setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:firqaaa/indo-sentence-bert-base base_model:finetune:firqaaa/indo-sentence-bert-base model-index region:us
---
base_model: firqaaa/indo-sentence-bert-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Aku sudah lebih tua dan hidupku sangat berbeda. Aku bisa merasakan betapa
    takjubnya aku pagi itu
- text: Saya merasa cukup href http kata-kata yang tak terucapkan disimpan di dalam
- text: Aku melihat ke dalam dompetku dan aku merasakan hawa dingin
- text: Aku menurunkan Erik dengan perasaan agak tidak puas dengan malam itu
- text: Aku bertanya-tanya apa yang siswa lain di kelasku rasakan ketika aku tidak
    takut untuk memberikan jawaban di luar sana
inference: true
model-index:
- name: SetFit with firqaaa/indo-sentence-bert-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: firqaaa/emotion-bahasa
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.718
      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:** 6 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label     | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| kesedihan | <ul><li>'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'</li><li>'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'</li><li>'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'</li></ul>                                                                  |
| sukacita  | <ul><li>'aku mungkin tidak merasa begitu keren'</li><li>'saya merasa baik-baik saja'</li><li>'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'</li></ul>                                                                                                                                                                                                                                                                 |
| cinta     | <ul><li>'aku merasa mencintaimu'</li><li>'aku akan merasa sangat nostalgia di usia yang begitu muda'</li><li>'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'</li></ul>                                                                                                                                                                                 |
| amarah    | <ul><li>'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'</li><li>'aku merasa memberontak sedikit kesal gila terkurung'</li><li>'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'</li></ul>                                             |
| takut     | <ul><li>'aku merasa seperti diserang oleh landak titanium'</li><li>'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'</li><li>'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'</li></ul> |
| kejutan   | <ul><li>'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'</li><li>'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'</li><li>'aku merasa penasaran, bersemangat dan tidak sabar'</li></ul>                                                                                                                                           |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.718    |

## 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-p3")
# Run inference
preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 16.7928 | 56  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| kesedihan | 300                   |
| sukacita  | 300                   |
| cinta     | 300                   |
| amarah    | 300                   |
| takut     | 300                   |
| kejutan   | 300                   |

### 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         | 0.2927        | -               |
| 0.0024  | 50        | 0.2605        | -               |
| 0.0047  | 100       | 0.2591        | -               |
| 0.0071  | 150       | 0.2638        | -               |
| 0.0095  | 200       | 0.245         | -               |
| 0.0119  | 250       | 0.226         | -               |
| 0.0142  | 300       | 0.222         | -               |
| 0.0166  | 350       | 0.1968        | -               |
| 0.0190  | 400       | 0.1703        | -               |
| 0.0213  | 450       | 0.1703        | -               |
| 0.0237  | 500       | 0.1587        | -               |
| 0.0261  | 550       | 0.1087        | -               |
| 0.0284  | 600       | 0.1203        | -               |
| 0.0308  | 650       | 0.0844        | -               |
| 0.0332  | 700       | 0.0696        | -               |
| 0.0356  | 750       | 0.0606        | -               |
| 0.0379  | 800       | 0.0333        | -               |
| 0.0403  | 850       | 0.0453        | -               |
| 0.0427  | 900       | 0.033         | -               |
| 0.0450  | 950       | 0.0142        | -               |
