---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Suasana:Tempatnya ramai sekali dan ngantei banget. Suasana di dalam resto
sangat panas dan padat. Makanannya enak enak.
- text: bener2 pedes puolll:Rasanya sgt gak cocok dilidah gue orang bekasi.. ayamnya
ayam kampung sih tp kecil bgt (beli yg dada).. terus tempe bacem sgt padet dan
tahunya enak sih.. untuk sambel pedes bgt bener2 pedes puolll, tp rasanya gasukaa.
- text: gang:Suasana di dalam resto sangat panas dan padat. Makanannya enak enak.
Dan restonya ada di beberapa tempat dalam satu gang.
- text: tempe:Menu makanannya khas Sunda ada ayam, pepes ikan, babat, tahu, tempe,
sayur-sayur. Tidak banyak variasinya tapi kualitas rasanya oke. Saat itu pesen
ayam bakar, jukut goreng, tempe sama pepes tahu. Ini semuanya enak (menurut pendapat
pribadi).
- text: 'babat:Kemaren kebetulan makan babat sama nyobain cumi, buat tekstur babatnya
itu engga alot sama sekali dan tidak amis, sedangkan buat cumi utuh lumayan gede
juga tekstur kenyel kenyelnya dapet dan mateng juga sampe ke dalem. '
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: 0.80625
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:** [pahri/setfit-indo-resto-RM-ibu-imas-aspect](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-aspect)
- **SetFitABSA Polarity Model:** [pahri/setfit-indo-resto-RM-ibu-imas-polarity](https://huggingface.co/pahri/setfit-indo-resto-RM-ibu-imas-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 |
|:----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| no aspect | - 'ambel leuncanya:ambel leuncanya enak terus pedesss'
- 'Warung Sunda:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'
- 'makanannya:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'
|
| aspect | - 'ayam bakar:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi dan ayam bakar'
- 'Ayam bakar:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'
- 'sambel terasi merah:Ayam bakar,sambel leunca sambel terasi merah enak banget 9/10, perkedel jagung 8/10 makan pakai sambel mantap. Makan berdua sekitar 77k'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8063 |
## 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(
"pahri/setfit-indo-resto-RM-ibu-imas-aspect",
"pahri/setfit-indo-resto-RM-ibu-imas-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 | 4 | 37.7180 | 93 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 371 |
