---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: sashimi:Kami berbagi sebotol sake, pesanan edamame, dan dia makan sesepiring
sushi sementara saya makan sashimi.
- text: kelompok:tempat agak kecil tapi saya kira jika mereka tidak terlalu sibuk
mungkin bisa memuat kelompok atau anak-anak.
- text: Suan:Lokasinya yang bagus dan fakta bahwa Hutner College dekat serta harga
sangat masuk akal, membuat siswa kembali ke Suan lagi dan lagi.
- text: rapido:Di sebelah kanan saya, nyonya rumah berdiri di dekat seorang busboy
dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang
meja untuk enam orang.
- text: hidangan:Jangan bersantap di Tamarind untuk hidangan vegetarian, mereka tidak
setara dengan pilihan non-sayuran.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7836879432624113
name: Accuracy
---
# SetFit Aspect Model
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
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.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** id_core_news_trf
- **SetFitABSA Aspect Model:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect)
- **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | - 'makanannya:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'
- 'makanannya:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'
- 'dapur:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'
|
| no aspect | - 'faktor penebusan:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'
- 'atas:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'
- 'kekurangan Teodora:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7837 |
## 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 AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"zeroix07/indo-setfit-absa-model-aspect",
"zeroix07/indo-setfit-absa-model-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 17.4396 | 40 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 415 |
| aspect | 181 |
### Training Hyperparameters
- batch_size: (6, 6)
- num_epochs: (1, 16)
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2509 | - |
| 0.0015 | 50 | 0.1002 | - |
| 0.0029 | 100 | 0.2166 | - |
| 0.0044 | 150 | 0.1083 | - |
| 0.0058 | 200 | 0.2008 | - |
| 0.0073 | 250 | 0.2292 | - |
| 0.0088 | 300 | 0.1745 | - |
| 0.0102 | 350 | 0.207 | - |
| 0.0117 | 400 | 0.0432 | - |
| 0.0131 | 450 | 0.0122 | - |
| 0.0146 | 500 | 0.0318 | - |
| 0.0161 | 550 | 0.0037 | - |
| 0.0175 | 600 | 0.0065 | - |
| 0.0190 | 650 | 0.0401 | - |
| 0.0204 | 700 | 0.0015 | - |
| 0.0219 | 750 | 0.0043 | - |
| 0.0233 | 800 | 0.0968 | - |
| 0.0248 | 850 | 0.1695 | - |
| 0.0263 | 900 | 0.0037 | - |
| 0.0277 | 950 | 0.001 | - |
| 0.0292 | 1000 | 0.0041 | - |
| 0.0306 | 1050 | 0.0009 | - |
| 0.0321 | 1100 | 0.0025 | - |
| 0.0336 | 1150 | 0.0015 | - |
| 0.0350 | 1200 | 0.0763 | - |
| 0.0365 | 1250 | 0.2008 | - |
| 0.0379 | 1300 | 0.0015 | - |
