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marcelomoreno26
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all-MiniLM-L6-v2-absa-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Eval Results
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SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. 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
aspect
  • 'cord:I charge it at night and skip taking the cord with me because of the good battery life.'
  • 'battery life:I charge it at night and skip taking the cord with me because of the good battery life.'
  • 'service center:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
no aspect
  • 'night:I charge it at night and skip taking the cord with me because of the good battery life.'
  • 'skip:I charge it at night and skip taking the cord with me because of the good battery life.'
  • 'exchange:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'

Evaluation

Metrics

Label Accuracy
all 0.8948

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(
    "marcelomoreno26/all-MiniLM-L6-v2-absa-aspect2",
    "setfit-absa-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 21.9670 75
Label Training Sample Count
no aspect 690
aspect 644

Training Hyperparameters

  • batch_size: (16, 2)
  • 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: False
  • 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 0.3662 -
0.0015 50 0.3374 -
0.0029 100 0.3411 -
0.0044 150 0.2945 -
0.0059 200 0.2944 -
0.0073 250 0.2942 -
0.0088 300 0.2409 -
0.0103 350 0.2817 -
0.0118 400 0.3149 -
0.0132 450 0.2618 -
0.0147 500 0.247 -
0.0162 550 0.2883 -
0.0176 600 0.2783 -
0.0191 650 0.2418 -
0.0206 700 0.2938 -
0.0220 750 0.2376 -
0.0235 800 0.2652 -
0.0250 850 0.2442 -
0.0265 900 0.2678 -
0.0279 950 0.2216 -
0.0294 1000 0.1816 -
0.0309 1050 0.1102 -
0.0323 1100 0.2985 -
0.0338 1150 0.1124 -
0.0353 1200 0.1075 -
0.0367 1250 0.0819 -
0.0382 1300 0.1238 -
0.0397 1350 0.0529 -
0.0412 1400 0.026 -
0.0426 1450 0.0289 -
0.0441 1500 0.067 -
0.0456 1550 0.0276 -
0.0470 1600 0.0162 -
0.0485 1650 0.0083 -
0.0500 1700 0.0017 -
0.0514 1750 0.0028 -
0.0529 1800 0.0045 -
0.0544 1850 0.0022 -
0.0558 1900 0.0014 -
0.0573 1950 0.0059 -
0.0588 2000 0.0019 -
0.0603 2050 0.0014 -
0.0617 2100 0.0022 -
0.0632 2150 0.0005 -
0.0647 2200 0.0008 -
0.0661 2250 0.0005 -
0.0676 2300 0.0006 -
0.0691 2350 0.0003 -
0.0705 2400 0.0007 -
0.0720 2450 0.0005 -
0.0735 2500 0.0005 -
0.0750 2550 0.0612 -
0.0764 2600 0.0004 -
0.0779 2650 0.041 -
0.0794 2700 0.0002 -
0.0808 2750 0.0003 -
0.0823 2800 0.0002 -
0.0838 2850 0.0002 -
0.0852 2900 0.0002 -
0.0867 2950 0.0004 -
0.0882 3000 0.0006 -
0.0897 3050 0.0601 -
0.0911 3100 0.0002 -
0.0926 3150 0.0108 -
0.0941 3200 0.0003 -
0.0955 3250 0.0363 -
0.0970 3300 0.0006 -
0.0985 3350 0.0002 -
0.0999 3400 0.0033 -
