---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: aku hanya menyukai setiap menit film ini.
- text: bioskop orang dalam kondisi terbaiknya.
- text: bukan untuk orang yang mudah tersinggung atau mudah tersinggung, ini adalah
pemeriksaan yang berani dan berkepanjangan terhadap budaya yang diidolakan, kebencian
terhadap diri sendiri, dan politik seksual.
- text: itu curang.
- text: Meskipun penduduk setempat akan senang melihat situs-situs Cleveland, seluruh
dunia akan menikmati komedi bertempo cepat dengan keunikan yang mungkin membuat
iri para coen bersaudara yang telah memenangkan penghargaan.
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: 0.4248868778280543
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:** 5 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 |
|:---------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negatif | - 'orang aneh berkaki delapan tidak akan bergabung dengan jajaran film monster\\/fiksi ilmiah hebat yang kita sukai ...'
- "Terlepas dari latar Hawaii, hiasan fiksi ilmiah, dan beberapa momen slapstick yang gaduh, plot dasar `` lilo '' bisa saja diambil dari naskah kuno Shirley Temple yang berlumuran air mata."
- 'ini adalah film yang sangat tidak aman dalam kemampuannya untuk menggairahkan sehingga menghasilkan bukan hanya satu tapi dua badai petir palsu untuk menggarisbawahi aksinya.'
|
| positif | - 'plot dari comeback curlers sebenarnya tidak terlalu menarik, tapi yang aku suka dari pria dengan sapu dan yang spesial adalah bagaimana filmnya mengetahui apa yang unik dan nyentrik dari orang Kanada.'
- 'sebuah studi psikologis yang dingin, merenung, namun bergema secara diam-diam mengenai ketegangan dan ketidakbahagiaan dalam rumah tangga.'
- 'seperti yang biasa mereka katakan di film-film fiksi ilmiah tahun 1950-an, tanda-tanda adalah penghormatan terhadap hadiah Shyamalan, yang sedemikian rupa sehingga kita akan terus mengawasi langit untuk proyek berikutnya.'
|
| sangat negatif | - "benar-benar transparan adalah serangan tanpa henti dari naskah tersebut berupa lelucon-lelucon seks memalukan yang berbau penulisan ulang naskah yang dirancang untuk membuat film tersebut mendapat peringkat `` lebih keren '' pg-13."
- 'bagaikan latihan improvisasi yang buruk, karakter-karakter yang ditulis secara dangkal mengoceh dengan membosankan tentang kehidupan, cinta, dan seni yang sedang mereka perjuangkan untuk ciptakan.'
- 'dari semua Halloween, ini yang paling tidak menarik secara visual.'
|
| netral | - 'film ini tidak menghormati hukum, kebenaran politik, atau kesopanan umum, namun menampilkan sesuatu yang lebih penting: rasa hormat terhadap orang-orang yang cacat dan gila.'
- 'tertahan oleh kekhidmatannya sendiri.'
- 'lebih banyak pertunjukan vaudeville daripada narasi yang dibangun dengan baik, namun dalam hal ini tidak menyinggung dan sebenarnya agak manis.'
|
| sangat positif | - 'hawn dan sarandon membentuk ikatan akting yang menjadikan banger bersaudara studi karakter yang menarik sambil tertawa.'
- 'isabelle huppert unggul sebagai mika yang penuh teka-teki dan anna mouglais adalah bakat muda baru yang menakjubkan dalam salah satu misteri psikologis chabrol yang paling intens.'