| 0.0394 | 1350 | 0.0766 | - |
| 0.0409 | 1400 | 0.2491 | - |
| 0.0423 | 1450 | 0.1411 | - |
| 0.0438 | 1500 | 0.0007 | - |
| 0.0452 | 1550 | 0.0057 | - |
| 0.0467 | 1600 | 0.0007 | - |
| 0.0482 | 1650 | 0.1603 | - |
| 0.0496 | 1700 | 0.0006 | - |
| 0.0511 | 1750 | 0.0019 | - |
| 0.0525 | 1800 | 0.0005 | - |
| 0.0540 | 1850 | 0.0005 | - |
| 0.0555 | 1900 | 0.2637 | - |
| 0.0569 | 1950 | 0.0011 | - |
| 0.0584 | 2000 | 0.0008 | - |
| 0.0598 | 2050 | 0.0017 | - |
| 0.0613 | 2100 | 0.0005 | - |
| 0.0627 | 2150 | 0.0002 | - |
| 0.0642 | 2200 | 0.0766 | - |
| 0.0657 | 2250 | 0.0001 | - |
| 0.0671 | 2300 | 0.0002 | - |
| 0.0686 | 2350 | 0.0023 | - |
| 0.0700 | 2400 | 0.0001 | - |
| 0.0715 | 2450 | 0.0122 | - |
| 0.0730 | 2500 | 0.001 | - |
| 0.0744 | 2550 | 0.0006 | - |
| 0.0759 | 2600 | 0.0056 | - |
| 0.0773 | 2650 | 0.0022 | - |
| 0.0788 | 2700 | 0.0002 | - |
| 0.0803 | 2750 | 0.0213 | - |
| 0.0817 | 2800 | 0.047 | - |
| 0.0832 | 2850 | 0.0002 | - |
| 0.0846 | 2900 | 0.0135 | - |
| 0.0861 | 2950 | 0.0473 | - |
| 0.0876 | 3000 | 0.0003 | - |
| 0.0890 | 3050 | 0.0078 | - |
| 0.0905 | 3100 | 0.0001 | - |
| 0.0919 | 3150 | 0.0002 | - |
| 0.0934 | 3200 | 0.008 | - |
| 0.0949 | 3250 | 0.0005 | - |
| 0.0963 | 3300 | 0.0002 | - |
| 0.0978 | 3350 | 0.0062 | - |
| 0.0992 | 3400 | 0.0002 | - |
| 0.1007 | 3450 | 0.0002 | - |
| 0.1021 | 3500 | 0.0007 | - |
| 0.1036 | 3550 | 0.0017 | - |
| 0.1051 | 3600 | 0.1652 | - |
| 0.1065 | 3650 | 0.0011 | - |
| 0.1080 | 3700 | 0.0 | - |
| 0.1094 | 3750 | 0.0003 | - |
| 0.1109 | 3800 | 0.0007 | - |
| 0.1124 | 3850 | 0.0006 | - |
| 0.1138 | 3900 | 0.0001 | - |
| 0.1153 | 3950 | 0.002 | - |
| 0.1167 | 4000 | 0.0001 | - |
| 0.1182 | 4050 | 0.0004 | - |
| 0.1197 | 4100 | 0.0003 | - |
| 0.1211 | 4150 | 0.0295 | - |
| 0.1226 | 4200 | 0.0012 | - |
| 0.1240 | 4250 | 0.0004 | - |
| 0.1255 | 4300 | 0.0003 | - |
| 0.1270 | 4350 | 0.0364 | - |
| 0.1284 | 4400 | 0.042 | - |
| 0.1299 | 4450 | 0.0 | - |
| 0.1313 | 4500 | 0.0 | - |
| 0.1328 | 4550 | 0.0001 | - |
| 0.1343 | 4600 | 0.0159 | - |
| 0.1357 | 4650 | 0.0001 | - |
| 0.1372 | 4700 | 0.0 | - |
| 0.1386 | 4750 | 0.0004 | - |
| 0.1401 | 4800 | 0.0409 | - |
| 0.1415 | 4850 | 0.0411 | - |
| 0.1430 | 4900 | 0.0001 | - |
| 0.1445 | 4950 | 0.0002 | - |
| 0.1459 | 5000 | 0.0 | - |
| 0.1474 | 5050 | 0.1251 | - |
| 0.1488 | 5100 | 0.0 | - |
| 0.1503 | 5150 | 0.0001 | - |
| 0.1518 | 5200 | 0.0 | - |
| 0.1532 | 5250 | 0.0 | - |
| 0.1547 | 5300 | 0.0466 | - |
| 0.1561 | 5350 | 0.0 | - |
| 0.1576 | 5400 | 0.0001 | - |
| 0.1591 | 5450 | 0.0 | - |
| 0.1605 | 5500 | 0.0254 | - |
| 0.1620 | 5550 | 0.0001 | - |
| 0.1634 | 5600 | 0.0002 | - |
| 0.1649 | 5650 | 0.0 | - |
| 0.1664 | 5700 | 0.0264 | - |
| 0.1678 | 5750 | 0.0006 | - |
| 0.1693 | 5800 | 0.0001 | - |
| 0.1707 | 5850 | 0.0022 | - |
| 0.1722 | 5900 | 0.0011 | - |
| 0.1737 | 5950 | 0.1395 | - |
