---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- f1
widget:
- text: Kerajaan bukannya memandai-mandai buat itu ini, sebaliknya yang dilaksanakan
adalah bagi penuhi permintaan atau cadangan diterima daripada peringkat bawahan
sendiri
- text: mahathir mohamad demi kelangsungan karier politiknya lebih-lebih lagi bekas
perdana menteri itu masih lagi mempunyai pengikut yang taksub
- text: '@AINAMIR96 Bukan..kalau letak mmg lah melecur..ambik towel kecik..iron bg
panas..lepas tu tuam lah kat perut towel https://t.co/pAw4o5vr5I'
- text: Kebanyakan orang Cina yang kamu temui di sini akan beritahu kamu, kebimbangan
mereka tentang isu yang melanda negara.
- text: WTB | WHAT TO BUY TAEIL BATU AKIK ( FIRE TRUCK ) dm aku yaa chagi kali aja
ada yg mau jual taeilnya ini bener https://t.co/el2UKgB3j4
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.608
name: F1
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 3 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 |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | - 'Biasanya cuma janji saja tapi tak pernah buat pun'
- 'Aku faham kalau diorang buat macam ni sebab kalau aku jadi doktor pun aku penat la kimak. Bodoh punya kerajaan !'
- 'Jika parlimen tidak dipanggil dalam masa 14 hari maka UMNO akan keluar menjadi pembangkang iaitu lebih kurang 4 Jul https://t.co/AvJSf1F8ux'
|
| positive | - 'Kek telapuk kuda, kek lembut rasa premium! Tgh kumpoi order ni utk warga Sungai Petani. Tak terlioq ka tengok? Mai https://t.co/wgcEADvxQK'
- 'Kalau projek ini berjalan, peneroka Felda tak payah masuk kebun lagi, harga sawit baru RM500 1 tan, murah.'
- 'Justeru, kita berharap lebih banyak pusat rawatan seperti ini diwujudkan untuk membantu.'
|
| neutral | - '08.05 WIB #Jalan_Layang_MBZ Cikunir - Tambun - Cikarang - Karawang LANCAR. ; Karawang - Cikarang - Tambun - Cikunir LANCAR.'
- '5) Menilai Kualiti Kandungan. Selepas dah faham apa yang penulis cuba sampaikan, kita boleh nilai sama ada ia bena https://t.co/LnkjtBc3Nm'
- '@syafirazbd_ Siapp'
|
## Evaluation
### Metrics
| Label | F1 |
|:--------|:------|
| **all** | 0.608 |
## 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("babysharkdododo/setfit-all-minilm-l6-v2-malay_en_cn_sentiment_analysis")
# Run inference
preds = model("Kebanyakan orang Cina yang kamu temui di sini akan beritahu kamu, kebimbangan mereka tentang isu yang melanda negara.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 16.598 | 58 |
| Label | Training Sample Count |
|:---------|:----------------------|
| positive | 235 |
| neutral | 77 |
| negative | 188 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (10, 10)
- 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 | 0.189 | - |
| 0.0052 | 50 | 0.3053 | - |
| 0.0104 | 100 | 0.2779 | - |
| 0.0156 | 150 | 0.4526 | - |
| 0.0208 | 200 | 0.3073 | - |
| 0.0261 | 250 | 0.4156 | - |
| 0.0313 | 300 | 0.3912 | - |
| 0.0365 | 350 | 0.2259 | - |
| 0.0417 | 400 | 0.2445 | - |
| 0.0004 | 1 | 0.3311 | - |