0.1014 3450 0.0002 -
0.1029 3500 0.0002 -
0.1044 3550 0.0006 -
0.1058 3600 0.0002 -
0.1073 3650 0.0002 -
0.1088 3700 0.0001 -
0.1102 3750 0.0002 -
0.1117 3800 0.0002 -
0.1132 3850 0.0004 -
0.1146 3900 0.0003 -
0.1161 3950 0.0001 -
0.1176 4000 0.0004 -
0.1190 4050 0.0003 -
0.1205 4100 0.001 -
0.1220 4150 0.0002 -
0.1235 4200 0.0001 -
0.1249 4250 0.0003 -
0.1264 4300 0.0003 -
0.1279 4350 0.0002 -
0.1293 4400 0.0001 -
0.1308 4450 0.0001 -
0.1323 4500 0.0001 -
0.1337 4550 0.0001 -
0.1352 4600 0.0001 -
0.1367 4650 0.0003 -
0.1382 4700 0.0006 -
0.1396 4750 0.0003 -
0.1411 4800 0.0001 -
0.1426 4850 0.0011 -
0.1440 4900 0.0001 -
0.1455 4950 0.0001 -
0.1470 5000 0.0001 -
0.1484 5050 0.0001 -
0.1499 5100 0.0002 -
0.1514 5150 0.0497 -
0.1529 5200 0.0002 -
0.1543 5250 0.0001 -
0.1558 5300 0.0008 -
0.1573 5350 0.0001 -
0.1587 5400 0.0002 -
0.1602 5450 0.0001 -
0.1617 5500 0.0003 -
0.1631 5550 0.0003 -
0.1646 5600 0.0004 -
0.1661 5650 0.0002 -
0.1675 5700 0.0002 -
0.1690 5750 0.0001 -
0.1705 5800 0.0001 -
0.1720 5850 0.0001 -
0.1734 5900 0.0004 -
0.1749 5950 0.0001 -
0.1764 6000 0.0001 -
0.1778 6050 0.0001 -
0.125 1 0.0002 -
0.5 4 0.0003 -
1.0 8 0.0 -
0.0000 1 0.0001 -
0.0015 50 0.0001 -
0.0029 100 0.0 -
0.0044 150 0.0001 -
0.125 1 0.0 -
0.5 4 0.0 -
0.0000 1 0.0003 -
0.0009 50 0.0003 -
0.0018 100 0.0003 -
0.0027 150 0.0001 -
0.0036 200 0.0001 -
0.0045 250 0.1015 -
0.0054 300 0.0005 -
0.0063 350 0.0579 -
0.0072 400 0.0001 -
0.0081 450 0.0897 -
0.0090 500 0.0618 -
0.0099 550 0.0002 -
0.0108 600 0.0001 -
0.0117 650 0.0004 -
0.0126 700 0.0002 -
0.0135 750 0.0002 -
0.0143 800 0.0001 -
0.0152 850 0.062 -
0.0161 900 0.0004 -
0.0170 950 0.0002 -
0.0179 1000 0.0001 -
0.0188 1050 0.0628 -
0.0197 1100 0.0003 -
0.0206 1150 0.0003 -
0.0215 1200 0.0001 -
0.0224 1250 0.0001 -
0.0233 1300 0.0001 -
0.0000 1 0.0002 -
0.0009 50 0.0002 -
0.0018 100 0.0001 -
0.0027 150 0.0001 -
0.0036 200 0.0001 -
0.0045 250 0.0002 -
0.0054 300 0.0001 -
0.0063 350 0.0002 -
0.0072 400 0.0002 -
0.0081 450 0.0262 -
0.0090 500 0.0001 -
0.0099 550 0.0002 -
0.0108 600 0.0001 -
0.0117 650 0.0001 -
0.0126 700 0.0001 -
0.0135 750 0.0001 -
0.0143 800 0.0001 -
0.0152 850 0.0002 -
0.0161 900 0.0001 -
0.0170 950 0.0001 -
0.0179 1000 0.0001 -
0.0188 1050 0.06 -
0.0197 1100 0.0001 -
0.0206 1150 0.0001 -
0.0215 1200 0.0001 -
0.0224 1250 0.0001 -
0.0233 1300 0.0001 -
0.0242 1350 0.0001 -
0.0251 1400 0.0001 -
0.0260 1450 0.0001 -
0.0269 1500 0.0002 -
0.0278 1550 0.0001 -
0.0287 1600 0.0001 -
0.0296 1650 0.0125 -
0.0305 1700 0.0001 -
0.0314 1750 0.0001 -
0.0323 1800 0.0001 -
0.0332 1850 0.0001 -
0.0341 1900 0.0001 -
0.0350 1950 0.0001 -
0.0359 2000 0.0001 -
0.0368 2050 0.0001 -
0.0377 2100 0.0002 -
0.0386 2150 0.0001 -
0.0395 2200 0.0001 -
0.0404 2250 0.0407 -
0.0412 2300 0.0001 -
0.0421 2350 0.0001 -
0.0430 2400 0.0001 -
0.0439 2450 0.0001 -
0.0448 2500 0.0001 -
