0.0 | - |
| 2.4558 | 38450 | 0.0 | - |
| 2.4590 | 38500 | 0.0 | - |
| 2.4622 | 38550 | 0.0 | - |
| 2.4654 | 38600 | 0.0 | - |
| 2.4685 | 38650 | 0.0 | - |
| 2.4717 | 38700 | 0.0 | - |
| 2.4749 | 38750 | 0.0 | - |
| 2.4781 | 38800 | 0.0 | - |
| 2.4813 | 38850 | 0.0 | - |
| 2.4845 | 38900 | 0.0 | - |
| 2.4877 | 38950 | 0.0 | - |
| 2.4909 | 39000 | 0.0 | - |
| 2.4941 | 39050 | 0.0 | - |
| 2.4973 | 39100 | 0.0 | - |
| 2.5005 | 39150 | 0.0 | - |
| 2.5037 | 39200 | 0.0 | - |
| 2.5069 | 39250 | 0.0 | - |
| 2.5101 | 39300 | 0.0 | - |
| 2.5133 | 39350 | 0.0 | - |
| 2.5164 | 39400 | 0.0 | - |
| 2.5196 | 39450 | 0.0 | - |
| 2.5228 | 39500 | 0.0 | - |
| 2.5260 | 39550 | 0.0 | - |
| 2.5292 | 39600 | 0.0 | - |
| 2.5324 | 39650 | 0.0 | - |
| 2.5356 | 39700 | 0.0 | - |
| 2.5388 | 39750 | 0.0 | - |
| 2.5420 | 39800 | 0.0 | - |
| 2.5452 | 39850 | 0.0 | - |
| 2.5484 | 39900 | 0.0 | - |
| 2.5516 | 39950 | 0.0 | - |
| 2.5548 | 40000 | 0.0 | - |
| 2.5580 | 40050 | 0.0 | - |
| 2.5612 | 40100 | 0.0 | - |
| 2.5643 | 40150 | 0.0 | - |
| 2.5675 | 40200 | 0.0 | - |
| 2.5707 | 40250 | 0.0 | - |
| 2.5739 | 40300 | 0.0 | - |
| 2.5771 | 40350 | 0.0 | - |
| 2.5803 | 40400 | 0.0 | - |
| 2.5835 | 40450 | 0.0 | - |
| 2.5867 | 40500 | 0.0 | - |
| 2.5899 | 40550 | 0.0 | - |
| 2.5931 | 40600 | 0.0 | - |
| 2.5963 | 40650 | 0.0 | - |
| 2.5995 | 40700 | 0.0 | - |
| 2.6027 | 40750 | 0.0 | - |
| 2.6059 | 40800 | 0.0 | - |
| 2.6091 | 40850 | 0.0 | - |
| 2.6123 | 40900 | 0.0 | - |
| 2.6154 | 40950 | 0.0 | - |
| 2.6186 | 41000 | 0.0 | - |
| 2.6218 | 41050 | 0.0 | - |
| 2.6250 | 41100 | 0.0 | - |
| 2.6282 | 41150 | 0.0 | - |
| 2.6314 | 41200 | 0.0 | - |
| 2.6346 | 41250 | 0.0 | - |
| 2.6378 | 41300 | 0.0 | - |
| 2.6410 | 41350 | 0.0 | - |
| 2.6442 | 41400 | 0.0 | - |
| 2.6474 | 41450 | 0.0 | - |
| 2.6506 | 41500 | 0.0 | - |
| 2.6538 | 41550 | 0.0 | - |
| 2.6570 | 41600 | 0.0 | - |
| 2.6602 | 41650 | 0.0 | - |
| 2.6633 | 41700 | 0.0 | - |
| 2.6665 | 41750 | 0.0 | - |
| 2.6697 | 41800 | 0.0 | - |
| 2.6729 | 41850 | 0.0 | - |
| 2.6761 | 41900 | 0.0 | - |
| 2.6793 | 41950 | 0.0 | - |
| 2.6825 | 42000 | 0.0 | - |
| 2.6857 | 42050 | 0.0 | - |
| 2.6889 | 42100 | 0.0 | - |
| 2.6921 | 42150 | 0.0 | - |
| 2.6953 | 42200 | 0.0 | - |
| 2.6985 | 42250 | 0.0 | - |
| 2.7017 | 42300 | 0.0 | - |
| 2.7049 | 42350 | 0.0 | - |
| 2.7081 | 42400 | 0.0 | - |
| 2.7112 | 42450 | 0.0 | - |
| 2.7144 | 42500 | 0.0 | - |
| 2.7176 | 42550 | 0.0 | - |
| 2.7208 | 42600 | 0.0 | - |
| 2.7240 | 42650 | 0.0 | - |
| 2.7272 | 42700 | 0.0 | - |
| 2.7304 | 42750 | 0.0 | - |
| 2.7336 | 42800 | 0.0 | - |