- 'williams menciptakan gambaran menakjubkan seperti sopir taksi tentang seorang pria yang tertatih-tatih di ambang kewarasan.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4249 |
## 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-p2")
# Run inference
preds = model("itu curang.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 15.7676 | 46 |
| Label | Training Sample Count |
|:---------------|:----------------------|
| sangat negatif | 500 |
| negatif | 500 |
| netral | 500 |
| positif | 500 |
| sangat positif | 500 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (1, 1)
- 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 |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.3367 | - |
| 0.0013 | 50 | 0.3139 | - |
| 0.0026 | 100 | 0.3005 | - |
| 0.0038 | 150 | 0.2627 | - |
| 0.0051 | 200 | 0.2701 | - |
| 0.0064 | 250 | 0.2647 | - |
| 0.0077 | 300 | 0.2646 | - |
| 0.0090 | 350 | 0.2494 | - |
| 0.0102 | 400 | 0.2356 | - |
| 0.0115 | 450 | 0.2093 | - |
| 0.0128 | 500 | 0.2187 | - |
| 0.0141 | 550 | 0.2131 | - |
| 0.0154 | 600 | 0.2288 | - |
| 0.0166 | 650 | 0.1996 | - |
| 0.0179 | 700 | 0.1825 | - |
| 0.0192 | 750 | 0.1887 | - |
| 0.0205 | 800 | 0.1809 | - |
| 0.0218 | 850 | 0.1756 | - |
| 0.0230 | 900 | 0.155 | - |
| 0.0243 | 950 | 0.1462 | - |
| 0.0256 | 1000 | 0.1455 | - |
| 0.0269 | 1050 | 0.1547 | - |
| 0.0282 | 1100 | 0.0863 | - |
| 0.0294 | 1150 | 0.1362 | - |
| 0.0307 | 1200 | 0.1096 | - |
| 0.0320 | 1250 | 0.0898 | - |
| 0.0333 | 1300 | 0.1202 | - |
| 0.0346 | 1350 | 0.0916 | - |
| 0.0358 | 1400 | 0.0918 | - |
| 0.0371 | 1450 | 0.1022 | - |
| 0.0384 | 1500 | 0.0518 | - |
| 0.0397 | 1550 | 0.0587 | - |
| 0.0410 | 1600 | 0.0526 | - |
| 0.0422 | 1650 | 0.0461 | - |
| 0.0435 | 1700 | 0.0617 | - |
| 0.0448 | 1750 | 0.0426 | - |
| 0.0461 | 1800 | 0.0347 | - |
| 0.0474 | 1850 | 0.0255 | - |
| 0.0486 | 1900 | 0.0349 | - |
| 0.0499 | 1950 | 0.0121 | - |
| 0.0512 | 2000 | 0.0164 | - |
| 0.0525 | 2050 | 0.0077 | - |
| 0.0538 | 2100 | 0.0084 | - |
| 0.0550 | 2150 | 0.006 | - |
| 0.0563 | 2200 | 0.0143 | - |
| 0.0576 | 2250 | 0.0123 | - |
| 0.0589 | 2300 | 0.0154 | - |
| 0.0602 | 2350 | 0.0108 | - |
| 0.0614 | 2400 | 0.0041 | - |
| 0.0627 | 2450 | 0.0048 | - |
| 0.0640 | 2500 | 0.0103 | - |
| 0.0653 | 2550 | 0.0099 | - |
| 0.0666 | 2600 | 0.026 | - |
| 0.0678 | 2650 | 0.0095 | - |
| 0.0691 | 2700 | 0.0091 | - |
| 0.0704 | 2750 | 0.0041 | - |
| 0.0717 | 2800 | 0.005 | - |
| 0.0730 | 2850 | 0.0024 | - |
| 0.0742 | 2900 | 0.0013 | - |
| 0.0755 | 2950 | 0.0067 | - |
| 0.0768 | 3000 | 0.0009 | - |
| 0.0781 | 3050 | 0.0042 | - |
| 0.0794 | 3100 | 0.0039 | - |
| 0.0806 | 3150 | 0.0023 | - |
| 0.0819 | 3200 | 0.0032 | - |
| 0.0832 | 3250 | 0.0071 | - |
| 0.0845 | 3300 | 0.013 | - |
| 0.0858 | 3350 | 0.015 | - |
| 0.0870 | 3400 | 0.0013 | - |
| 0.0883 | 3450 | 0.0012 | - |
| 0.0896 | 3500 | 0.0017 | - |
| 0.0909 | 3550 | 0.002 | - |
| 0.0922 | 3600 | 0.0247 | - |
| 0.0934 | 3650 | 0.0044 | - |
| 0.0947 | 3700 | 0.0004 | - |
| 0.0960 | 3750 | 0.0031 | - |
| 0.0973 | 3800 | 0.0235 | - |
| 0.0986 | 3850 | 0.0017 | - |
| 0.0998 | 3900 | 0.001 | - |
| 0.1011 | 3950 | 0.0065 | - |
| 0.1024 | 4000 | 0.0043 | - |
| 0.1037 | 4050 | 0.0051 | - |
| 0.1050 | 4100 | 0.0009 | - |
| 0.1062 | 4150 | 0.0006 | - |