| aspect | 51 |
### 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 | 0.4225 | - |
| 0.0021 | 50 | 0.2528 | - |
| 0.0043 | 100 | 0.3611 | - |
| 0.0064 | 150 | 0.2989 | - |
| 0.0085 | 200 | 0.2907 | - |
| 0.0107 | 250 | 0.1609 | - |
| 0.0128 | 300 | 0.3534 | - |
| 0.0149 | 350 | 0.1294 | - |
| 0.0171 | 400 | 0.2797 | - |
| 0.0192 | 450 | 0.3119 | - |
| 0.0213 | 500 | 0.004 | - |
| 0.0235 | 550 | 0.1057 | - |
| 0.0256 | 600 | 0.1049 | - |
| 0.0277 | 650 | 0.1601 | - |
| 0.0299 | 700 | 0.151 | - |
| 0.0320 | 750 | 0.1034 | - |
| 0.0341 | 800 | 0.2356 | - |
| 0.0363 | 850 | 0.1335 | - |
| 0.0384 | 900 | 0.0559 | - |
| 0.0405 | 950 | 0.0028 | - |
| 0.0427 | 1000 | 0.1307 | - |
| 0.0448 | 1050 | 0.0049 | - |
| 0.0469 | 1100 | 0.1348 | - |
| 0.0491 | 1150 | 0.0392 | - |
| 0.0512 | 1200 | 0.054 | - |
| 0.0533 | 1250 | 0.0016 | - |
| 0.0555 | 1300 | 0.0012 | - |
| 0.0576 | 1350 | 0.0414 | - |
| 0.0597 | 1400 | 0.1087 | - |
| 0.0618 | 1450 | 0.0464 | - |
| 0.0640 | 1500 | 0.0095 | - |
| 0.0661 | 1550 | 0.0011 | - |
| 0.0682 | 1600 | 0.0002 | - |
| 0.0704 | 1650 | 0.1047 | - |
| 0.0725 | 1700 | 0.001 | - |
| 0.0746 | 1750 | 0.0965 | - |
| 0.0768 | 1800 | 0.0002 | - |
| 0.0789 | 1850 | 0.1436 | - |
| 0.0810 | 1900 | 0.0011 | - |
| 0.0832 | 1950 | 0.001 | - |
| 0.0853 | 2000 | 0.1765 | - |
| 0.0874 | 2050 | 0.1401 | - |
| 0.0896 | 2100 | 0.0199 | - |
| 0.0917 | 2150 | 0.0 | - |
| 0.0938 | 2200 | 0.0023 | - |
| 0.0960 | 2250 | 0.0034 | - |
| 0.0981 | 2300 | 0.0001 | - |
| 0.1002 | 2350 | 0.0948 | - |
| 0.1024 | 2400 | 0.1634 | - |
| 0.1045 | 2450 | 0.0 | - |
| 0.1066 | 2500 | 0.0005 | - |
| 0.1088 | 2550 | 0.0695 | - |
| 0.1109 | 2600 | 0.0 | - |
| 0.1130 | 2650 | 0.0067 | - |
| 0.1152 | 2700 | 0.0025 | - |
| 0.1173 | 2750 | 0.0013 | - |
| 0.1194 | 2800 | 0.1426 | - |
| 0.1216 | 2850 | 0.0001 | - |
| 0.1237 | 2900 | 0.0 | - |
| 0.1258 | 2950 | 0.0 | - |
| 0.1280 | 3000 | 0.0001 | - |
| 0.1301 | 3050 | 0.0001 | - |
| 0.1322 | 3100 | 0.0122 | - |
| 0.1344 | 3150 | 0.0 | - |
| 0.1365 | 3200 | 0.0001 | - |
| 0.1386 | 3250 | 0.0041 | - |
| 0.1408 | 3300 | 0.2549 | - |
| 0.1429 | 3350 | 0.0062 | - |
| 0.1450 | 3400 | 0.0154 | - |
| 0.1472 | 3450 | 0.1776 | - |
| 0.1493 | 3500 | 0.0039 | - |
| 0.1514 | 3550 | 0.0183 | - |
| 0.1536 | 3600 | 0.0045 | - |
| 0.1557 | 3650 | 0.1108 | - |
| 0.1578 | 3700 | 0.0002 | - |
| 0.1600 | 3750 | 0.01 | - |
| 0.1621 | 3800 | 0.0002 | - |