| 0.0474  | 1000      | 0.004         | -               |
| 0.0498  | 1050      | 0.0097        | -               |
| 0.0521  | 1100      | 0.0065        | -               |
| 0.0545  | 1150      | 0.0081        | -               |
| 0.0569  | 1200      | 0.0041        | -               |
| 0.0593  | 1250      | 0.0044        | -               |
| 0.0616  | 1300      | 0.0013        | -               |
| 0.0640  | 1350      | 0.0024        | -               |
| 0.0664  | 1400      | 0.001         | -               |
| 0.0687  | 1450      | 0.0012        | -               |
| 0.0711  | 1500      | 0.0013        | -               |
| 0.0735  | 1550      | 0.0006        | -               |
| 0.0759  | 1600      | 0.0033        | -               |
| 0.0782  | 1650      | 0.0006        | -               |
| 0.0806  | 1700      | 0.0013        | -               |
| 0.0830  | 1750      | 0.0008        | -               |
| 0.0853  | 1800      | 0.0006        | -               |
| 0.0877  | 1850      | 0.0008        | -               |
| 0.0901  | 1900      | 0.0004        | -               |
| 0.0924  | 1950      | 0.0005        | -               |
| 0.0948  | 2000      | 0.0004        | -               |
| 0.0972  | 2050      | 0.0002        | -               |
| 0.0996  | 2100      | 0.0002        | -               |
| 0.1019  | 2150      | 0.0003        | -               |
| 0.1043  | 2200      | 0.0006        | -               |
| 0.1067  | 2250      | 0.0005        | -               |
| 0.1090  | 2300      | 0.0003        | -               |
| 0.1114  | 2350      | 0.0018        | -               |
| 0.1138  | 2400      | 0.0003        | -               |
| 0.1161  | 2450      | 0.0002        | -               |
| 0.1185  | 2500      | 0.0018        | -               |
| 0.1209  | 2550      | 0.0003        | -               |
| 0.1233  | 2600      | 0.0008        | -               |
| 0.1256  | 2650      | 0.0002        | -               |
| 0.1280  | 2700      | 0.0007        | -               |
| 0.1304  | 2750      | 0.006         | -               |
| 0.1327  | 2800      | 0.0002        | -               |
| 0.1351  | 2850      | 0.0001        | -               |
| 0.1375  | 2900      | 0.0001        | -               |
| 0.1399  | 2950      | 0.0001        | -               |
| 0.1422  | 3000      | 0.0001        | -               |
| 0.1446  | 3050      | 0.0001        | -               |
| 0.1470  | 3100      | 0.0001        | -               |
| 0.1493  | 3150      | 0.0001        | -               |
| 0.1517  | 3200      | 0.0002        | -               |
| 0.1541  | 3250      | 0.0003        | -               |
| 0.1564  | 3300      | 0.0004        | -               |
| 0.1588  | 3350      | 0.0001        | -               |
| 0.1612  | 3400      | 0.0001        | -               |
| 0.1636  | 3450      | 0.0014        | -               |
| 0.1659  | 3500      | 0.0005        | -               |
| 0.1683  | 3550      | 0.0003        | -               |
| 0.1707  | 3600      | 0.0001        | -               |
| 0.1730  | 3650      | 0.0001        | -               |
| 0.1754  | 3700      | 0.0001        | -               |
| 0.1778  | 3750      | 0.0001        | -               |
| 0.1801  | 3800      | 0.0001        | -               |
| 0.1825  | 3850      | 0.0001        | -               |
| 0.1849  | 3900      | 0.0001        | -               |
| 0.1873  | 3950      | 0.0001        | -               |
| 0.1896  | 4000      | 0.0001        | -               |
| 0.1920  | 4050      | 0.0001        | -               |
| 0.1944  | 4100      | 0.0003        | -               |
| 0.1967  | 4150      | 0.0006        | -               |
| 0.1991  | 4200      | 0.0001        | -               |
| 0.2015  | 4250      | 0.0           | -               |
| 0.2038  | 4300      | 0.0           | -               |
| 0.2062  | 4350      | 0.0001        | -               |
| 0.2086  | 4400      | 0.0           | -               |
| 0.2110  | 4450      | 0.0           | -               |
| 0.2133  | 4500      | 0.0001        | -               |
| 0.2157  | 4550      | 0.0002        | -               |
| 0.2181  | 4600      | 0.0003        | -               |
| 0.2204  | 4650      | 0.0018        | -               |
| 0.2228  | 4700      | 0.0003        | -               |
| 0.2252  | 4750      | 0.0145        | -               |
| 0.2276  | 4800      | 0.0001        | -               |
| 0.2299  | 4850      | 0.0006        | -               |
| 0.2323  | 4900      | 0.0001        | -               |
| 0.2347  | 4950      | 0.0007        | -               |
| 0.2370  | 5000      | 0.0001        | -               |