0.0457 2550 0.0 -
0.0466 2600 0.0 -
0.0475 2650 0.0001 -
0.0484 2700 0.0 -
0.0493 2750 0.0001 -
0.0502 2800 0.0001 -
0.0511 2850 0.0001 -
0.0520 2900 0.0001 -
0.0529 2950 0.0002 -
0.0538 3000 0.0001 -
0.0547 3050 0.0001 -
0.0556 3100 0.0001 -
0.0565 3150 0.0001 -
0.0574 3200 0.0 -
0.0583 3250 0.0 -
0.0592 3300 0.0 -
0.0601 3350 0.0001 -
0.0610 3400 0.0 -
0.0619 3450 0.0 -
0.0628 3500 0.0001 -
0.0637 3550 0.0001 -
0.0646 3600 0.0 -
0.0655 3650 0.0001 -
0.0664 3700 0.0 -
0.0673 3750 0.0001 -
0.0681 3800 0.0 -
0.0690 3850 0.0005 -
0.0699 3900 0.0001 -
0.0708 3950 0.0001 -
0.0717 4000 0.0 -
0.0726 4050 0.0001 -
0.0735 4100 0.0009 -
0.0744 4150 0.0001 -
0.0753 4200 0.0001 -
0.0762 4250 0.0001 -
0.0771 4300 0.0 -
0.0780 4350 0.0001 -
0.0789 4400 0.0001 -
0.0798 4450 0.0001 -
0.0807 4500 0.0 -
0.0816 4550 0.0 -
0.0825 4600 0.0001 -
0.0834 4650 0.0 -
0.0843 4700 0.0 -
0.0852 4750 0.0 -
0.0861 4800 0.0 -
0.0870 4850 0.0 -
0.0879 4900 0.0004 -
0.0888 4950 0.0002 -
0.0897 5000 0.0001 -
0.0906 5050 0.0001 -
0.0915 5100 0.0 -
0.0924 5150 0.0026 -
0.0933 5200 0.0549 -
0.0942 5250 0.0001 -
0.0950 5300 0.0011 -
0.0959 5350 0.0 -
0.0968 5400 0.0 -
0.0977 5450 0.0 -
0.0986 5500 0.0002 -
0.0995 5550 0.0001 -
0.1004 5600 0.0 -
0.1013 5650 0.0001 -
0.1022 5700 0.0001 -
0.1031 5750 0.0 -
0.1040 5800 0.0 -
0.1049 5850 0.0 -
0.1058 5900 0.0203 -
0.1067 5950 0.0001 -
0.1076 6000 0.0 -
0.1085 6050 0.0 -
0.1094 6100 0.0 -
0.1103 6150 0.0 -
0.1112 6200 0.0001 -
0.1121 6250 0.0 -
0.1130 6300 0.0 -
0.1139 6350 0.0 -
0.1148 6400 0.0 -
0.1157 6450 0.0164 -
0.1166 6500 0.0001 -
0.1175 6550 0.0 -
0.1184 6600 0.0001 -
0.1193 6650 0.0002 -
0.1202 6700 0.0001 -
0.1211 6750 0.0 -
0.1219 6800 0.0 -
0.1228 6850 0.0 -
0.1237 6900 0.0 -
0.1246 6950 0.0 -
0.1255 7000 0.0001 -
0.1264 7050 0.0 -
0.1273 7100 0.0 -
0.1282 7150 0.0 -
0.1291 7200 0.0002 -
0.1300 7250 0.0 -
0.1309 7300 0.0 -
0.1318 7350 0.0 -
0.1327 7400 0.0 -
0.1336 7450 0.0 -
0.1345 7500 0.0002 -
0.1354 7550 0.0 -
0.1363 7600 0.0 -
0.1372 7650 0.0001 -
0.1381 7700 0.0001 -
0.1390 7750 0.0001 -
0.1399 7800 0.0001 -
0.1408 7850 0.0 -
0.1417 7900 0.0 -
0.1426 7950 0.0 -
0.1435 8000 0.0142 -
0.1444 8050 0.0001 -
0.1453 8100 0.0 -
0.1462 8150 0.0002 -
0.1471 8200 0.0 -
0.1480 8250 0.0 -
0.1488 8300 0.0 -
0.1497 8350 0.0 -
0.1506 8400 0.0003 -
0.1515 8450 0.0 -
0.1524 8500 0.0 -
0.1533 8550 0.0 -
0.1542 8600 0.0 -
0.1551 8650 0.0 -
0.1560 8700 0.0 -
0.1569 8750 0.0 -
0.1578 8800 0.0 -
0.1587 8850 0.0 -
0.1596 8900 0.0 -
0.1605 8950 0.0 -
0.1614 9000 0.0 -
0.1623 9050 0.0 -
0.1632 9100 0.0 -
0.1641 9150 0.0 -
0.1650 9200 0.0 -
0.1659 9250 0.0001 -
0.1668 9300 0.0 -
0.1677 9350 0.0 -
0.1686 9400 0.0 -
0.1695 9450 0.0 -
0.1704 9500 0.0 -
0.1713 9550 0.0 -
0.1722 9600 0.0 -
0.1731 9650 0.0 -
0.1740 9700 0.0 -
0.1749 9750 0.0 -
0.1758 9800 0.0 -
0.1766 9850 0.0 -