| 0.1642 | 3850 | 0.0001 | - |
| 0.1664 | 3900 | 0.1612 | - |
| 0.1685 | 3950 | 0.0107 | - |
| 0.1706 | 4000 | 0.0548 | - |
| 0.1728 | 4050 | 0.0001 | - |
| 0.1749 | 4100 | 0.0162 | - |
| 0.1770 | 4150 | 0.1294 | - |
| 0.1792 | 4200 | 0.0 | - |
| 0.1813 | 4250 | 0.0032 | - |
| 0.1834 | 4300 | 0.0051 | - |
| 0.1855 | 4350 | 0.0 | - |
| 0.1877 | 4400 | 0.0151 | - |
| 0.1898 | 4450 | 0.0097 | - |
| 0.1919 | 4500 | 0.0002 | - |
| 0.1941 | 4550 | 0.0045 | - |
| 0.1962 | 4600 | 0.0001 | - |
| 0.1983 | 4650 | 0.0001 | - |
| 0.2005 | 4700 | 0.0227 | - |
| 0.2026 | 4750 | 0.0018 | - |
| 0.2047 | 4800 | 0.0 | - |
| 0.2069 | 4850 | 0.0001 | - |
| 0.2090 | 4900 | 0.0 | - |
| 0.2111 | 4950 | 0.0 | - |
| 0.2133 | 5000 | 0.0 | - |
| 0.2154 | 5050 | 0.0002 | - |
| 0.2175 | 5100 | 0.0002 | - |
| 0.2197 | 5150 | 0.0038 | - |
| 0.2218 | 5200 | 0.0 | - |
| 0.2239 | 5250 | 0.0 | - |
| 0.2261 | 5300 | 0.0 | - |
| 0.2282 | 5350 | 0.0028 | - |
| 0.2303 | 5400 | 0.0 | - |
| 0.2325 | 5450 | 0.1146 | - |
| 0.2346 | 5500 | 0.0 | - |
| 0.2367 | 5550 | 0.0073 | - |
| 0.2389 | 5600 | 0.0467 | - |
| 0.2410 | 5650 | 0.0092 | - |
| 0.2431 | 5700 | 0.0196 | - |
| 0.2453 | 5750 | 0.0002 | - |
| 0.2474 | 5800 | 0.0043 | - |
| 0.2495 | 5850 | 0.0378 | - |
| 0.2517 | 5900 | 0.0049 | - |
| 0.2538 | 5950 | 0.0054 | - |
| 0.2559 | 6000 | 0.1757 | - |
| 0.2581 | 6050 | 0.0 | - |
| 0.2602 | 6100 | 0.0001 | - |
| 0.2623 | 6150 | 0.1327 | - |
| 0.2645 | 6200 | 0.0 | - |
| 0.2666 | 6250 | 0.0 | - |
| 0.2687 | 6300 | 0.0 | - |
| 0.2709 | 6350 | 0.0134 | - |
| 0.2730 | 6400 | 0.0001 | - |
| 0.2751 | 6450 | 0.0112 | - |
| 0.2773 | 6500 | 0.0864 | - |
| 0.2794 | 6550 | 0.0 | - |
| 0.2815 | 6600 | 0.0094 | - |
| 0.2837 | 6650 | 0.1358 | - |
| 0.2858 | 6700 | 0.0155 | - |
| 0.2879 | 6750 | 0.0025 | - |
| 0.2901 | 6800 | 0.0002 | - |
| 0.2922 | 6850 | 0.0001 | - |
| 0.2943 | 6900 | 0.2809 | - |
| 0.2965 | 6950 | 0.0 | - |
| 0.2986 | 7000 | 0.0242 | - |
| 0.3007 | 7050 | 0.0015 | - |
| 0.3028 | 7100 | 0.0 | - |
| 0.3050 | 7150 | 0.1064 | - |
| 0.3071 | 7200 | 0.1636 | - |
| 0.3092 | 7250 | 0.267 | - |
| 0.3114 | 7300 | 0.1656 | - |
| 0.3135 | 7350 | 0.0943 | - |
| 0.3156 | 7400 | 0.189 | - |
| 0.3178 | 7450 | 0.0055 | - |
| 0.3199 | 7500 | 0.1286 | - |
| 0.3220 | 7550 | 0.1062 | - |
| 0.3242 | 7600 | 0.1275 | - |
| 0.3263 | 7650 | 0.0101 | - |
| 0.3284 | 7700 | 0.0162 | - |
| 0.3306 | 7750 | 0.0001 | - |