| 0.1075 | 4200 | 0.0081 | - |
| 0.1088 | 4250 | 0.0005 | - |
| 0.1101 | 4300 | 0.0155 | - |
| 0.1114 | 4350 | 0.0091 | - |
| 0.1126 | 4400 | 0.0187 | - |
| 0.1139 | 4450 | 0.0011 | - |
| 0.1152 | 4500 | 0.0037 | - |
| 0.1165 | 4550 | 0.0033 | - |
| 0.1178 | 4600 | 0.0006 | - |
| 0.1190 | 4650 | 0.0024 | - |
| 0.1203 | 4700 | 0.0008 | - |
| 0.1216 | 4750 | 0.0007 | - |
| 0.1229 | 4800 | 0.0012 | - |
| 0.1242 | 4850 | 0.0113 | - |
| 0.1254 | 4900 | 0.0004 | - |
| 0.1267 | 4950 | 0.0059 | - |
| 0.1280 | 5000 | 0.0004 | - |
| 0.1293 | 5050 | 0.001 | - |
| 0.1306 | 5100 | 0.0001 | - |
| 0.1318 | 5150 | 0.002 | - |
| 0.1331 | 5200 | 0.0006 | - |
| 0.1344 | 5250 | 0.0007 | - |
| 0.1357 | 5300 | 0.0026 | - |
| 0.1370 | 5350 | 0.0079 | - |
| 0.1382 | 5400 | 0.001 | - |
| 0.1395 | 5450 | 0.0065 | - |
| 0.1408 | 5500 | 0.0009 | - |
| 0.1421 | 5550 | 0.0008 | - |
| 0.1434 | 5600 | 0.0003 | - |
| 0.1446 | 5650 | 0.0002 | - |
| 0.1459 | 5700 | 0.0001 | - |
| 0.1472 | 5750 | 0.0027 | - |
| 0.1485 | 5800 | 0.0002 | - |
| 0.1498 | 5850 | 0.0002 | - |
| 0.1510 | 5900 | 0.0003 | - |
| 0.1523 | 5950 | 0.0001 | - |
| 0.1536 | 6000 | 0.0061 | - |
| 0.1549 | 6050 | 0.0066 | - |
| 0.1562 | 6100 | 0.0015 | - |
| 0.1574 | 6150 | 0.016 | - |
| 0.1587 | 6200 | 0.0009 | - |
| 0.1600 | 6250 | 0.0062 | - |
| 0.1613 | 6300 | 0.0002 | - |
| 0.1626 | 6350 | 0.0002 | - |
| 0.1638 | 6400 | 0.0002 | - |
| 0.1651 | 6450 | 0.0153 | - |
| 0.1664 | 6500 | 0.0031 | - |
| 0.1677 | 6550 | 0.0003 | - |
| 0.1690 | 6600 | 0.0009 | - |
| 0.1702 | 6650 | 0.0043 | - |
| 0.1715 | 6700 | 0.0007 | - |
| 0.1728 | 6750 | 0.0002 | - |
| 0.1741 | 6800 | 0.0001 | - |
| 0.1754 | 6850 | 0.0003 | - |
| 0.1766 | 6900 | 0.0013 | - |
| 0.1779 | 6950 | 0.0003 | - |
| 0.1792 | 7000 | 0.0002 | - |
| 0.1805 | 7050 | 0.0001 | - |
| 0.1818 | 7100 | 0.0001 | - |
| 0.1830 | 7150 | 0.0001 | - |
| 0.1843 | 7200 | 0.0001 | - |
| 0.1856 | 7250 | 0.0003 | - |
| 0.1869 | 7300 | 0.0001 | - |
| 0.1882 | 7350 | 0.0002 | - |
| 0.1894 | 7400 | 0.0012 | - |
| 0.1907 | 7450 | 0.0001 | - |
| 0.1920 | 7500 | 0.0002 | - |
| 0.1933 | 7550 | 0.0002 | - |
| 0.1946 | 7600 | 0.0003 | - |
| 0.1958 | 7650 | 0.0014 | - |
| 0.1971 | 7700 | 0.0093 | - |
| 0.1984 | 7750 | 0.0001 | - |
| 0.1997 | 7800 | 0.0005 | - |
| 0.2010 | 7850 | 0.0001 | - |
| 0.2022 | 7900 | 0.0001 | - |
| 0.2035 | 7950 | 0.0058 | - |
| 0.2048 | 8000 | 0.0002 | - |
| 0.2061 | 8050 | 0.0001 | - |
| 0.2074 | 8100 | 0.0002 | - |
| 0.2086 | 8150 | 0.0003 | - |
| 0.2099 | 8200 | 0.0003 | - |
| 0.2112 | 8250 | 0.0068 | - |
| 0.2125 | 8300 | 0.0004 | - |
| 0.2138 | 8350 | 0.0002 | - |
| 0.2150 | 8400 | 0.0001 | - |
| 0.2163 | 8450 | 0.0002 | - |
| 0.2176 | 8500 | 0.0001 | - |
| 0.2189 | 8550 | 0.0002 | - |
| 0.2202 | 8600 | 0.0001 | - |
| 0.2214 | 8650 | 0.0001 | - |
| 0.2227 | 8700 | 0.0001 | - |
| 0.2240 | 8750 | 0.0001 | - |
| 0.2253 | 8800 | 0.0001 | - |
| 0.2266 | 8850 | 0.0006 | - |
| 0.2278 | 8900 | 0.0 | - |
| 0.2291 | 8950 | 0.0 | - |
| 0.2304 | 9000 | 0.0001 | - |
| 0.2317 | 9050 | 0.0 | - |
| 0.2330 | 9100 | 0.0001 | - |
| 0.2342 | 9150 | 0.0 | - |
| 0.2355 | 9200 | 0.0001 | - |
| 0.2368 | 9250 | 0.0 | - |
| 0.2381 | 9300 | 0.0001 | - |
| 0.2394 | 9350 | 0.0001 | - |
| 0.2406 | 9400 | 0.0 | - |