| 0.1751 | 6000 | 0.0169 | - |
| 0.1766 | 6050 | 0.0043 | - |
| 0.1780 | 6100 | 0.1513 | - |
| 0.1795 | 6150 | 0.0001 | - |
| 0.1809 | 6200 | 0.0008 | - |
| 0.1824 | 6250 | 0.0 | - |
| 0.1839 | 6300 | 0.0009 | - |
| 0.1853 | 6350 | 0.0002 | - |
| 0.1868 | 6400 | 0.0001 | - |
| 0.1882 | 6450 | 0.0002 | - |
| 0.1897 | 6500 | 0.0534 | - |
| 0.1912 | 6550 | 0.0002 | - |
| 0.1926 | 6600 | 0.0001 | - |
| 0.1941 | 6650 | 0.0007 | - |
| 0.1955 | 6700 | 0.1641 | - |
| 0.1970 | 6750 | 0.0001 | - |
| 0.1985 | 6800 | 0.0012 | - |
| 0.1999 | 6850 | 0.0035 | - |
| 0.2014 | 6900 | 0.0006 | - |
| 0.2028 | 6950 | 0.0001 | - |
| 0.2043 | 7000 | 0.0107 | - |
| 0.2058 | 7050 | 0.0001 | - |
| 0.2072 | 7100 | 0.0028 | - |
| 0.2087 | 7150 | 0.0004 | - |
| 0.2101 | 7200 | 0.0 | - |
| 0.2116 | 7250 | 0.0866 | - |
| 0.2131 | 7300 | 0.0 | - |
| 0.2145 | 7350 | 0.0001 | - |
| 0.2160 | 7400 | 0.0 | - |
| 0.2174 | 7450 | 0.0 | - |
| 0.2189 | 7500 | 0.0001 | - |
| 0.2203 | 7550 | 0.0 | - |
| 0.2218 | 7600 | 0.0001 | - |
| 0.2233 | 7650 | 0.0001 | - |
| 0.2247 | 7700 | 0.0 | - |
| 0.2262 | 7750 | 0.0532 | - |
| 0.2276 | 7800 | 0.0 | - |
| 0.2291 | 7850 | 0.0611 | - |
| 0.2306 | 7900 | 0.0001 | - |
| 0.2320 | 7950 | 0.0 | - |
| 0.2335 | 8000 | 0.0001 | - |
| 0.2349 | 8050 | 0.0 | - |
| 0.2364 | 8100 | 0.0 | - |
| 0.2379 | 8150 | 0.0 | - |
| 0.2393 | 8200 | 0.0304 | - |
| 0.2408 | 8250 | 0.0 | - |
| 0.2422 | 8300 | 0.0253 | - |
| 0.2437 | 8350 | 0.0 | - |
| 0.2452 | 8400 | 0.0 | - |
| 0.2466 | 8450 | 0.0 | - |
| 0.2481 | 8500 | 0.0173 | - |
| 0.2495 | 8550 | 0.0002 | - |
| 0.2510 | 8600 | 0.0003 | - |
| 0.2525 | 8650 | 0.0012 | - |
| 0.2539 | 8700 | 0.1639 | - |
| 0.2554 | 8750 | 0.0308 | - |
| 0.2568 | 8800 | 0.0 | - |
| 0.2583 | 8850 | 0.0 | - |
| 0.2597 | 8900 | 0.068 | - |
| 0.2612 | 8950 | 0.0001 | - |
| 0.2627 | 9000 | 0.0001 | - |
| 0.2641 | 9050 | 0.0 | - |
| 0.2656 | 9100 | 0.0734 | - |
| 0.2670 | 9150 | 0.0002 | - |
| 0.2685 | 9200 | 0.0 | - |
| 0.2700 | 9250 | 0.0244 | - |
| 0.2714 | 9300 | 0.1642 | - |
| 0.2729 | 9350 | 0.326 | - |
| 0.2743 | 9400 | 0.0023 | - |
| 0.2758 | 9450 | 0.1533 | - |
| 0.2773 | 9500 | 0.0003 | - |
| 0.2787 | 9550 | 0.0005 | - |
| 0.2802 | 9600 | 0.0005 | - |
| 0.2816 | 9650 | 0.0003 | - |
| 0.2831 | 9700 | 0.0001 | - |
| 0.2846 | 9750 | 0.0001 | - |
| 0.2860 | 9800 | 0.0003 | - |
| 0.2875 | 9850 | 0.0008 | - |
| 0.2889 | 9900 | 0.1625 | - |
| 0.2904 | 9950 | 0.0011 | - |
| 0.2919 | 10000 | 0.037 | - |
| 0.2933 | 10050 | 0.0006 | - |
| 0.2948 | 10100 | 0.0006 | - |
| 0.2962 | 10150 | 0.0001 | - |
| 0.2977 | 10200 | 0.0002 | - |
| 0.2991 | 10250 | 0.0149 | - |
| 0.3006 | 10300 | 0.0 | - |
| 0.3021 | 10350 | 0.0 | - |
| 0.3035 | 10400 | 0.0 | - |
| 0.3050 | 10450 | 0.0 | - |
| 0.3064 | 10500 | 0.0 | - |
| 0.3079 | 10550 | 0.0 | - |
| 0.3094 | 10600 | 0.0 | - |
| 0.3108 | 10650 | 0.0001 | - |
| 0.3123 | 10700 | 0.0932 | - |