| 0.3327 | 7800 | 0.0001 | - |
| 0.3348 | 7850 | 0.0003 | - |
| 0.3370 | 7900 | 0.0 | - |
| 0.3391 | 7950 | 0.135 | - |
| 0.3412 | 8000 | 0.0 | - |
| 0.3434 | 8050 | 0.0125 | - |
| 0.3455 | 8100 | 0.0004 | - |
| 0.3476 | 8150 | 0.0 | - |
| 0.3498 | 8200 | 0.2229 | - |
| 0.3519 | 8250 | 0.0 | - |
| 0.3540 | 8300 | 0.0051 | - |
| 0.3562 | 8350 | 0.0 | - |
| 0.3583 | 8400 | 0.0001 | - |
| 0.3604 | 8450 | 0.0 | - |
| 0.3626 | 8500 | 0.1261 | - |
| 0.3647 | 8550 | 0.0054 | - |
| 0.3668 | 8600 | 0.1636 | - |
| 0.3690 | 8650 | 0.0036 | - |
| 0.3711 | 8700 | 0.0 | - |
| 0.3732 | 8750 | 0.0027 | - |
| 0.3754 | 8800 | 0.0 | - |
| 0.3775 | 8850 | 0.1422 | - |
| 0.3796 | 8900 | 0.1314 | - |
| 0.3818 | 8950 | 0.003 | - |
| 0.3839 | 9000 | 0.0 | - |
| 0.3860 | 9050 | 0.0092 | - |
| 0.3882 | 9100 | 0.0129 | - |
| 0.3903 | 9150 | 0.0 | - |
| 0.3924 | 9200 | 0.0 | - |
| 0.3946 | 9250 | 0.1659 | - |
| 0.3967 | 9300 | 0.0 | - |
| 0.3988 | 9350 | 0.0 | - |
| 0.4010 | 9400 | 0.0085 | - |
| 0.4031 | 9450 | 0.0 | - |
| 0.4052 | 9500 | 0.0 | - |
| 0.4074 | 9550 | 0.0 | - |
| 0.4095 | 9600 | 0.0112 | - |
| 0.4116 | 9650 | 0.0 | - |
| 0.4138 | 9700 | 0.0154 | - |
| 0.4159 | 9750 | 0.0011 | - |
| 0.4180 | 9800 | 0.0077 | - |
| 0.4202 | 9850 | 0.0064 | - |
| 0.4223 | 9900 | 0.0 | - |
| 0.4244 | 9950 | 0.0 | - |
| 0.4265 | 10000 | 0.0121 | - |
| 0.4287 | 10050 | 0.0 | - |
| 0.4308 | 10100 | 0.0 | - |
| 0.4329 | 10150 | 0.0076 | - |
| 0.4351 | 10200 | 0.0039 | - |
| 0.4372 | 10250 | 0.2153 | - |
| 0.4393 | 10300 | 0.0 | - |
| 0.4415 | 10350 | 0.1218 | - |
| 0.4436 | 10400 | 0.0077 | - |
| 0.4457 | 10450 | 0.1311 | - |
| 0.4479 | 10500 | 0.0 | - |
| 0.4500 | 10550 | 0.0 | - |
| 0.4521 | 10600 | 0.0 | - |
| 0.4543 | 10650 | 0.0041 | - |
| 0.4564 | 10700 | 0.0073 | - |
| 0.4585 | 10750 | 0.0051 | - |
| 0.4607 | 10800 | 0.0 | - |
| 0.4628 | 10850 | 0.0 | - |
| 0.4649 | 10900 | 0.0 | - |
| 0.4671 | 10950 | 0.0001 | - |
| 0.4692 | 11000 | 0.0 | - |
| 0.4713 | 11050 | 0.1696 | - |
| 0.4735 | 11100 | 0.0 | - |
| 0.4756 | 11150 | 0.1243 | - |
| 0.4777 | 11200 | 0.0 | - |
| 0.4799 | 11250 | 0.0 | - |
| 0.4820 | 11300 | 0.0003 | - |
| 0.4841 | 11350 | 0.0707 | - |
| 0.4863 | 11400 | 0.166 | - |
| 0.4884 | 11450 | 0.4964 | - |
| 0.4905 | 11500 | 0.0023 | - |
| 0.4927 | 11550 | 0.0 | - |
| 0.4948 | 11600 | 0.0 | - |
| 0.4969 | 11650 | 0.173 | - |
| 0.4991 | 11700 | 0.0 | - |