| 0.2419 | 9450 | 0.0 | - |
| 0.2432 | 9500 | 0.0001 | - |
| 0.2445 | 9550 | 0.0 | - |
| 0.2458 | 9600 | 0.0001 | - |
| 0.2470 | 9650 | 0.0001 | - |
| 0.2483 | 9700 | 0.003 | - |
| 0.2496 | 9750 | 0.0077 | - |
| 0.2509 | 9800 | 0.0099 | - |
| 0.2522 | 9850 | 0.0223 | - |
| 0.2534 | 9900 | 0.0002 | - |
| 0.2547 | 9950 | 0.0001 | - |
| 0.2560 | 10000 | 0.003 | - |
| 0.2573 | 10050 | 0.0118 | - |
| 0.2586 | 10100 | 0.0002 | - |
| 0.2598 | 10150 | 0.0022 | - |
| 0.2611 | 10200 | 0.0001 | - |
| 0.2624 | 10250 | 0.0077 | - |
| 0.2637 | 10300 | 0.0003 | - |
| 0.2650 | 10350 | 0.0 | - |
| 0.2662 | 10400 | 0.0074 | - |
| 0.2675 | 10450 | 0.0072 | - |
| 0.2688 | 10500 | 0.0001 | - |
| 0.2701 | 10550 | 0.008 | - |
| 0.2714 | 10600 | 0.0001 | - |
| 0.2726 | 10650 | 0.0001 | - |
| 0.2739 | 10700 | 0.0 | - |
| 0.2752 | 10750 | 0.0001 | - |
| 0.2765 | 10800 | 0.0074 | - |
| 0.2778 | 10850 | 0.0001 | - |
| 0.2790 | 10900 | 0.0001 | - |
| 0.2803 | 10950 | 0.0003 | - |
| 0.2816 | 11000 | 0.0004 | - |
| 0.2829 | 11050 | 0.0078 | - |
| 0.2842 | 11100 | 0.0 | - |
| 0.2854 | 11150 | 0.0001 | - |
| 0.2867 | 11200 | 0.0001 | - |
| 0.2880 | 11250 | 0.0001 | - |
| 0.2893 | 11300 | 0.0 | - |
| 0.2906 | 11350 | 0.0001 | - |
| 0.2918 | 11400 | 0.0001 | - |
| 0.2931 | 11450 | 0.0004 | - |
| 0.2944 | 11500 | 0.0002 | - |
| 0.2957 | 11550 | 0.0 | - |
| 0.2970 | 11600 | 0.0 | - |
| 0.2982 | 11650 | 0.0078 | - |
| 0.2995 | 11700 | 0.0 | - |
| 0.3008 | 11750 | 0.0005 | - |
| 0.3021 | 11800 | 0.0001 | - |
| 0.3034 | 11850 | 0.0 | - |
| 0.3046 | 11900 | 0.0 | - |
| 0.3059 | 11950 | 0.0 | - |
| 0.3072 | 12000 | 0.0006 | - |
| 0.3085 | 12050 | 0.0078 | - |
| 0.3098 | 12100 | 0.0001 | - |
| 0.3110 | 12150 | 0.0 | - |
| 0.3123 | 12200 | 0.0 | - |
| 0.3136 | 12250 | 0.0 | - |
| 0.3149 | 12300 | 0.0 | - |
| 0.3162 | 12350 | 0.0 | - |
| 0.3174 | 12400 | 0.0 | - |
| 0.3187 | 12450 | 0.0 | - |
| 0.3200 | 12500 | 0.0 | - |
| 0.3213 | 12550 | 0.0002 | - |
| 0.3226 | 12600 | 0.0 | - |
| 0.3238 | 12650 | 0.0003 | - |
| 0.3251 | 12700 | 0.0001 | - |
| 0.3264 | 12750 | 0.0001 | - |
| 0.3277 | 12800 | 0.0 | - |
| 0.3290 | 12850 | 0.0001 | - |
| 0.3302 | 12900 | 0.0001 | - |
| 0.3315 | 12950 | 0.0001 | - |
| 0.3328 | 13000 | 0.0 | - |
| 0.3341 | 13050 | 0.0 | - |
| 0.3354 | 13100 | 0.0 | - |
| 0.3366 | 13150 | 0.0 | - |
| 0.3379 | 13200 | 0.0 | - |
| 0.3392 | 13250 | 0.0 | - |
| 0.3405 | 13300 | 0.0 | - |
| 0.3418 | 13350 | 0.0 | - |
| 0.3430 | 13400 | 0.0 | - |
| 0.3443 | 13450 | 0.0 | - |
| 0.3456 | 13500 | 0.0 | - |
| 0.3469 | 13550 | 0.0005 | - |
| 0.3482 | 13600 | 0.0 | - |
| 0.3494 | 13650 | 0.0 | - |
| 0.3507 | 13700 | 0.0 | - |
| 0.3520 | 13750 | 0.0 | - |
| 0.3533 | 13800 | 0.0011 | - |
| 0.3546 | 13850 | 0.0001 | - |
| 0.3558 | 13900 | 0.0079 | - |
| 0.3571 | 13950 | 0.0001 | - |
| 0.3584 | 14000 | 0.0 | - |
| 0.3597 | 14050 | 0.0 | - |
| 0.3610 | 14100 | 0.0 | - |
| 0.3622 | 14150 | 0.0 | - |
| 0.3635 | 14200 | 0.0074 | - |
| 0.3648 | 14250 | 0.0 | - |
| 0.3661 | 14300 | 0.0 | - |
| 0.3674 | 14350 | 0.0001 | - |
| 0.3686 | 14400 | 0.0 | - |
| 0.3699 | 14450 | 0.0001 | - |
| 0.3712 | 14500 | 0.0 | - |
| 0.3725 | 14550 | 0.0 | - |
| 0.3738 | 14600 | 0.0 | - |
| 0.3750 | 14650 | 0.0002 | - |
| 0.3763 | 14700 | 0.0001 | - |