| 0.3137 | 10750 | 0.0 | - |
| 0.3152 | 10800 | 0.0 | - |
| 0.3167 | 10850 | 0.0 | - |
| 0.3181 | 10900 | 0.0 | - |
| 0.3196 | 10950 | 0.0 | - |
| 0.3210 | 11000 | 0.0004 | - |
| 0.3225 | 11050 | 0.0 | - |
| 0.3240 | 11100 | 0.0 | - |
| 0.3254 | 11150 | 0.0 | - |
| 0.3269 | 11200 | 0.0228 | - |
| 0.3283 | 11250 | 0.0 | - |
| 0.3298 | 11300 | 0.0263 | - |
| 0.3313 | 11350 | 0.0001 | - |
| 0.3327 | 11400 | 0.0218 | - |
| 0.3342 | 11450 | 0.0 | - |
| 0.3356 | 11500 | 0.0826 | - |
| 0.3371 | 11550 | 0.0 | - |
| 0.3385 | 11600 | 0.0 | - |
| 0.3400 | 11650 | 0.0 | - |
| 0.3415 | 11700 | 0.0 | - |
| 0.3429 | 11750 | 0.0002 | - |
| 0.3444 | 11800 | 0.0 | - |
| 0.3458 | 11850 | 0.0001 | - |
| 0.3473 | 11900 | 0.0 | - |
| 0.3488 | 11950 | 0.0 | - |
| 0.3502 | 12000 | 0.0 | - |
| 0.3517 | 12050 | 0.0 | - |
| 0.3531 | 12100 | 0.0563 | - |
| 0.3546 | 12150 | 0.0 | - |
| 0.3561 | 12200 | 0.0384 | - |
| 0.3575 | 12250 | 0.0002 | - |
| 0.3590 | 12300 | 0.0352 | - |
| 0.3604 | 12350 | 0.0003 | - |
| 0.3619 | 12400 | 0.0001 | - |
| 0.3634 | 12450 | 0.0003 | - |
| 0.3648 | 12500 | 0.0 | - |
| 0.3663 | 12550 | 0.0003 | - |
| 0.3677 | 12600 | 0.0 | - |
| 0.3692 | 12650 | 0.0 | - |
| 0.3707 | 12700 | 0.0002 | - |
| 0.3721 | 12750 | 0.0002 | - |
| 0.3736 | 12800 | 0.0 | - |
| 0.3750 | 12850 | 0.0 | - |
| 0.3765 | 12900 | 0.0 | - |
| 0.3779 | 12950 | 0.0 | - |
| 0.3794 | 13000 | 0.0 | - |
| 0.3809 | 13050 | 0.0141 | - |
| 0.3823 | 13100 | 0.0 | - |
| 0.3838 | 13150 | 0.1085 | - |
| 0.3852 | 13200 | 0.0 | - |
| 0.3867 | 13250 | 0.0006 | - |
| 0.3882 | 13300 | 0.0778 | - |
| 0.3896 | 13350 | 0.0003 | - |
| 0.3911 | 13400 | 0.0001 | - |
| 0.3925 | 13450 | 0.0 | - |
| 0.3940 | 13500 | 0.0001 | - |
| 0.3955 | 13550 | 0.0 | - |
| 0.3969 | 13600 | 0.0001 | - |
| 0.3984 | 13650 | 0.0 | - |
| 0.3998 | 13700 | 0.0086 | - |
| 0.4013 | 13750 | 0.0079 | - |
| 0.4028 | 13800 | 0.0001 | - |
| 0.4042 | 13850 | 0.0001 | - |
| 0.4057 | 13900 | 0.084 | - |
| 0.4071 | 13950 | 0.0003 | - |
| 0.4086 | 14000 | 0.0004 | - |
| 0.4101 | 14050 | 0.0053 | - |
| 0.4115 | 14100 | 0.0 | - |
| 0.4130 | 14150 | 0.0008 | - |
| 0.4144 | 14200 | 0.1477 | - |
| 0.4159 | 14250 | 0.0 | - |
| 0.4173 | 14300 | 0.0017 | - |
| 0.4188 | 14350 | 0.0 | - |
| 0.4203 | 14400 | 0.0001 | - |
| 0.4217 | 14450 | 0.0414 | - |
| 0.4232 | 14500 | 0.0 | - |
| 0.4246 | 14550 | 0.0002 | - |
| 0.4261 | 14600 | 0.0627 | - |
| 0.4276 | 14650 | 0.1112 | - |
| 0.4290 | 14700 | 0.0 | - |
| 0.4305 | 14750 | 0.0002 | - |
| 0.4319 | 14800 | 0.0002 | - |
| 0.4334 | 14850 | 0.0393 | - |
| 0.4349 | 14900 | 0.0 | - |
| 0.4363 | 14950 | 0.0 | - |
| 0.4378 | 15000 | 0.0003 | - |
| 0.4392 | 15050 | 0.0001 | - |
| 0.4407 | 15100 | 0.0005 | - |
| 0.4422 | 15150 | 0.0009 | - |
| 0.4436 | 15200 | 0.0001 | - |
| 0.4451 | 15250 | 0.0001 | - |
| 0.4465 | 15300 | 0.0212 | - |
| 0.4480 | 15350 | 0.0 | - |