| 0.5012 | 11750 | 0.0004 | - |
| 0.5033 | 11800 | 0.0 | - |
| 0.5055 | 11850 | 0.125 | - |
| 0.5076 | 11900 | 0.0042 | - |
| 0.5097 | 11950 | 0.012 | - |
| 0.5119 | 12000 | 0.0046 | - |
| 0.5140 | 12050 | 0.0001 | - |
| 0.5161 | 12100 | 0.0062 | - |
| 0.5183 | 12150 | 0.0 | - |
| 0.5204 | 12200 | 0.017 | - |
| 0.5225 | 12250 | 0.2668 | - |
| 0.5247 | 12300 | 0.0986 | - |
| 0.5268 | 12350 | 0.0071 | - |
| 0.5289 | 12400 | 0.0055 | - |
| 0.5311 | 12450 | 0.006 | - |
| 0.5332 | 12500 | 0.0057 | - |
| 0.5353 | 12550 | 0.0044 | - |
| 0.5375 | 12600 | 0.0039 | - |
| 0.5396 | 12650 | 0.1685 | - |
| 0.5417 | 12700 | 0.125 | - |
| 0.5438 | 12750 | 0.0026 | - |
| 0.5460 | 12800 | 0.0 | - |
| 0.5481 | 12850 | 0.0 | - |
| 0.5502 | 12900 | 0.1024 | - |
| 0.5524 | 12950 | 0.0 | - |
| 0.5545 | 13000 | 0.0 | - |
| 0.5566 | 13050 | 0.0083 | - |
| 0.5588 | 13100 | 0.0 | - |
| 0.5609 | 13150 | 0.0001 | - |
| 0.5630 | 13200 | 0.0 | - |
| 0.5652 | 13250 | 0.095 | - |
| 0.5673 | 13300 | 0.0001 | - |
| 0.5694 | 13350 | 0.0026 | - |
| 0.5716 | 13400 | 0.0 | - |
| 0.5737 | 13450 | 0.0041 | - |
| 0.5758 | 13500 | 0.1654 | - |
| 0.5780 | 13550 | 0.0003 | - |
| 0.5801 | 13600 | 0.0056 | - |
| 0.5822 | 13650 | 0.0 | - |
| 0.5844 | 13700 | 0.1012 | - |
| 0.5865 | 13750 | 0.0 | - |
| 0.5886 | 13800 | 0.0001 | - |
| 0.5908 | 13850 | 0.0042 | - |
| 0.5929 | 13900 | 0.0122 | - |
| 0.5950 | 13950 | 0.1047 | - |
| 0.5972 | 14000 | 0.0 | - |
| 0.5993 | 14050 | 0.0121 | - |
| 0.6014 | 14100 | 0.0 | - |
| 0.6036 | 14150 | 0.0 | - |
| 0.6057 | 14200 | 0.0 | - |
| 0.6078 | 14250 | 0.0105 | - |
| 0.6100 | 14300 | 0.0 | - |
| 0.6121 | 14350 | 0.011 | - |
| 0.6142 | 14400 | 0.0329 | - |
| 0.6164 | 14450 | 0.0942 | - |
| 0.6185 | 14500 | 0.0173 | - |
| 0.6206 | 14550 | 0.0 | - |
| 0.6228 | 14600 | 0.1032 | - |
| 0.6249 | 14650 | 0.016 | - |
| 0.6270 | 14700 | 0.0079 | - |
| 0.6292 | 14750 | 0.0 | - |
| 0.6313 | 14800 | 0.1088 | - |
| 0.6334 | 14850 | 0.0091 | - |
| 0.6356 | 14900 | 0.0039 | - |
| 0.6377 | 14950 | 0.0 | - |
| 0.6398 | 15000 | 0.0 | - |
| 0.6420 | 15050 | 0.0 | - |
| 0.6441 | 15100 | 0.1654 | - |
| 0.6462 | 15150 | 0.0 | - |
| 0.6484 | 15200 | 0.0002 | - |
| 0.6505 | 15250 | 0.0 | - |
| 0.6526 | 15300 | 0.1745 | - |
| 0.6548 | 15350 | 0.0 | - |
| 0.6569 | 15400 | 0.156 | - |
| 0.6590 | 15450 | 0.0 | - |
| 0.6611 | 15500 | 0.0 | - |
| 0.6633 | 15550 | 0.1755 | - |
| 0.6654 | 15600 | 0.008 | - |