| 0.3776 | 14750 | 0.0 | - |
| 0.3789 | 14800 | 0.0001 | - |
| 0.3802 | 14850 | 0.0 | - |
| 0.3814 | 14900 | 0.0001 | - |
| 0.3827 | 14950 | 0.0 | - |
| 0.3840 | 15000 | 0.0 | - |
| 0.3853 | 15050 | 0.0 | - |
| 0.3866 | 15100 | 0.0 | - |
| 0.3878 | 15150 | 0.0 | - |
| 0.3891 | 15200 | 0.0 | - |
| 0.3904 | 15250 | 0.0 | - |
| 0.3917 | 15300 | 0.0001 | - |
| 0.3930 | 15350 | 0.0 | - |
| 0.3942 | 15400 | 0.0 | - |
| 0.3955 | 15450 | 0.0 | - |
| 0.3968 | 15500 | 0.0 | - |
| 0.3981 | 15550 | 0.0 | - |
| 0.3994 | 15600 | 0.0 | - |
| 0.4006 | 15650 | 0.0 | - |
| 0.4019 | 15700 | 0.0 | - |
| 0.4032 | 15750 | 0.0001 | - |
| 0.4045 | 15800 | 0.0 | - |
| 0.4058 | 15850 | 0.0 | - |
| 0.4070 | 15900 | 0.0 | - |
| 0.4083 | 15950 | 0.0 | - |
| 0.4096 | 16000 | 0.0 | - |
| 0.4109 | 16050 | 0.0 | - |
| 0.4122 | 16100 | 0.0 | - |
| 0.4134 | 16150 | 0.0 | - |
| 0.4147 | 16200 | 0.0 | - |
| 0.4160 | 16250 | 0.0003 | - |
| 0.4173 | 16300 | 0.0 | - |
| 0.4186 | 16350 | 0.0 | - |
| 0.4198 | 16400 | 0.0 | - |
| 0.4211 | 16450 | 0.0 | - |
| 0.4224 | 16500 | 0.0 | - |
| 0.4237 | 16550 | 0.0 | - |
| 0.4250 | 16600 | 0.0 | - |
| 0.4262 | 16650 | 0.0 | - |
| 0.4275 | 16700 | 0.0 | - |
| 0.4288 | 16750 | 0.0 | - |
| 0.4301 | 16800 | 0.0 | - |
| 0.4314 | 16850 | 0.0 | - |
| 0.4326 | 16900 | 0.0 | - |
| 0.4339 | 16950 | 0.0 | - |
| 0.4352 | 17000 | 0.0 | - |
| 0.4365 | 17050 | 0.0 | - |
| 0.4378 | 17100 | 0.0 | - |
| 0.4390 | 17150 | 0.0 | - |
| 0.4403 | 17200 | 0.0 | - |
| 0.4416 | 17250 | 0.0 | - |
| 0.4429 | 17300 | 0.0 | - |
| 0.4442 | 17350 | 0.0 | - |
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| 0.4480 | 17500 | 0.0016 | - |
| 0.4493 | 17550 | 0.0 | - |
| 0.4506 | 17600 | 0.0 | - |
| 0.4518 | 17650 | 0.0 | - |
| 0.4531 | 17700 | 0.0 | - |
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| 0.4570 | 17850 | 0.0 | - |
| 0.4582 | 17900 | 0.0 | - |
| 0.4595 | 17950 | 0.0068 | - |
| 0.4608 | 18000 | 0.0001 | - |
| 0.4621 | 18050 | 0.0001 | - |
| 0.4634 | 18100 | 0.0001 | - |
| 0.4646 | 18150 | 0.0001 | - |
| 0.4659 | 18200 | 0.0001 | - |
| 0.4672 | 18250 | 0.0 | - |
| 0.4685 | 18300 | 0.0 | - |
| 0.4698 | 18350 | 0.0001 | - |
| 0.4710 | 18400 | 0.0 | - |
| 0.4723 | 18450 | 0.0 | - |
| 0.4736 | 18500 | 0.0 | - |
| 0.4749 | 18550 | 0.0 | - |
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| 0.4992 | 19500 | 0.0 | - |
| 0.5005 | 19550 | 0.0 | - |
| 0.5018 | 19600 | 0.0 | - |
| 0.5030 | 19650 | 0.0 | - |
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| 0.5056 | 19750 | 0.0 | - |
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| 0.5082 | 19850 | 0.0 | - |
| 0.5094 | 19900 | 0.0 | - |
| 0.5107 | 19950 | 0.0 | - |
| 0.5120 | 20000 | 0.0 | - |
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| 0.5146 | 20100 | 0.0 | - |
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| 0.5171 | 20200 | 0.0 | - |
| 0.5184 | 20250 | 0.0 | - |
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| 0.5235 | 20450 | 0.0 | - |
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| 0.5286 | 20650 | 0.0 | - |
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| 0.5338 | 20850 | 0.0 | - |
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| 0.5363 | 20950 | 0.0 | - |
| 0.5376 | 21000 | 0.0 | - |