| 0.4495 | 15400 | 0.0 | - |
| 0.4509 | 15450 | 0.0 | - |
| 0.4524 | 15500 | 0.0 | - |
| 0.4538 | 15550 | 0.05 | - |
| 0.4553 | 15600 | 0.0 | - |
| 0.4567 | 15650 | 0.028 | - |
| 0.4582 | 15700 | 0.0001 | - |
| 0.4597 | 15750 | 0.0 | - |
| 0.4611 | 15800 | 0.0 | - |
| 0.4626 | 15850 | 0.0 | - |
| 0.4640 | 15900 | 0.0 | - |
| 0.4655 | 15950 | 0.043 | - |
| 0.4670 | 16000 | 0.0363 | - |
| 0.4684 | 16050 | 0.0 | - |
| 0.4699 | 16100 | 0.054 | - |
| 0.4713 | 16150 | 0.0 | - |
| 0.4728 | 16200 | 0.0 | - |
| 0.4743 | 16250 | 0.0 | - |
| 0.4757 | 16300 | 0.0 | - |
| 0.4772 | 16350 | 0.1 | - |
| 0.4786 | 16400 | 0.0001 | - |
| 0.4801 | 16450 | 0.0001 | - |
| 0.4816 | 16500 | 0.0 | - |
| 0.4830 | 16550 | 0.0267 | - |
| 0.4845 | 16600 | 0.0361 | - |
| 0.4859 | 16650 | 0.0 | - |
| 0.4874 | 16700 | 0.0181 | - |
| 0.4889 | 16750 | 0.0 | - |
| 0.4903 | 16800 | 0.0382 | - |
| 0.4918 | 16850 | 0.0276 | - |
| 0.4932 | 16900 | 0.0 | - |
| 0.4947 | 16950 | 0.0345 | - |
| 0.4961 | 17000 | 0.0 | - |
| 0.4976 | 17050 | 0.0 | - |
| 0.4991 | 17100 | 0.0 | - |
| 0.5005 | 17150 | 0.0 | - |
| 0.5020 | 17200 | 0.0 | - |
| 0.5034 | 17250 | 0.0 | - |
| 0.5049 | 17300 | 0.0001 | - |
| 0.5064 | 17350 | 0.0 | - |
| 0.5078 | 17400 | 0.0 | - |
| 0.5093 | 17450 | 0.0004 | - |
| 0.5107 | 17500 | 0.071 | - |
| 0.5122 | 17550 | 0.0 | - |
| 0.5137 | 17600 | 0.0 | - |
| 0.5151 | 17650 | 0.0 | - |
| 0.5166 | 17700 | 0.0239 | - |
| 0.5180 | 17750 | 0.0 | - |
| 0.5195 | 17800 | 0.0 | - |
| 0.5210 | 17850 | 0.0 | - |
| 0.5224 | 17900 | 0.0 | - |
| 0.5239 | 17950 | 0.0 | - |
| 0.5253 | 18000 | 0.0 | - |
| 0.5268 | 18050 | 0.0 | - |
| 0.5283 | 18100 | 0.0 | - |
| 0.5297 | 18150 | 0.0 | - |
| 0.5312 | 18200 | 0.0001 | - |
| 0.5326 | 18250 | 0.0 | - |
| 0.5341 | 18300 | 0.0 | - |
| 0.5355 | 18350 | 0.064 | - |
| 0.5370 | 18400 | 0.0 | - |
| 0.5385 | 18450 | 0.0 | - |
| 0.5399 | 18500 | 0.0 | - |
| 0.5414 | 18550 | 0.0499 | - |
| 0.5428 | 18600 | 0.0001 | - |
| 0.5443 | 18650 | 0.0 | - |
| 0.5458 | 18700 | 0.0 | - |
| 0.5472 | 18750 | 0.0002 | - |
| 0.5487 | 18800 | 0.0964 | - |
| 0.5501 | 18850 | 0.0 | - |
| 0.5516 | 18900 | 0.0 | - |
| 0.5531 | 18950 | 0.0 | - |
| 0.5545 | 19000 | 0.0 | - |
| 0.5560 | 19050 | 0.0001 | - |
| 0.5574 | 19100 | 0.0 | - |
| 0.5589 | 19150 | 0.0 | - |
| 0.5604 | 19200 | 0.0556 | - |
| 0.5618 | 19250 | 0.0715 | - |
| 0.5633 | 19300 | 0.0004 | - |
| 0.5647 | 19350 | 0.0 | - |
| 0.5662 | 19400 | 0.0 | - |
| 0.5677 | 19450 | 0.0 | - |
| 0.5691 | 19500 | 0.0001 | - |
| 0.5706 | 19550 | 0.0 | - |
| 0.5720 | 19600 | 0.0446 | - |
| 0.5735 | 19650 | 0.0 | - |
| 0.5749 | 19700 | 0.0 | - |
| 0.5764 | 19750 | 0.0 | - |
| 0.5779 | 19800 | 0.0324 | - |
| 0.5793 | 19850 | 0.0001 | - |
| 0.5808 | 19900 | 0.0001 | - |
| 0.5822 | 19950 | 0.0 | - |
| 0.5837 | 20000 | 0.0 | - |
| 0.5852 | 20050 | 0.0 | - |
| 0.5866 | 20100 | 0.0429 | - |