| 0.0208 | 50 | 0.3569 | - |
| 0.0417 | 100 | 0.2946 | - |
| 0.0625 | 150 | 0.3397 | - |
| 0.0008 | 1 | 0.3177 | - |
| 0.0417 | 50 | 0.2764 | - |
| 0.0833 | 100 | 0.226 | - |
| 0.125 | 150 | 0.2649 | - |
| 0.1667 | 200 | 0.282 | - |
| 0.2083 | 250 | 0.2438 | - |
| 0.25 | 300 | 0.2425 | - |
| 0.2917 | 350 | 0.2555 | - |
| 0.3333 | 400 | 0.2266 | - |
| 0.375 | 450 | 0.14 | - |
| 0.4167 | 500 | 0.1446 | - |
| 0.4583 | 550 | 0.152 | - |
| 0.5 | 600 | 0.184 | - |
| 0.5417 | 650 | 0.095 | - |
| 0.5833 | 700 | 0.1358 | - |
| 0.625 | 750 | 0.0859 | - |
| 0.6667 | 800 | 0.0756 | - |
| 0.7083 | 850 | 0.0622 | - |
| 0.75 | 900 | 0.0719 | - |
| 0.7917 | 950 | 0.0681 | - |
| 0.8333 | 1000 | 0.0684 | - |
| 0.875 | 1050 | 0.0356 | - |
| 0.9167 | 1100 | 0.0233 | - |
| 0.9583 | 1150 | 0.0126 | - |
| 1.0 | 1200 | 0.0022 | 0.3748 |
| 1.0417 | 1250 | 0.0095 | - |
| 1.0833 | 1300 | 0.0095 | - |
| 1.125 | 1350 | 0.0376 | - |
| 1.1667 | 1400 | 0.0075 | - |
| 1.2083 | 1450 | 0.0075 | - |
| 1.25 | 1500 | 0.0142 | - |
| 1.2917 | 1550 | 0.0113 | - |
| 1.3333 | 1600 | 0.0022 | - |
| 1.375 | 1650 | 0.0006 | - |
| 1.4167 | 1700 | 0.0005 | - |
| 1.4583 | 1750 | 0.0005 | - |
| 1.5 | 1800 | 0.0003 | - |
| 1.5417 | 1850 | 0.0021 | - |
| 1.5833 | 1900 | 0.0004 | - |
| 1.625 | 1950 | 0.0006 | - |
| 1.6667 | 2000 | 0.001 | - |
| 1.7083 | 2050 | 0.0002 | - |
| 1.75 | 2100 | 0.0002 | - |
| 1.7917 | 2150 | 0.0002 | - |
| 1.8333 | 2200 | 0.0002 | - |
| 1.875 | 2250 | 0.0059 | - |
| 1.9167 | 2300 | 0.0002 | - |
| 1.9583 | 2350 | 0.0005 | - |
| 2.0 | 2400 | 0.0001 | 0.3806 |
| 2.0417 | 2450 | 0.0001 | - |
| 2.0833 | 2500 | 0.0012 | - |
| 2.125 | 2550 | 0.0001 | - |
| 2.1667 | 2600 | 0.0002 | - |
| 2.2083 | 2650 | 0.0002 | - |
| 2.25 | 2700 | 0.0001 | - |
| 2.2917 | 2750 | 0.0011 | - |
| 2.3333 | 2800 | 0.0002 | - |
| 2.375 | 2850 | 0.0001 | - |
| 2.4167 | 2900 | 0.0003 | - |
| 2.4583 | 2950 | 0.0007 | - |
| 2.5 | 3000 | 0.0001 | - |
| 2.5417 | 3050 | 0.0001 | - |
| 2.5833 | 3100 | 0.0001 | - |
| 2.625 | 3150 | 0.0001 | - |
| 2.6667 | 3200 | 0.0001 | - |
| 2.7083 | 3250 | 0.0001 | - |
| 2.75 | 3300 | 0.0001 | - |
| 2.7917 | 3350 | 0.0002 | - |
| 2.8333 | 3400 | 0.0001 | - |
| 2.875 | 3450 | 0.0001 | - |
| 2.9167 | 3500 | 0.0001 | - |
| 2.9583 | 3550 | 0.0001 | - |
| 3.0 | 3600 | 0.0001 | 0.4004 |
| 3.0417 | 3650 | 0.0001 | - |
| 3.0833 | 3700 | 0.0001 | - |
| 3.125 | 3750 | 0.0001 | - |
| 3.1667 | 3800 | 0.0001 | - |
| 3.2083 | 3850 | 0.0002 | - |
| 3.25 | 3900 | 0.0001 | - |
| 3.2917 | 3950 | 0.0001 | - |
| 3.3333 | 4000 | 0.0005 | - |
| 3.375 | 4050 | 0.0001 | - |
| 3.4167 | 4100 | 0.0001 | - |
| 3.4583 | 4150 | 0.0001 | - |
| 3.5 | 4200 | 0.0004 | - |
| 3.5417 | 4250 | 0.0 | - |
| 3.5833 | 4300 | 0.0001 | - |
| 3.625 | 4350 | 0.0001 | - |
| 3.6667 | 4400 | 0.0001 | - |
| 3.7083 | 4450 | 0.0001 | - |
| 3.75 | 4500 | 0.0 | - |
| 3.7917 | 4550 | 0.0 | - |
| 3.8333 | 4600 | 0.0 | - |
| 3.875 | 4650 | 0.0001 | - |
| 3.9167 | 4700 | 0.0001 | - |
| 3.9583 | 4750 | 0.0001 | - |