| 0.2394  | 5050      | 0.0           | -               |
| 0.2418  | 5100      | 0.0           | -               |
| 0.2441  | 5150      | 0.0001        | -               |
| 0.2465  | 5200      | 0.0003        | -               |
| 0.2489  | 5250      | 0.0           | -               |
| 0.2513  | 5300      | 0.0           | -               |
| 0.2536  | 5350      | 0.0           | -               |
| 0.2560  | 5400      | 0.0           | -               |
| 0.2584  | 5450      | 0.0004        | -               |
| 0.2607  | 5500      | 0.0           | -               |
| 0.2631  | 5550      | 0.0           | -               |
| 0.2655  | 5600      | 0.0           | -               |
| 0.2678  | 5650      | 0.0           | -               |
| 0.2702  | 5700      | 0.0           | -               |
| 0.2726  | 5750      | 0.0002        | -               |
| 0.2750  | 5800      | 0.0           | -               |
| 0.2773  | 5850      | 0.0           | -               |
| 0.2797  | 5900      | 0.0           | -               |
| 0.2821  | 5950      | 0.0           | -               |
| 0.2844  | 6000      | 0.0           | -               |
| 0.2868  | 6050      | 0.0           | -               |
| 0.2892  | 6100      | 0.0           | -               |
| 0.2916  | 6150      | 0.0           | -               |
| 0.2939  | 6200      | 0.0           | -               |
| 0.2963  | 6250      | 0.0           | -               |
| 0.2987  | 6300      | 0.0001        | -               |
| 0.3010  | 6350      | 0.0003        | -               |
| 0.3034  | 6400      | 0.0048        | -               |
| 0.3058  | 6450      | 0.0           | -               |
| 0.3081  | 6500      | 0.0           | -               |
| 0.3105  | 6550      | 0.0           | -               |
| 0.3129  | 6600      | 0.0           | -               |
| 0.3153  | 6650      | 0.0           | -               |
| 0.3176  | 6700      | 0.0           | -               |
| 0.3200  | 6750      | 0.0           | -               |
| 0.3224  | 6800      | 0.0           | -               |
| 0.3247  | 6850      | 0.0           | -               |
| 0.3271  | 6900      | 0.0           | -               |
| 0.3295  | 6950      | 0.0           | -               |
| 0.3318  | 7000      | 0.0           | -               |
| 0.3342  | 7050      | 0.0           | -               |
| 0.3366  | 7100      | 0.0           | -               |
| 0.3390  | 7150      | 0.0011        | -               |
| 0.3413  | 7200      | 0.0002        | -               |
| 0.3437  | 7250      | 0.0           | -               |
| 0.3461  | 7300      | 0.0           | -               |
| 0.3484  | 7350      | 0.0001        | -               |
| 0.3508  | 7400      | 0.0001        | -               |
| 0.3532  | 7450      | 0.0002        | -               |
| 0.3556  | 7500      | 0.0           | -               |
| 0.3579  | 7550      | 0.0           | -               |
| 0.3603  | 7600      | 0.0           | -               |
| 0.3627  | 7650      | 0.0           | -               |
| 0.3650  | 7700      | 0.0           | -               |
| 0.3674  | 7750      | 0.0           | -               |
| 0.3698  | 7800      | 0.0001        | -               |
| 0.3721  | 7850      | 0.0           | -               |
| 0.3745  | 7900      | 0.0           | -               |
| 0.3769  | 7950      | 0.0           | -               |
| 0.3793  | 8000      | 0.0           | -               |
| 0.3816  | 8050      | 0.0           | -               |
| 0.3840  | 8100      | 0.0           | -               |
| 0.3864  | 8150      | 0.0           | -               |
| 0.3887  | 8200      | 0.0           | -               |
| 0.3911  | 8250      | 0.0           | -               |
| 0.3935  | 8300      | 0.0           | -               |
| 0.3958  | 8350      | 0.0           | -               |
| 0.3982  | 8400      | 0.0           | -               |
| 0.4006  | 8450      | 0.0           | -               |
| 0.4030  | 8500      | 0.0           | -               |
| 0.4053  | 8550      | 0.0001        | -               |
| 0.4077  | 8600      | 0.0001        | -               |
| 0.4101  | 8650      | 0.0008        | -               |
| 0.4124  | 8700      | 0.0001        | -               |
| 0.4148  | 8750      | 0.0           | -               |
| 0.4172  | 8800      | 0.0           | -               |
| 0.4196  | 8850      | 0.0001        | -               |
| 0.4219  | 8900      | 0.0           | -               |
| 0.4243  | 8950      | 0.0           | -               |
| 0.4267  | 9000      | 0.0           | -               |
| 0.4290  | 9050      | 0.0           | -               |
