
# 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
<!-- - **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:--------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positif | <ul><li>'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.'</li><li>'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.'</li><li>'mengerikan dalam potret obyektif Amerika abad kedua puluh satu yang suram dan hilang.'</li></ul> |
| negatif | <ul><li>'dengan hari-hari anjing di bulan Agustus yang akan datang, anggaplah film anjing ini setara dengan sinematik dengan kelembapan tinggi.'</li><li>'itu kelam dan mudah ditebak, dan tidak banyak yang bisa tertawa.'</li><li>'pencapaian film yang paling mustahil?'</li></ul>                                                                                                                                                                                                                                                                                                  |

## 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.")
```

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### Downstream Use

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

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

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<!--
## 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.*
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### Recommendations

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## 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           | -               |
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| 0.2267  | 3550      | 0.0           | -               |
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| 0.2331  | 3650      | 0.021         | -               |
| 0.2363  | 3700      | 0.0001        | -               |
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| 0.2587  | 4050      | 0.0001        | -               |
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| 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        | -               |
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| 0.2938  | 4600      | 0.0002        | -               |
| 0.2970  | 4650      | 0.0           | -               |
| 0.3002  | 4700      | 0.0305        | -               |
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| 0.3736  | 5850      | 0.0001        | -               |
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| 0.3832  | 6000      | 0.0           | -               |
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| 0.3928  | 6150      | 0.0001        | -               |
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| 0.3992  | 6250      | 0.0           | -               |
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| 0.5620  | 8800      | 0.0           | -               |
| 0.5652  | 8850      | 0.0           | -               |
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| 0.9037  | 14150     | 0.0           | -               |
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| 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           | -               |
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| 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}
}
```

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{"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 safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:firqaaa/indo-sentence-bert-base model-index region:us