| 4.0 | 4800 | 0.0 | 0.4004 |
| 4.0417 | 4850 | 0.0001 | - |
| 4.0833 | 4900 | 0.0003 | - |
| 4.125 | 4950 | 0.0 | - |
| 4.1667 | 5000 | 0.0001 | - |
| 4.2083 | 5050 | 0.0001 | - |
| 4.25 | 5100 | 0.0 | - |
| 4.2917 | 5150 | 0.0003 | - |
| 4.3333 | 5200 | 0.0001 | - |
| 4.375 | 5250 | 0.0 | - |
| 4.4167 | 5300 | 0.0 | - |
| 4.4583 | 5350 | 0.0002 | - |
| 4.5 | 5400 | 0.0 | - |
| 4.5417 | 5450 | 0.0001 | - |
| 4.5833 | 5500 | 0.0001 | - |
| 4.625 | 5550 | 0.0 | - |
| 4.6667 | 5600 | 0.0006 | - |
| 4.7083 | 5650 | 0.0 | - |
| 4.75 | 5700 | 0.0 | - |
| 4.7917 | 5750 | 0.0 | - |
| 4.8333 | 5800 | 0.0 | - |
| 4.875 | 5850 | 0.0 | - |
| 4.9167 | 5900 | 0.0 | - |
| 4.9583 | 5950 | 0.0 | - |
| 5.0 | 6000 | 0.0001 | 0.391 |
| 5.0417 | 6050 | 0.0 | - |
| 5.0833 | 6100 | 0.0001 | - |
| 5.125 | 6150 | 0.0 | - |
| 5.1667 | 6200 | 0.0 | - |
| 5.2083 | 6250 | 0.0 | - |
| 5.25 | 6300 | 0.0 | - |
| 5.2917 | 6350 | 0.0 | - |
| 5.3333 | 6400 | 0.0 | - |
| 5.375 | 6450 | 0.0 | - |
| 5.4167 | 6500 | 0.0 | - |
| 5.4583 | 6550 | 0.0 | - |
| 5.5 | 6600 | 0.0001 | - |
| 5.5417 | 6650 | 0.0 | - |
| 5.5833 | 6700 | 0.0 | - |
| 5.625 | 6750 | 0.0 | - |
| 5.6667 | 6800 | 0.0 | - |
| 5.7083 | 6850 | 0.0 | - |
| 5.75 | 6900 | 0.0001 | - |
| 5.7917 | 6950 | 0.0 | - |
| 5.8333 | 7000 | 0.0001 | - |
| 5.875 | 7050 | 0.0 | - |
| 5.9167 | 7100 | 0.0 | - |
| 5.9583 | 7150 | 0.0 | - |
| 6.0 | 7200 | 0.0001 | 0.4026 |
| 6.0417 | 7250 | 0.0 | - |
| 6.0833 | 7300 | 0.0 | - |
| 6.125 | 7350 | 0.0 | - |
| 6.1667 | 7400 | 0.0 | - |
| 6.2083 | 7450 | 0.0 | - |
| 6.25 | 7500 | 0.0 | - |
| 6.2917 | 7550 | 0.0 | - |
| 6.3333 | 7600 | 0.0 | - |
| 6.375 | 7650 | 0.0 | - |
| 6.4167 | 7700 | 0.0 | - |
| 6.4583 | 7750 | 0.0 | - |
| 6.5 | 7800 | 0.0 | - |
| 6.5417 | 7850 | 0.0 | - |
| 6.5833 | 7900 | 0.0 | - |
| 6.625 | 7950 | 0.0 | - |
| 6.6667 | 8000 | 0.0001 | - |
| 6.7083 | 8050 | 0.0005 | - |
| 6.75 | 8100 | 0.0063 | - |
| 6.7917 | 8150 | 0.0 | - |
| 6.8333 | 8200 | 0.0 | - |
| 6.875 | 8250 | 0.0 | - |
| 6.9167 | 8300 | 0.0 | - |
| 6.9583 | 8350 | 0.0 | - |
| 7.0 | 8400 | 0.0 | 0.4018 |
| 7.0417 | 8450 | 0.0 | - |
| 7.0833 | 8500 | 0.0 | - |
| 7.125 | 8550 | 0.0 | - |
| 7.1667 | 8600 | 0.0 | - |
| 7.2083 | 8650 | 0.0 | - |
| 7.25 | 8700 | 0.0 | - |
| 7.2917 | 8750 | 0.0 | - |
| 7.3333 | 8800 | 0.0 | - |
| 7.375 | 8850 | 0.0 | - |
| 7.4167 | 8900 | 0.0 | - |
| 7.4583 | 8950 | 0.0 | - |
| 7.5 | 9000 | 0.0 | - |
| 7.5417 | 9050 | 0.0 | - |
| 7.5833 | 9100 | 0.0 | - |
| 7.625 | 9150 | 0.0 | - |
| 7.6667 | 9200 | 0.0 | - |
| 7.7083 | 9250 | 0.0 | - |
| 7.75 | 9300 | 0.0 | - |
| 7.7917 | 9350 | 0.0 | - |
| 7.8333 | 9400 | 0.0 | - |
| 7.875 | 9450 | 0.0 | - |
| 7.9167 | 9500 | 0.0 | - |
| 7.9583 | 9550 | 0.0 | - |
| 8.0 | 9600 | 0.0 | 0.4001 |
| 8.0417 | 9650 | 0.0 | - |
| 8.0833 | 9700 | 0.0 | - |
| 8.125 | 9750 | 0.0 | - |
### Framework Versions
- Python: 3.10.14
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.2.2
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```