| 0.4314  | 9100      | 0.0           | -               |
| 0.4338  | 9150      | 0.0           | -               |
| 0.4361  | 9200      | 0.0           | -               |
| 0.4385  | 9250      | 0.0           | -               |
| 0.4409  | 9300      | 0.0           | -               |
| 0.4433  | 9350      | 0.0           | -               |
| 0.4456  | 9400      | 0.0           | -               |
| 0.4480  | 9450      | 0.0           | -               |
| 0.4504  | 9500      | 0.0           | -               |
| 0.4527  | 9550      | 0.0           | -               |
| 0.4551  | 9600      | 0.0           | -               |
| 0.4575  | 9650      | 0.0           | -               |
| 0.4598  | 9700      | 0.0           | -               |
| 0.4622  | 9750      | 0.0001        | -               |
| 0.4646  | 9800      | 0.0           | -               |
| 0.4670  | 9850      | 0.0           | -               |
| 0.4693  | 9900      | 0.0           | -               |
| 0.4717  | 9950      | 0.0           | -               |
| 0.4741  | 10000     | 0.0           | -               |
| 0.4764  | 10050     | 0.0           | -               |
| 0.4788  | 10100     | 0.0006        | -               |
| 0.4812  | 10150     | 0.0           | -               |
| 0.4835  | 10200     | 0.0           | -               |
| 0.4859  | 10250     | 0.0           | -               |
| 0.4883  | 10300     | 0.0           | -               |
| 0.4907  | 10350     | 0.0           | -               |
| 0.4930  | 10400     | 0.0           | -               |
| 0.4954  | 10450     | 0.0           | -               |
| 0.4978  | 10500     | 0.0           | -               |
| 0.5001  | 10550     | 0.0           | -               |
| 0.5025  | 10600     | 0.0           | -               |
| 0.5049  | 10650     | 0.0           | -               |
| 0.5073  | 10700     | 0.0           | -               |
| 0.5096  | 10750     | 0.0           | -               |
| 0.5120  | 10800     | 0.0           | -               |
| 0.5144  | 10850     | 0.0           | -               |
| 0.5167  | 10900     | 0.0           | -               |
| 0.5191  | 10950     | 0.0           | -               |
| 0.5215  | 11000     | 0.0           | -               |
| 0.5238  | 11050     | 0.0           | -               |
| 0.5262  | 11100     | 0.0           | -               |
| 0.5286  | 11150     | 0.0           | -               |
| 0.5310  | 11200     | 0.0           | -               |
| 0.5333  | 11250     | 0.0           | -               |
| 0.5357  | 11300     | 0.0           | -               |
| 0.5381  | 11350     | 0.0           | -               |
| 0.5404  | 11400     | 0.0           | -               |
| 0.5428  | 11450     | 0.0           | -               |
| 0.5452  | 11500     | 0.0           | -               |
| 0.5475  | 11550     | 0.0           | -               |
| 0.5499  | 11600     | 0.0           | -               |
| 0.5523  | 11650     | 0.0001        | -               |
| 0.5547  | 11700     | 0.0           | -               |
| 0.5570  | 11750     | 0.0043        | -               |
| 0.5594  | 11800     | 0.0           | -               |
| 0.5618  | 11850     | 0.0           | -               |
| 0.5641  | 11900     | 0.0           | -               |
| 0.5665  | 11950     | 0.0           | -               |
| 0.5689  | 12000     | 0.0           | -               |
| 0.5713  | 12050     | 0.0           | -               |
| 0.5736  | 12100     | 0.0           | -               |
| 0.5760  | 12150     | 0.0           | -               |
| 0.5784  | 12200     | 0.0           | -               |
| 0.5807  | 12250     | 0.0029        | -               |
| 0.5831  | 12300     | 0.0           | -               |
| 0.5855  | 12350     | 0.0           | -               |
| 0.5878  | 12400     | 0.0           | -               |
| 0.5902  | 12450     | 0.0           | -               |
| 0.5926  | 12500     | 0.0           | -               |
| 0.5950  | 12550     | 0.0           | -               |
| 0.5973  | 12600     | 0.0           | -               |
| 0.5997  | 12650     | 0.0           | -               |
| 0.6021  | 12700     | 0.0           | -               |
| 0.6044  | 12750     | 0.0           | -               |
| 0.6068  | 12800     | 0.0           | -               |
| 0.6092  | 12850     | 0.0           | -               |
| 0.6115  | 12900     | 0.0           | -               |
| 0.6139  | 12950     | 0.0           | -               |
| 0.6163  | 13000     | 0.0           | -               |
| 0.6187  | 13050     | 0.0           | -               |
| 0.6210  | 13100     | 0.0           | -               |