| 0.5881 | 20150 | 0.0 | - |
| 0.5895 | 20200 | 0.0 | - |
| 0.5910 | 20250 | 0.0 | - |
| 0.5925 | 20300 | 0.0 | - |
| 0.5939 | 20350 | 0.0 | - |
| 0.5954 | 20400 | 0.0 | - |
| 0.5968 | 20450 | 0.0214 | - |
| 0.5983 | 20500 | 0.0 | - |
| 0.5998 | 20550 | 0.0 | - |
| 0.6012 | 20600 | 0.0 | - |
| 0.6027 | 20650 | 0.0 | - |
| 0.6041 | 20700 | 0.0 | - |
| 0.6056 | 20750 | 0.0 | - |
| 0.6071 | 20800 | 0.0 | - |
| 0.6085 | 20850 | 0.0 | - |
| 0.6100 | 20900 | 0.0 | - |
| 0.6114 | 20950 | 0.0001 | - |
| 0.6129 | 21000 | 0.0 | - |
| 0.6143 | 21050 | 0.0 | - |
| 0.6158 | 21100 | 0.0 | - |
| 0.6173 | 21150 | 0.0 | - |
| 0.6187 | 21200 | 0.0 | - |
| 0.6202 | 21250 | 0.0 | - |
| 0.6216 | 21300 | 0.0402 | - |
| 0.6231 | 21350 | 0.0603 | - |
| 0.6246 | 21400 | 0.0 | - |
| 0.6260 | 21450 | 0.0 | - |
| 0.6275 | 21500 | 0.0 | - |
| 0.6289 | 21550 | 0.0 | - |
| 0.6304 | 21600 | 0.0 | - |
| 0.6319 | 21650 | 0.0238 | - |
| 0.6333 | 21700 | 0.0187 | - |
| 0.6348 | 21750 | 0.0 | - |
| 0.6362 | 21800 | 0.0 | - |
| 0.6377 | 21850 | 0.0 | - |
| 0.6392 | 21900 | 0.0325 | - |
| 0.6406 | 21950 | 0.0 | - |
| 0.6421 | 22000 | 0.0 | - |
| 0.6435 | 22050 | 0.0361 | - |
| 0.6450 | 22100 | 0.0 | - |
| 0.6465 | 22150 | 0.0853 | - |
| 0.6479 | 22200 | 0.0 | - |
| 0.6494 | 22250 | 0.0 | - |
| 0.6508 | 22300 | 0.0 | - |
| 0.6523 | 22350 | 0.0 | - |
| 0.6537 | 22400 | 0.0649 | - |
| 0.6552 | 22450 | 0.0 | - |
| 0.6567 | 22500 | 0.0 | - |
| 0.6581 | 22550 | 0.0 | - |
| 0.6596 | 22600 | 0.0 | - |
| 0.6610 | 22650 | 0.0 | - |
| 0.6625 | 22700 | 0.0 | - |
| 0.6640 | 22750 | 0.0 | - |
| 0.6654 | 22800 | 0.0 | - |
| 0.6669 | 22850 | 0.0382 | - |
| 0.6683 | 22900 | 0.0 | - |
| 0.6698 | 22950 | 0.0 | - |
| 0.6713 | 23000 | 0.0 | - |
| 0.6727 | 23050 | 0.0 | - |
| 0.6742 | 23100 | 0.0 | - |
| 0.6756 | 23150 | 0.0 | - |
| 0.6771 | 23200 | 0.0001 | - |
| 0.6786 | 23250 | 0.0458 | - |
| 0.6800 | 23300 | 0.0 | - |
| 0.6815 | 23350 | 0.0 | - |
| 0.6829 | 23400 | 0.0 | - |
| 0.6844 | 23450 | 0.0 | - |
| 0.6859 | 23500 | 0.0 | - |
| 0.6873 | 23550 | 0.0 | - |
| 0.6888 | 23600 | 0.044 | - |
| 0.6902 | 23650 | 0.0 | - |
| 0.6917 | 23700 | 0.0406 | - |
| 0.6931 | 23750 | 0.0 | - |
| 0.6946 | 23800 | 0.0318 | - |
| 0.6961 | 23850 | 0.0306 | - |
| 0.6975 | 23900 | 0.077 | - |
| 0.6990 | 23950 | 0.0194 | - |
| 0.7004 | 24000 | 0.0 | - |
| 0.7019 | 24050 | 0.0 | - |
| 0.7034 | 24100 | 0.0 | - |
| 0.7048 | 24150 | 0.0 | - |
| 0.7063 | 24200 | 0.0 | - |
| 0.7077 | 24250 | 0.0 | - |
| 0.7092 | 24300 | 0.0 | - |
| 0.7107 | 24350 | 0.0521 | - |
| 0.7121 | 24400 | 0.0 | - |
| 0.7136 | 24450 | 0.0 | - |
| 0.7150 | 24500 | 0.0 | - |
| 0.7165 | 24550 | 0.0 | - |
| 0.7180 | 24600 | 0.0 | - |
| 0.7194 | 24650 | 0.0 | - |
| 0.7209 | 24700 | 0.0 | - |
| 0.7223 | 24750 | 0.0518 | - |
| 0.7238 | 24800 | 0.0 | - |
| 0.7253 | 24850 | 0.0 | - |
| 0.7267 | 24900 | 0.0475 | - |