| 0.5389 | 21050 | 0.0 | - |
| 0.5402 | 21100 | 0.0 | - |
| 0.5414 | 21150 | 0.0 | - |
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| 0.5645 | 22050 | 0.0 | - |
| 0.5658 | 22100 | 0.0 | - |
| 0.5670 | 22150 | 0.0 | - |
| 0.5683 | 22200 | 0.0 | - |
| 0.5696 | 22250 | 0.0 | - |
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| 0.5773 | 22550 | 0.0 | - |
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| 0.5875 | 22950 | 0.0 | - |
| 0.5888 | 23000 | 0.0 | - |
| 0.5901 | 23050 | 0.0 | - |
| 0.5914 | 23100 | 0.0 | - |
| 0.5926 | 23150 | 0.0 | - |
| 0.5939 | 23200 | 0.0 | - |
| 0.5952 | 23250 | 0.0 | - |
| 0.5965 | 23300 | 0.0 | - |
| 0.5978 | 23350 | 0.0 | - |
| 0.5990 | 23400 | 0.0 | - |
| 0.6003 | 23450 | 0.0 | - |
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| 0.6042 | 23600 | 0.0 | - |
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| 0.6106 | 23850 | 0.0 | - |
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| 0.6131 | 23950 | 0.0 | - |
| 0.6144 | 24000 | 0.0 | - |
| 0.6157 | 24050 | 0.0 | - |
| 0.6170 | 24100 | 0.0 | - |
| 0.6182 | 24150 | 0.0 | - |
| 0.6195 | 24200 | 0.0 | - |
| 0.6208 | 24250 | 0.0 | - |
| 0.6221 | 24300 | 0.0 | - |
| 0.6234 | 24350 | 0.0 | - |
| 0.6246 | 24400 | 0.0 | - |
| 0.6259 | 24450 | 0.0 | - |
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| 0.6285 | 24550 | 0.0 | - |
| 0.6298 | 24600 | 0.0 | - |
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| 0.6323 | 24700 | 0.0 | - |
| 0.6336 | 24750 | 0.0 | - |
| 0.6349 | 24800 | 0.0 | - |
| 0.6362 | 24850 | 0.0 | - |
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| 0.6477 | 25300 | 0.0 | - |
| 0.6490 | 25350 | 0.0 | - |
| 0.6502 | 25400 | 0.0 | - |
| 0.6515 | 25450 | 0.0 | - |
| 0.6528 | 25500 | 0.0 | - |
| 0.6541 | 25550 | 0.0 | - |
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| 0.6592 | 25750 | 0.0 | - |
| 0.6605 | 25800 | 0.0 | - |
| 0.6618 | 25850 | 0.0 | - |
| 0.6630 | 25900 | 0.0 | - |
| 0.6643 | 25950 | 0.0 | - |
| 0.6656 | 26000 | 0.0 | - |
| 0.6669 | 26050 | 0.0 | - |
| 0.6682 | 26100 | 0.0 | - |
| 0.6694 | 26150 | 0.0 | - |
| 0.6707 | 26200 | 0.0 | - |
| 0.6720 | 26250 | 0.0 | - |
| 0.6733 | 26300 | 0.0 | - |
| 0.6746 | 26350 | 0.0 | - |
| 0.6758 | 26400 | 0.0 | - |
| 0.6771 | 26450 | 0.0 | - |
| 0.6784 | 26500 | 0.0 | - |
| 0.6797 | 26550 | 0.0 | - |
| 0.6810 | 26600 | 0.0 | - |
| 0.6822 | 26650 | 0.0 | - |
| 0.6835 | 26700 | 0.0 | - |
| 0.6848 | 26750 | 0.0 | - |
| 0.6861 | 26800 | 0.0 | - |
| 0.6874 | 26850 | 0.0 | - |
| 0.6886 | 26900 | 0.0 | - |
| 0.6899 | 26950 | 0.0 | - |
| 0.6912 | 27000 | 0.0 | - |
| 0.6925 | 27050 | 0.0 | - |
| 0.6938 | 27100 | 0.0 | - |
| 0.6950 | 27150 | 0.0 | - |
| 0.6963 | 27200 | 0.0 | - |
| 0.6976 | 27250 | 0.0 | - |
| 0.6989 | 27300 | 0.0 | - |
| 0.7002 | 27350 | 0.0 | - |
| 0.7014 | 27400 | 0.0 | - |
| 0.7027 | 27450 | 0.0 | - |
| 0.7040 | 27500 | 0.0 | - |
| 0.7053 | 27550 | 0.0 | - |
| 0.7066 | 27600 | 0.0 | - |
| 0.7078 | 27650 | 0.0 | - |
| 0.7091 | 27700 | 0.0 | - |
| 0.7104 | 27750 | 0.0 | - |
| 0.7117 | 27800 | 0.0 | - |
| 0.7130 | 27850 | 0.0 | - |
| 0.7142 | 27900 | 0.0 | - |
| 0.7155 | 27950 | 0.0 | - |
| 0.7168 | 28000 | 0.0 | - |
| 0.7181 | 28050 | 0.0 | - |
| 0.7194 | 28100 | 0.0 | - |