| 0.6234  | 13150     | 0.0001        | -               |
| 0.6258  | 13200     | 0.0           | -               |
| 0.6281  | 13250     | 0.0           | -               |
| 0.6305  | 13300     | 0.0           | -               |
| 0.6329  | 13350     | 0.0           | -               |
| 0.6353  | 13400     | 0.0001        | -               |
| 0.6376  | 13450     | 0.0           | -               |
| 0.6400  | 13500     | 0.0           | -               |
| 0.6424  | 13550     | 0.0           | -               |
| 0.6447  | 13600     | 0.0           | -               |
| 0.6471  | 13650     | 0.0           | -               |
| 0.6495  | 13700     | 0.0           | -               |
| 0.6518  | 13750     | 0.0           | -               |
| 0.6542  | 13800     | 0.0           | -               |
| 0.6566  | 13850     | 0.0           | -               |
| 0.6590  | 13900     | 0.0           | -               |
| 0.6613  | 13950     | 0.0           | -               |
| 0.6637  | 14000     | 0.0           | -               |
| 0.6661  | 14050     | 0.0           | -               |
| 0.6684  | 14100     | 0.0           | -               |
| 0.6708  | 14150     | 0.0           | -               |
| 0.6732  | 14200     | 0.0           | -               |
| 0.6755  | 14250     | 0.0           | -               |
| 0.6779  | 14300     | 0.0           | -               |
| 0.6803  | 14350     | 0.0           | -               |
| 0.6827  | 14400     | 0.0           | -               |
| 0.6850  | 14450     | 0.0           | -               |
| 0.6874  | 14500     | 0.0           | -               |
| 0.6898  | 14550     | 0.0           | -               |
| 0.6921  | 14600     | 0.0           | -               |
| 0.6945  | 14650     | 0.0           | -               |
| 0.6969  | 14700     | 0.0           | -               |
| 0.6993  | 14750     | 0.0           | -               |
| 0.7016  | 14800     | 0.0           | -               |
| 0.7040  | 14850     | 0.0           | -               |
| 0.7064  | 14900     | 0.0           | -               |
| 0.7087  | 14950     | 0.0           | -               |
| 0.7111  | 15000     | 0.0           | -               |
| 0.7135  | 15050     | 0.0           | -               |
| 0.7158  | 15100     | 0.0           | -               |
| 0.7182  | 15150     | 0.0           | -               |
| 0.7206  | 15200     | 0.0           | -               |
| 0.7230  | 15250     | 0.0           | -               |
| 0.7253  | 15300     | 0.0           | -               |
| 0.7277  | 15350     | 0.0           | -               |
| 0.7301  | 15400     | 0.0           | -               |
| 0.7324  | 15450     | 0.0           | -               |
| 0.7348  | 15500     | 0.0           | -               |
| 0.7372  | 15550     | 0.0           | -               |
| 0.7395  | 15600     | 0.0           | -               |
| 0.7419  | 15650     | 0.0           | -               |
| 0.7443  | 15700     | 0.0           | -               |
| 0.7467  | 15750     | 0.0           | -               |
| 0.7490  | 15800     | 0.0           | -               |
| 0.7514  | 15850     | 0.0           | -               |
| 0.7538  | 15900     | 0.0           | -               |
| 0.7561  | 15950     | 0.0           | -               |
| 0.7585  | 16000     | 0.0           | -               |
| 0.7609  | 16050     | 0.0           | -               |
| 0.7633  | 16100     | 0.0           | -               |
| 0.7656  | 16150     | 0.0           | -               |
| 0.7680  | 16200     | 0.0           | -               |
| 0.7704  | 16250     | 0.0           | -               |
| 0.7727  | 16300     | 0.0           | -               |
| 0.7751  | 16350     | 0.0           | -               |
| 0.7775  | 16400     | 0.0           | -               |
| 0.7798  | 16450     | 0.0           | -               |
| 0.7822  | 16500     | 0.0           | -               |
| 0.7846  | 16550     | 0.0           | -               |
| 0.7870  | 16600     | 0.0           | -               |
| 0.7893  | 16650     | 0.0           | -               |
| 0.7917  | 16700     | 0.0           | -               |
| 0.7941  | 16750     | 0.0           | -               |
| 0.7964  | 16800     | 0.0           | -               |
| 0.7988  | 16850     | 0.0           | -               |
| 0.8012  | 16900     | 0.0           | -               |
| 0.8035  | 16950     | 0.0           | -               |
| 0.8059  | 17000     | 0.0           | -               |
| 0.8083  | 17050     | 0.0           | -               |
| 0.8107  | 17100     | 0.0           | -               |
| 0.8130  | 17150     | 0.0           | -               |