| 2.7368 | 42850 | 0.0 | - |
| 2.7400 | 42900 | 0.0 | - |
| 2.7432 | 42950 | 0.0 | - |
| 2.7464 | 43000 | 0.0 | - |
| 2.7496 | 43050 | 0.0 | - |
| 2.7528 | 43100 | 0.0 | - |
| 2.7560 | 43150 | 0.0 | - |
| 2.7591 | 43200 | 0.0 | - |
| 2.7623 | 43250 | 0.0 | - |
| 2.7655 | 43300 | 0.0 | - |
| 2.7687 | 43350 | 0.0 | - |
| 2.7719 | 43400 | 0.0 | - |
| 2.7751 | 43450 | 0.0 | - |
| 2.7783 | 43500 | 0.0 | - |
| 2.7815 | 43550 | 0.0 | - |
| 2.7847 | 43600 | 0.0 | - |
| 2.7879 | 43650 | 0.0 | - |
| 2.7911 | 43700 | 0.0 | - |
| 2.7943 | 43750 | 0.0 | - |
| 2.7975 | 43800 | 0.0 | - |
| 2.8007 | 43850 | 0.0 | - |
| 2.8039 | 43900 | 0.0 | - |
| 2.8071 | 43950 | 0.0 | - |
| 2.8102 | 44000 | 0.0 | - |
| 2.8134 | 44050 | 0.0 | - |
| 2.8166 | 44100 | 0.0 | - |
| 2.8198 | 44150 | 0.0 | - |
| 2.8230 | 44200 | 0.0 | - |
| 2.8262 | 44250 | 0.0 | - |
| 2.8294 | 44300 | 0.0 | - |
| 2.8326 | 44350 | 0.0 | - |
| 2.8358 | 44400 | 0.0 | - |
| 2.8390 | 44450 | 0.0 | - |
| 2.8422 | 44500 | 0.0 | - |
| 2.8454 | 44550 | 0.0 | - |
| 2.8486 | 44600 | 0.0 | - |
| 2.8518 | 44650 | 0.0 | - |
| 2.8550 | 44700 | 0.0 | - |
| 2.8581 | 44750 | 0.0 | - |
| 2.8613 | 44800 | 0.0 | - |
| 2.8645 | 44850 | 0.0 | - |
| 2.8677 | 44900 | 0.0 | - |
| 2.8709 | 44950 | 0.0 | - |
| 2.8741 | 45000 | 0.0 | - |
| 2.8773 | 45050 | 0.0 | - |
| 2.8805 | 45100 | 0.0 | - |
| 2.8837 | 45150 | 0.0 | - |
| 2.8869 | 45200 | 0.0 | - |
| 2.8901 | 45250 | 0.0 | - |
| 2.8933 | 45300 | 0.0 | - |
| 2.8965 | 45350 | 0.0 | - |
| 2.8997 | 45400 | 0.0 | - |
| 2.9029 | 45450 | 0.0 | - |
| 2.9060 | 45500 | 0.0 | - |
| 2.9092 | 45550 | 0.0 | - |
| 2.9124 | 45600 | 0.0 | - |
| 2.9156 | 45650 | 0.0 | - |
| 2.9188 | 45700 | 0.0 | - |
| 2.9220 | 45750 | 0.0 | - |
| 2.9252 | 45800 | 0.0 | - |
| 2.9284 | 45850 | 0.0 | - |
| 2.9316 | 45900 | 0.0 | - |
| 2.9348 | 45950 | 0.0 | - |
| 2.9380 | 46000 | 0.0 | - |
| 2.9412 | 46050 | 0.0 | - |
| 2.9444 | 46100 | 0.0 | - |
| 2.9476 | 46150 | 0.0 | - |
| 2.9508 | 46200 | 0.0 | - |
| 2.9540 | 46250 | 0.0 | - |
| 2.9571 | 46300 | 0.0 | - |
| 2.9603 | 46350 | 0.0 | - |
| 2.9635 | 46400 | 0.0 | - |
| 2.9667 | 46450 | 0.0 | - |
| 2.9699 | 46500 | 0.0 | - |
| 2.9731 | 46550 | 0.0 | - |
| 2.9763 | 46600 | 0.0 | - |
| 2.9795 | 46650 | 0.0 | - |
| 2.9827 | 46700 | 0.0 | - |
| 2.9859 | 46750 | 0.0 | - |
| 2.9891 | 46800 | 0.0 | - |
| 2.9923 | 46850 | 0.0 | - |
| 2.9955 | 46900 | 0.0 | - |
| 2.9987 | 46950 | 0.0 | - |
| 3.0 | 46971 | - | 0.2651 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## 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}
}
```