| 0.7206 | 28150 | 0.0 | - |
| 0.7219 | 28200 | 0.0 | - |
| 0.7232 | 28250 | 0.0 | - |
| 0.7245 | 28300 | 0.0 | - |
| 0.7258 | 28350 | 0.0 | - |
| 0.7270 | 28400 | 0.0 | - |
| 0.7283 | 28450 | 0.0 | - |
| 0.7296 | 28500 | 0.0 | - |
| 0.7309 | 28550 | 0.0 | - |
| 0.7322 | 28600 | 0.0 | - |
| 0.7334 | 28650 | 0.0 | - |
| 0.7347 | 28700 | 0.0 | - |
| 0.7360 | 28750 | 0.0 | - |
| 0.7373 | 28800 | 0.0 | - |
| 0.7386 | 28850 | 0.0 | - |
| 0.7398 | 28900 | 0.0 | - |
| 0.7411 | 28950 | 0.0 | - |
| 0.7424 | 29000 | 0.0 | - |
| 0.7437 | 29050 | 0.0 | - |
| 0.7450 | 29100 | 0.0 | - |
| 0.7462 | 29150 | 0.0 | - |
| 0.7475 | 29200 | 0.0 | - |
| 0.7488 | 29250 | 0.0 | - |
| 0.7501 | 29300 | 0.0 | - |
| 0.7514 | 29350 | 0.0 | - |
| 0.7526 | 29400 | 0.0 | - |
| 0.7539 | 29450 | 0.0 | - |
| 0.7552 | 29500 | 0.0 | - |
| 0.7565 | 29550 | 0.0 | - |
| 0.7578 | 29600 | 0.0 | - |
| 0.7590 | 29650 | 0.0 | - |
| 0.7603 | 29700 | 0.0 | - |
| 0.7616 | 29750 | 0.0 | - |
| 0.7629 | 29800 | 0.0 | - |
| 0.7642 | 29850 | 0.0 | - |
| 0.7654 | 29900 | 0.0 | - |
| 0.7667 | 29950 | 0.0 | - |
| 0.7680 | 30000 | 0.0 | - |
| 0.7693 | 30050 | 0.0 | - |
| 0.7706 | 30100 | 0.0 | - |
| 0.7718 | 30150 | 0.0 | - |
| 0.7731 | 30200 | 0.0 | - |
| 0.7744 | 30250 | 0.0 | - |
| 0.7757 | 30300 | 0.0 | - |
| 0.7770 | 30350 | 0.0 | - |
| 0.7782 | 30400 | 0.0 | - |
| 0.7795 | 30450 | 0.0 | - |
| 0.7808 | 30500 | 0.0 | - |
| 0.7821 | 30550 | 0.0 | - |
| 0.7833 | 30600 | 0.0 | - |
| 0.7846 | 30650 | 0.0 | - |
| 0.7859 | 30700 | 0.0 | - |
| 0.7872 | 30750 | 0.0 | - |
| 0.7885 | 30800 | 0.0 | - |
| 0.7897 | 30850 | 0.0 | - |
| 0.7910 | 30900 | 0.0 | - |
| 0.7923 | 30950 | 0.0 | - |
| 0.7936 | 31000 | 0.0 | - |
| 0.7949 | 31050 | 0.0 | - |
| 0.7961 | 31100 | 0.0 | - |
| 0.7974 | 31150 | 0.0 | - |
| 0.7987 | 31200 | 0.0 | - |
| 0.8000 | 31250 | 0.0 | - |
| 0.8013 | 31300 | 0.0 | - |
| 0.8025 | 31350 | 0.0 | - |
| 0.8038 | 31400 | 0.0 | - |
| 0.8051 | 31450 | 0.0 | - |
| 0.8064 | 31500 | 0.0 | - |
| 0.8077 | 31550 | 0.0 | - |
| 0.8089 | 31600 | 0.0 | - |
| 0.8102 | 31650 | 0.0 | - |
| 0.8115 | 31700 | 0.0 | - |
| 0.8128 | 31750 | 0.0 | - |
| 0.8141 | 31800 | 0.0 | - |
| 0.8153 | 31850 | 0.0 | - |
| 0.8166 | 31900 | 0.0 | - |
| 0.8179 | 31950 | 0.0 | - |
| 0.8192 | 32000 | 0.0 | - |
| 0.8205 | 32050 | 0.0 | - |
| 0.8217 | 32100 | 0.0 | - |
| 0.8230 | 32150 | 0.0 | - |
| 0.8243 | 32200 | 0.0 | - |
| 0.8256 | 32250 | 0.0 | - |
| 0.8269 | 32300 | 0.0 | - |
| 0.8281 | 32350 | 0.0 | - |
| 0.8294 | 32400 | 0.0 | - |
| 0.8307 | 32450 | 0.0 | - |
| 0.8320 | 32500 | 0.0 | - |
| 0.8333 | 32550 | 0.0 | - |
| 0.8345 | 32600 | 0.0 | - |
| 0.8358 | 32650 | 0.0 | - |
| 0.8371 | 32700 | 0.0 | - |
| 0.8384 | 32750 | 0.0 | - |
| 0.8397 | 32800 | 0.0 | - |
| 0.8409 | 32850 | 0.0 | - |
| 0.8422 | 32900 | 0.0 | - |
| 0.8435 | 32950 | 0.0 | - |
| 0.8448 | 33000 | 0.0 | - |
| 0.8461 | 33050 | 0.0 | - |
| 0.8473 | 33100 | 0.0 | - |
| 0.8486 | 33150 | 0.0 | - |
| 0.8499 | 33200 | 0.0 | - |
| 0.8512 | 33250 | 0.0 | - |
| 0.8525 | 33300 | 0.0 | - |
| 0.8537 | 33350 | 0.0 | - |
| 0.8550 | 33400 | 0.0 | - |
| 0.8563 | 33450 | 0.0 | - |
| 0.8576 | 33500 | 0.0 | - |
| 0.8589 | 33550 | 0.0 | - |
| 0.8601 | 33600 | 0.0 | - |
| 0.8614 | 33650 | 0.0 | - |