| 0.8154  | 17200     | 0.0           | -               |
| 0.8178  | 17250     | 0.0           | -               |
| 0.8201  | 17300     | 0.0           | -               |
| 0.8225  | 17350     | 0.0           | -               |
| 0.8249  | 17400     | 0.0           | -               |
| 0.8272  | 17450     | 0.0           | -               |
| 0.8296  | 17500     | 0.0           | -               |
| 0.8320  | 17550     | 0.0           | -               |
| 0.8344  | 17600     | 0.0           | -               |
| 0.8367  | 17650     | 0.0           | -               |
| 0.8391  | 17700     | 0.0           | -               |
| 0.8415  | 17750     | 0.0           | -               |
| 0.8438  | 17800     | 0.0           | -               |
| 0.8462  | 17850     | 0.0           | -               |
| 0.8486  | 17900     | 0.0           | -               |
| 0.8510  | 17950     | 0.0           | -               |
| 0.8533  | 18000     | 0.0           | -               |
| 0.8557  | 18050     | 0.0           | -               |
| 0.8581  | 18100     | 0.0           | -               |
| 0.8604  | 18150     | 0.0           | -               |
| 0.8628  | 18200     | 0.0           | -               |
| 0.8652  | 18250     | 0.0           | -               |
| 0.8675  | 18300     | 0.0           | -               |
| 0.8699  | 18350     | 0.0           | -               |
| 0.8723  | 18400     | 0.0           | -               |
| 0.8747  | 18450     | 0.0           | -               |
| 0.8770  | 18500     | 0.0           | -               |
| 0.8794  | 18550     | 0.0           | -               |
| 0.8818  | 18600     | 0.0           | -               |
| 0.8841  | 18650     | 0.0           | -               |
| 0.8865  | 18700     | 0.0           | -               |
| 0.8889  | 18750     | 0.0           | -               |
| 0.8912  | 18800     | 0.0           | -               |
| 0.8936  | 18850     | 0.0           | -               |
| 0.8960  | 18900     | 0.0           | -               |
| 0.8984  | 18950     | 0.0           | -               |
| 0.9007  | 19000     | 0.0           | -               |
| 0.9031  | 19050     | 0.0           | -               |
| 0.9055  | 19100     | 0.0           | -               |
| 0.9078  | 19150     | 0.0           | -               |
| 0.9102  | 19200     | 0.0           | -               |
| 0.9126  | 19250     | 0.0           | -               |
| 0.9150  | 19300     | 0.0           | -               |
| 0.9173  | 19350     | 0.0           | -               |
| 0.9197  | 19400     | 0.0           | -               |
| 0.9221  | 19450     | 0.0           | -               |
| 0.9244  | 19500     | 0.0           | -               |
| 0.9268  | 19550     | 0.0           | -               |
| 0.9292  | 19600     | 0.0           | -               |
| 0.9315  | 19650     | 0.0           | -               |
| 0.9339  | 19700     | 0.0           | -               |
| 0.9363  | 19750     | 0.0           | -               |
| 0.9387  | 19800     | 0.0           | -               |
| 0.9410  | 19850     | 0.0           | -               |
| 0.9434  | 19900     | 0.0           | -               |
| 0.9458  | 19950     | 0.0           | -               |
| 0.9481  | 20000     | 0.0           | -               |
| 0.9505  | 20050     | 0.0           | -               |
| 0.9529  | 20100     | 0.0           | -               |
| 0.9552  | 20150     | 0.0           | -               |
| 0.9576  | 20200     | 0.0           | -               |
| 0.9600  | 20250     | 0.0           | -               |
| 0.9624  | 20300     | 0.0           | -               |
| 0.9647  | 20350     | 0.0           | -               |
| 0.9671  | 20400     | 0.0           | -               |
| 0.9695  | 20450     | 0.0           | -               |
| 0.9718  | 20500     | 0.0           | -               |
| 0.9742  | 20550     | 0.0           | -               |
| 0.9766  | 20600     | 0.0           | -               |
| 0.9790  | 20650     | 0.0           | -               |
| 0.9813  | 20700     | 0.0           | -               |
| 0.9837  | 20750     | 0.0           | -               |
| 0.9861  | 20800     | 0.0           | -               |
| 0.9884  | 20850     | 0.0           | -               |
| 0.9908  | 20900     | 0.0           | -               |
| 0.9932  | 20950     | 0.0           | -               |
| 0.9955  | 21000     | 0.0           | -               |
| 0.9979  | 21050     | 0.0           | -               |
| **1.0** | **21094** | **-**         | **0.2251**      |

* 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}
}
```

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