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pahri
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setfit-indo-resto-RM-ibu-imas-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
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SetFit Aspect Model

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). A LogisticRegression 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 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 Sources

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:

pip install setfit

Then you can load this model and run inference.

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 

@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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