| 0.8627 | 33700 | 0.0 | - |
| 0.8640 | 33750 | 0.0 | - |
| 0.8653 | 33800 | 0.0 | - |
| 0.8665 | 33850 | 0.0 | - |
| 0.8678 | 33900 | 0.0 | - |
| 0.8691 | 33950 | 0.0 | - |
| 0.8704 | 34000 | 0.0 | - |
| 0.8717 | 34050 | 0.0 | - |
| 0.8729 | 34100 | 0.0 | - |
| 0.8742 | 34150 | 0.0 | - |
| 0.8755 | 34200 | 0.0 | - |
| 0.8768 | 34250 | 0.0 | - |
| 0.8781 | 34300 | 0.0 | - |
| 0.8793 | 34350 | 0.0 | - |
| 0.8806 | 34400 | 0.0 | - |
| 0.8819 | 34450 | 0.0 | - |
| 0.8832 | 34500 | 0.0 | - |
| 0.8845 | 34550 | 0.0 | - |
| 0.8857 | 34600 | 0.0 | - |
| 0.8870 | 34650 | 0.0 | - |
| 0.8883 | 34700 | 0.0 | - |
| 0.8896 | 34750 | 0.0 | - |
| 0.8909 | 34800 | 0.0 | - |
| 0.8921 | 34850 | 0.0 | - |
| 0.8934 | 34900 | 0.0 | - |
| 0.8947 | 34950 | 0.0 | - |
| 0.8960 | 35000 | 0.0 | - |
| 0.8973 | 35050 | 0.0 | - |
| 0.8985 | 35100 | 0.0 | - |
| 0.8998 | 35150 | 0.0 | - |
| 0.9011 | 35200 | 0.0 | - |
| 0.9024 | 35250 | 0.0 | - |
| 0.9037 | 35300 | 0.0 | - |
| 0.9049 | 35350 | 0.0 | - |
| 0.9062 | 35400 | 0.0 | - |
| 0.9075 | 35450 | 0.0 | - |
| 0.9088 | 35500 | 0.0 | - |
| 0.9101 | 35550 | 0.0 | - |
| 0.9113 | 35600 | 0.0 | - |
| 0.9126 | 35650 | 0.0 | - |
| 0.9139 | 35700 | 0.0 | - |
| 0.9152 | 35750 | 0.0 | - |
| 0.9165 | 35800 | 0.0 | - |
| 0.9177 | 35850 | 0.0 | - |
| 0.9190 | 35900 | 0.0 | - |
| 0.9203 | 35950 | 0.0 | - |
| 0.9216 | 36000 | 0.0 | - |
| 0.9229 | 36050 | 0.0 | - |
| 0.9241 | 36100 | 0.0 | - |
| 0.9254 | 36150 | 0.0 | - |
| 0.9267 | 36200 | 0.0 | - |
| 0.9280 | 36250 | 0.0 | - |
| 0.9293 | 36300 | 0.0 | - |
| 0.9305 | 36350 | 0.0 | - |
| 0.9318 | 36400 | 0.0 | - |
| 0.9331 | 36450 | 0.0 | - |
| 0.9344 | 36500 | 0.0 | - |
| 0.9357 | 36550 | 0.0 | - |
| 0.9369 | 36600 | 0.0 | - |
| 0.9382 | 36650 | 0.0 | - |
| 0.9395 | 36700 | 0.0 | - |
| 0.9408 | 36750 | 0.0 | - |
| 0.9421 | 36800 | 0.0 | - |
| 0.9433 | 36850 | 0.0 | - |
| 0.9446 | 36900 | 0.0 | - |
| 0.9459 | 36950 | 0.0 | - |
| 0.9472 | 37000 | 0.0 | - |
| 0.9485 | 37050 | 0.0 | - |
| 0.9497 | 37100 | 0.0 | - |
| 0.9510 | 37150 | 0.0 | - |
| 0.9523 | 37200 | 0.0 | - |
| 0.9536 | 37250 | 0.0 | - |
| 0.9549 | 37300 | 0.0 | - |
| 0.9561 | 37350 | 0.0 | - |
| 0.9574 | 37400 | 0.0 | - |
| 0.9587 | 37450 | 0.0 | - |
| 0.9600 | 37500 | 0.0 | - |
| 0.9613 | 37550 | 0.0 | - |
| 0.9625 | 37600 | 0.0 | - |
| 0.9638 | 37650 | 0.0 | - |
| 0.9651 | 37700 | 0.0 | - |
| 0.9664 | 37750 | 0.0 | - |
| 0.9677 | 37800 | 0.0 | - |
| 0.9689 | 37850 | 0.0 | - |
| 0.9702 | 37900 | 0.0 | - |
| 0.9715 | 37950 | 0.0 | - |
| 0.9728 | 38000 | 0.0 | - |
| 0.9741 | 38050 | 0.0 | - |
| 0.9753 | 38100 | 0.0 | - |
| 0.9766 | 38150 | 0.0 | - |
| 0.9779 | 38200 | 0.0 | - |
| 0.9792 | 38250 | 0.0 | - |
| 0.9805 | 38300 | 0.0 | - |
| 0.9817 | 38350 | 0.0 | - |
| 0.9830 | 38400 | 0.0 | - |
| 0.9843 | 38450 | 0.0 | - |
| 0.9856 | 38500 | 0.0 | - |
| 0.9869 | 38550 | 0.0 | - |
| 0.9881 | 38600 | 0.0 | - |
| 0.9894 | 38650 | 0.0 | - |
| 0.9907 | 38700 | 0.0 | - |
| 0.9920 | 38750 | 0.0 | - |
| 0.9933 | 38800 | 0.0 | - |
| 0.9945 | 38850 | 0.0 | - |
| 0.9958 | 38900 | 0.0 | - |
| 0.9971 | 38950 | 0.0 | - |
| 0.9984 | 39000 | 0.0 | - |
| 0.9997 | 39050 | 0.0 | - |
| **1.0** | **39063** | **-** | **0.4016** |
* 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}
}
```