| 0.6675 | 15650 | 0.0 | - |
| 0.6697 | 15700 | 0.0 | - |
| 0.6718 | 15750 | 0.0041 | - |
| 0.6739 | 15800 | 0.0037 | - |
| 0.6761 | 15850 | 0.0 | - |
| 0.6782 | 15900 | 0.0 | - |
| 0.6803 | 15950 | 0.0092 | - |
| 0.6825 | 16000 | 0.0071 | - |
| 0.6846 | 16050 | 0.0053 | - |
| 0.6867 | 16100 | 0.0 | - |
| 0.6889 | 16150 | 0.004 | - |
| 0.6910 | 16200 | 0.0036 | - |
| 0.6931 | 16250 | 0.0 | - |
| 0.6953 | 16300 | 0.0 | - |
| 0.6974 | 16350 | 0.184 | - |
| 0.6995 | 16400 | 0.0 | - |
| 0.7017 | 16450 | 0.0133 | - |
| 0.7038 | 16500 | 0.0 | - |
| 0.7059 | 16550 | 0.174 | - |
| 0.7081 | 16600 | 0.0 | - |
| 0.7102 | 16650 | 0.0233 | - |
| 0.7123 | 16700 | 0.0117 | - |
| 0.7145 | 16750 | 0.0272 | - |
| 0.7166 | 16800 | 0.0095 | - |
| 0.7187 | 16850 | 0.0 | - |
| 0.7209 | 16900 | 0.1656 | - |
| 0.7230 | 16950 | 0.0055 | - |
| 0.7251 | 17000 | 0.0 | - |
| 0.7273 | 17050 | 0.1716 | - |
| 0.7294 | 17100 | 0.0 | - |
| 0.7315 | 17150 | 0.0 | - |
| 0.7337 | 17200 | 0.1035 | - |
| 0.7358 | 17250 | 0.0694 | - |
| 0.7379 | 17300 | 0.1733 | - |
| 0.7401 | 17350 | 0.0092 | - |
| 0.7422 | 17400 | 0.1656 | - |
| 0.7443 | 17450 | 0.0 | - |
| 0.7465 | 17500 | 0.1655 | - |
| 0.7486 | 17550 | 0.0059 | - |
| 0.7507 | 17600 | 0.1116 | - |
| 0.7529 | 17650 | 0.0 | - |
| 0.7550 | 17700 | 0.0068 | - |
| 0.7571 | 17750 | 0.0053 | - |
| 0.7593 | 17800 | 0.0 | - |
| 0.7614 | 17850 | 0.0062 | - |
| 0.7635 | 17900 | 0.0104 | - |
| 0.7657 | 17950 | 0.1727 | - |
| 0.7678 | 18000 | 0.0 | - |
| 0.7699 | 18050 | 0.0 | - |
| 0.7721 | 18100 | 0.0 | - |
| 0.7742 | 18150 | 0.0714 | - |
| 0.7763 | 18200 | 0.0 | - |
| 0.7785 | 18250 | 0.0 | - |
| 0.7806 | 18300 | 0.0002 | - |
| 0.7827 | 18350 | 0.0 | - |
| 0.7848 | 18400 | 0.0 | - |
| 0.7870 | 18450 | 0.0996 | - |
| 0.7891 | 18500 | 0.0 | - |
| 0.7912 | 18550 | 0.0 | - |
| 0.7934 | 18600 | 0.0139 | - |
| 0.7955 | 18650 | 0.0 | - |
| 0.7976 | 18700 | 0.1701 | - |
| 0.7998 | 18750 | 0.0 | - |
| 0.8019 | 18800 | 0.0001 | - |
| 0.8040 | 18850 | 0.0 | - |
| 0.8062 | 18900 | 0.0 | - |
| 0.8083 | 18950 | 0.0 | - |
| 0.8104 | 19000 | 0.0 | - |
| 0.8126 | 19050 | 0.0 | - |
| 0.8147 | 19100 | 0.1093 | - |
| 0.8168 | 19150 | 0.0 | - |
| 0.8190 | 19200 | 0.0 | - |
| 0.8211 | 19250 | 0.0075 | - |
| 0.8232 | 19300 | 0.1079 | - |
| 0.8254 | 19350 | 0.0112 | - |
| 0.8275 | 19400 | 0.1655 | - |
| 0.8296 | 19450 | 0.0152 | - |
| 0.8318 | 19500 | 0.1152 | - |