| 0.7282 | 24950 | 0.0 | - |
| 0.7296 | 25000 | 0.0 | - |
| 0.7311 | 25050 | 0.0374 | - |
| 0.7325 | 25100 | 0.0 | - |
| 0.7340 | 25150 | 0.0 | - |
| 0.7355 | 25200 | 0.0345 | - |
| 0.7369 | 25250 | 0.0 | - |
| 0.7384 | 25300 | 0.0 | - |
| 0.7398 | 25350 | 0.1585 | - |
| 0.7413 | 25400 | 0.0007 | - |
| 0.7428 | 25450 | 0.1661 | - |
| 0.7442 | 25500 | 0.0 | - |
| 0.7457 | 25550 | 0.0 | - |
| 0.7471 | 25600 | 0.0 | - |
| 0.7486 | 25650 | 0.0 | - |
| 0.7501 | 25700 | 0.0001 | - |
| 0.7515 | 25750 | 0.0 | - |
| 0.7530 | 25800 | 0.0 | - |
| 0.7544 | 25850 | 0.1657 | - |
| 0.7559 | 25900 | 0.0 | - |
| 0.7574 | 25950 | 0.0002 | - |
| 0.7588 | 26000 | 0.0001 | - |
| 0.7603 | 26050 | 0.0004 | - |
| 0.7617 | 26100 | 0.0 | - |
| 0.7632 | 26150 | 0.0449 | - |
| 0.7647 | 26200 | 0.1664 | - |
| 0.7661 | 26250 | 0.0002 | - |
| 0.7676 | 26300 | 0.0 | - |
| 0.7690 | 26350 | 0.0 | - |
| 0.7705 | 26400 | 0.0 | - |
| 0.7719 | 26450 | 0.0 | - |
| 0.7734 | 26500 | 0.0464 | - |
| 0.7749 | 26550 | 0.0 | - |
| 0.7763 | 26600 | 0.0002 | - |
| 0.7778 | 26650 | 0.0 | - |
| 0.7792 | 26700 | 0.0 | - |
| 0.7807 | 26750 | 0.0001 | - |
| 0.7822 | 26800 | 0.038 | - |
| 0.7836 | 26850 | 0.0 | - |
| 0.7851 | 26900 | 0.0 | - |
| 0.7865 | 26950 | 0.0 | - |
| 0.7880 | 27000 | 0.0 | - |
| 0.7895 | 27050 | 0.0 | - |
| 0.7909 | 27100 | 0.0 | - |
| 0.7924 | 27150 | 0.0464 | - |
| 0.7938 | 27200 | 0.0001 | - |
| 0.7953 | 27250 | 0.0376 | - |
| 0.7968 | 27300 | 0.0 | - |
| 0.7982 | 27350 | 0.0 | - |
| 0.7997 | 27400 | 0.0001 | - |
| 0.8011 | 27450 | 0.0001 | - |
| 0.8026 | 27500 | 0.0431 | - |
| 0.8041 | 27550 | 0.0 | - |
| 0.8055 | 27600 | 0.0263 | - |
| 0.8070 | 27650 | 0.0 | - |
| 0.8084 | 27700 | 0.0 | - |
| 0.8099 | 27750 | 0.0001 | - |
| 0.8113 | 27800 | 0.0 | - |
| 0.8128 | 27850 | 0.0 | - |
| 0.8143 | 27900 | 0.0 | - |
| 0.8157 | 27950 | 0.0 | - |
| 0.8172 | 28000 | 0.0 | - |
| 0.8186 | 28050 | 0.0 | - |
| 0.8201 | 28100 | 0.0 | - |
| 0.8216 | 28150 | 0.0 | - |
| 0.8230 | 28200 | 0.0 | - |
| 0.8245 | 28250 | 0.0 | - |
| 0.8259 | 28300 | 0.0 | - |
| 0.8274 | 28350 | 0.0 | - |
| 0.8289 | 28400 | 0.0 | - |
| 0.8303 | 28450 | 0.0253 | - |
| 0.8318 | 28500 | 0.0603 | - |
| 0.8332 | 28550 | 0.0 | - |
| 0.8347 | 28600 | 0.0627 | - |
| 0.8362 | 28650 | 0.0 | - |
| 0.8376 | 28700 | 0.0659 | - |
| 0.8391 | 28750 | 0.0 | - |
| 0.8405 | 28800 | 0.0 | - |
| 0.8420 | 28850 | 0.0 | - |
| 0.8435 | 28900 | 0.0 | - |
| 0.8449 | 28950 | 0.0 | - |
| 0.8464 | 29000 | 0.0 | - |
| 0.8478 | 29050 | 0.0 | - |
| 0.8493 | 29100 | 0.0314 | - |
| 0.8507 | 29150 | 0.0002 | - |
| 0.8522 | 29200 | 0.0 | - |
| 0.8537 | 29250 | 0.0001 | - |
| 0.8551 | 29300 | 0.0 | - |
| 0.8566 | 29350 | 0.0 | - |
| 0.8580 | 29400 | 0.0 | - |
| 0.8595 | 29450 | 0.1661 | - |
| 0.8610 | 29500 | 0.0 | - |
| 0.8624 | 29550 | 0.0 | - |
| 0.8639 | 29600 | 0.0464 | - |
| 0.8653 | 29650 | 0.0 | - |
| 0.8668 | 29700 | 0.0 | - |
| 0.8683 | 29750 | 0.0 | - |
| 0.8697 | 29800 | 0.0387 | - |
| 0.8712 | 29850 | 0.0872 | - |
| 0.8726 | 29900 | 0.0638 | - |
| 0.8741 | 29950 | 0.0 | - |
| 0.8756 | 30000 | 0.0638 | - |
| 0.8770 | 30050 | 0.0 | - |
| 0.8785 | 30100 | 0.0431 | - |
| 0.8799 | 30150 | 0.0 | - |
| 0.8814 | 30200 | 0.0397 | - |
| 0.8829 | 30250 | 0.0379 | - |
| 0.8843 | 30300 | 0.0642 | - |
| 0.8858 | 30350 | 0.0 | - |
| 0.8872 | 30400 | 0.0652 | - |
| 0.8887 | 30450 | 0.0641 | - |
| 0.8901 | 30500 | 0.0 | - |
| 0.8916 | 30550 | 0.0 | - |
| 0.8931 | 30600 | 0.021 | - |
| 0.8945 | 30650 | 0.0 | - |
| 0.8960 | 30700 | 0.0218 | - |
| 0.8974 | 30750 | 0.0 | - |
| 0.8989 | 30800 | 0.0 | - |
| 0.9004 | 30850 | 0.0214 | - |
| 0.9018 | 30900 | 0.0 | - |
| 0.9033 | 30950 | 0.0 | - |
| 0.9047 | 31000 | 0.0 | - |
| 0.9062 | 31050 | 0.0717 | - |
| 0.9077 | 31100 | 0.0 | - |
| 0.9091 | 31150 | 0.0476 | - |
| 0.9106 | 31200 | 0.0 | - |
| 0.9120 | 31250 | 0.0 | - |
| 0.9135 | 31300 | 0.0 | - |
| 0.9150 | 31350 | 0.0 | - |
| 0.9164 | 31400 | 0.0 | - |
| 0.9179 | 31450 | 0.0 | - |
| 0.9193 | 31500 | 0.0548 | - |
| 0.9208 | 31550 | 0.0002 | - |
| 0.9223 | 31600 | 0.0 | - |
| 0.9237 | 31650 | 0.0 | - |
| 0.9252 | 31700 | 0.0 | - |
| 0.9266 | 31750 | 0.0 | - |
| 0.9281 | 31800 | 0.0 | - |
| 0.9295 | 31850 | 0.0 | - |
| 0.9310 | 31900 | 0.0 | - |
| 0.9325 | 31950 | 0.0 | - |
| 0.9339 | 32000 | 0.0358 | - |
| 0.9354 | 32050 | 0.0 | - |
| 0.9368 | 32100 | 0.0 | - |
| 0.9383 | 32150 | 0.0 | - |
| 0.9398 | 32200 | 0.0 | - |
| 0.9412 | 32250 | 0.0 | - |
| 0.9427 | 32300 | 0.0 | - |
| 0.9441 | 32350 | 0.0 | - |
| 0.9456 | 32400 | 0.0 | - |
| 0.9471 | 32450 | 0.0 | - |
| 0.9485 | 32500 | 0.0 | - |
| 0.9500 | 32550 | 0.0863 | - |
| 0.9514 | 32600 | 0.0 | - |
| 0.9529 | 32650 | 0.0 | - |
| 0.9544 | 32700 | 0.0 | - |
| 0.9558 | 32750 | 0.0 | - |
| 0.9573 | 32800 | 0.0 | - |
| 0.9587 | 32850 | 0.0 | - |
| 0.9602 | 32900 | 0.0 | - |
| 0.9617 | 32950 | 0.0 | - |
| 0.9631 | 33000 | 0.0241 | - |
| 0.9646 | 33050 | 0.0 | - |
| 0.9660 | 33100 | 0.0 | - |
| 0.9675 | 33150 | 0.0 | - |
| 0.9689 | 33200 | 0.0258 | - |
| 0.9704 | 33250 | 0.0 | - |
| 0.9719 | 33300 | 0.0 | - |
| 0.9733 | 33350 | 0.0 | - |
| 0.9748 | 33400 | 0.0 | - |
| 0.9762 | 33450 | 0.0 | - |
| 0.9777 | 33500 | 0.0 | - |
| 0.9792 | 33550 | 0.0 | - |
| 0.9806 | 33600 | 0.0 | - |
| 0.9821 | 33650 | 0.0 | - |
| 0.9835 | 33700 | 0.0605 | - |
| 0.9850 | 33750 | 0.0 | - |
| 0.9865 | 33800 | 0.0 | - |
| 0.9879 | 33850 | 0.0 | - |
| 0.9894 | 33900 | 0.0245 | - |
| 0.9908 | 33950 | 0.0 | - |
| 0.9923 | 34000 | 0.0 | - |
| 0.9938 | 34050 | 0.0585 | - |
| 0.9952 | 34100 | 0.0513 | - |
| 0.9967 | 34150 | 0.0 | - |
| 0.9981 | 34200 | 0.0 | - |
| 0.9996 | 34250 | 0.0 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.18.0
- 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}
}
```