| 0.8339 | 19550 | 0.0 | - |
| 0.8360 | 19600 | 0.0 | - |
| 0.8382 | 19650 | 0.0079 | - |
| 0.8403 | 19700 | 0.0 | - |
| 0.8424 | 19750 | 0.0 | - |
| 0.8446 | 19800 | 0.0 | - |
| 0.8467 | 19850 | 0.0 | - |
| 0.8488 | 19900 | 0.1161 | - |
| 0.8510 | 19950 | 0.0057 | - |
| 0.8531 | 20000 | 0.0 | - |
| 0.8552 | 20050 | 0.0046 | - |
| 0.8574 | 20100 | 0.0 | - |
| 0.8595 | 20150 | 0.0068 | - |
| 0.8616 | 20200 | 0.0 | - |
| 0.8638 | 20250 | 0.0 | - |
| 0.8659 | 20300 | 0.0 | - |
| 0.8680 | 20350 | 0.0 | - |
| 0.8702 | 20400 | 0.0141 | - |
| 0.8723 | 20450 | 0.0001 | - |
| 0.8744 | 20500 | 0.0 | - |
| 0.8766 | 20550 | 0.0 | - |
| 0.8787 | 20600 | 0.0171 | - |
| 0.8808 | 20650 | 0.0 | - |
| 0.8830 | 20700 | 0.0 | - |
| 0.8851 | 20750 | 0.0077 | - |
| 0.8872 | 20800 | 0.0 | - |
| 0.8894 | 20850 | 0.0 | - |
| 0.8915 | 20900 | 0.0 | - |
| 0.8936 | 20950 | 0.0 | - |
| 0.8958 | 21000 | 0.0 | - |
| 0.8979 | 21050 | 0.0 | - |
| 0.9000 | 21100 | 0.0 | - |
| 0.9021 | 21150 | 0.0 | - |
| 0.9043 | 21200 | 0.0 | - |
| 0.9064 | 21250 | 0.1048 | - |
| 0.9085 | 21300 | 0.006 | - |
| 0.9107 | 21350 | 0.0 | - |
| 0.9128 | 21400 | 0.0 | - |
| 0.9149 | 21450 | 0.005 | - |
| 0.9171 | 21500 | 0.0 | - |
| 0.9192 | 21550 | 0.0325 | - |
| 0.9213 | 21600 | 0.0136 | - |
| 0.9235 | 21650 | 0.0 | - |
| 0.9256 | 21700 | 0.0062 | - |
| 0.9277 | 21750 | 0.1656 | - |
| 0.9299 | 21800 | 0.1648 | - |
| 0.9320 | 21850 | 0.0 | - |
| 0.9341 | 21900 | 0.0 | - |
| 0.9363 | 21950 | 0.0 | - |
| 0.9384 | 22000 | 0.2844 | - |
| 0.9405 | 22050 | 0.0 | - |
| 0.9427 | 22100 | 0.0 | - |
| 0.9448 | 22150 | 0.0 | - |
| 0.9469 | 22200 | 0.0 | - |
| 0.9491 | 22250 | 0.0 | - |
| 0.9512 | 22300 | 0.2096 | - |
| 0.9533 | 22350 | 0.0073 | - |
| 0.9555 | 22400 | 0.006 | - |
| 0.9576 | 22450 | 0.0 | - |
| 0.9597 | 22500 | 0.0079 | - |
| 0.9619 | 22550 | 0.0071 | - |
| 0.9640 | 22600 | 0.0 | - |
| 0.9661 | 22650 | 0.006 | - |
| 0.9683 | 22700 | 0.1048 | - |
| 0.9704 | 22750 | 0.007 | - |
| 0.9725 | 22800 | 0.0 | - |
| 0.9747 | 22850 | 0.0 | - |
| 0.9768 | 22900 | 0.007 | - |
| 0.9789 | 22950 | 0.0 | - |
| 0.9811 | 23000 | 0.1049 | - |
| 0.9832 | 23050 | 0.0069 | - |
| 0.9853 | 23100 | 0.0 | - |
| 0.9875 | 23150 | 0.0 | - |
| 0.9896 | 23200 | 0.0 | - |
| 0.9917 | 23250 | 0.0 | - |
| 0.9939 | 23300 | 0.007 | - |
| 0.9960 | 23350 | 0.0147 | - |
| 0.9981 | 23400 | 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}
}
```