0.1775 9900 0.0 -
0.1784 9950 0.0 -
0.1793 10000 0.0 -
0.1802 10050 0.0097 -
0.1811 10100 0.0 -
0.1820 10150 0.0 -
0.1829 10200 0.0 -
0.1838 10250 0.0 -
0.1847 10300 0.0001 -
0.1856 10350 0.0 -
0.1865 10400 0.0 -
0.1874 10450 0.0 -
0.1883 10500 0.0 -
0.1892 10550 0.0 -
0.1901 10600 0.0 -
0.1910 10650 0.0 -
0.1919 10700 0.0 -
0.1928 10750 0.0 -
0.1937 10800 0.0 -
0.1946 10850 0.0 -
0.1955 10900 0.0 -
0.1964 10950 0.0 -
0.1973 11000 0.0001 -
0.1982 11050 0.0 -
0.1991 11100 0.0 -
0.2000 11150 0.0 -
0.2009 11200 0.0 -
0.2018 11250 0.0004 -
0.2027 11300 0.0001 -
0.2035 11350 0.0001 -
0.2044 11400 0.0 -
0.2053 11450 0.0001 -
0.2062 11500 0.0 -
0.2071 11550 0.0001 -
0.2080 11600 0.0 -
0.2089 11650 0.0 -
0.2098 11700 0.0 -
0.2107 11750 0.0 -
0.2116 11800 0.0 -
0.2125 11850 0.0 -
0.2134 11900 0.0 -
0.2143 11950 0.0001 -
0.2152 12000 0.0 -
0.2161 12050 0.0 -
0.2170 12100 0.0 -
0.2179 12150 0.0 -
0.2188 12200 0.0 -
0.2197 12250 0.0 -
0.2206 12300 0.0 -
0.2215 12350 0.0 -
0.2224 12400 0.0 -
0.2233 12450 0.0 -
0.2242 12500 0.0 -
0.2251 12550 0.0 -
0.2260 12600 0.0 -
0.2269 12650 0.0 -
0.2278 12700 0.0 -
0.2287 12750 0.0 -
0.2296 12800 0.0 -
0.2304 12850 0.0 -
0.2313 12900 0.0 -
0.2322 12950 0.0 -
0.2331 13000 0.0 -
0.2340 13050 0.0 -
0.2349 13100 0.0 -
0.2358 13150 0.0264 -
0.2367 13200 0.0 -
0.2376 13250 0.0 -
0.2385 13300 0.0 -
0.2394 13350 0.0 -
0.2403 13400 0.0 -
0.2412 13450 0.0 -
0.2421 13500 0.0 -
0.2430 13550 0.0 -
0.2439 13600 0.0 -
0.2448 13650 0.0 -
0.2457 13700 0.0 -
0.2466 13750 0.0 -
0.2475 13800 0.0 -
0.2484 13850 0.0 -
0.2493 13900 0.0 -
0.2502 13950 0.0 -
0.2511 14000 0.0 -
0.2520 14050 0.0 -
0.2529 14100 0.0 -
0.2538 14150 0.0001 -
0.2547 14200 0.0 -
0.2556 14250 0.0 -
0.2565 14300 0.0 -
0.2573 14350 0.0 -
0.2582 14400 0.0 -
0.2591 14450 0.0 -
0.2600 14500 0.0 -
0.2609 14550 0.0001 -
0.2618 14600 0.0 -
0.2627 14650 0.0 -
0.2636 14700 0.0 -
0.2645 14750 0.0 -
0.2654 14800 0.0 -
0.2663 14850 0.0 -
0.2672 14900 0.0 -
0.2681 14950 0.0001 -
0.2690 15000 0.0 -
0.2699 15050 0.0 -
0.2708 15100 0.0 -
0.2717 15150 0.0 -
0.2726 15200 0.0 -
0.2735 15250 0.0 -
0.2744 15300 0.0 -
0.2753 15350 0.0 -
0.2762 15400 0.0 -
0.2771 15450 0.0 -
0.2780 15500 0.0001 -
0.2789 15550 0.0621 -
0.2798 15600 0.0056 -
0.2807 15650 0.0 -
0.2816 15700 0.0 -
0.2825 15750 0.0145 -
0.2834 15800 0.0 -
0.2842 15850 0.0 -
0.2851 15900 0.0166 -
0.2860 15950 0.0 -
0.2869 16000 0.0 -
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0.2887 16100 0.0166 -
0.2896 16150 0.0 -
0.2905 16200 0.0 -
0.2914 16250 0.0169 -
0.2923 16300 0.0 -
0.2932 16350 0.0 -
0.2941 16400 0.0 -
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0.2986 16650 0.0 -
0.2995 16700 0.0 -
0.3004 16750 0.0 -
0.3013 16800 0.0 -
0.3022 16850 0.0 -
0.3031 16900 0.0 -
0.3040 16950 0.0 -
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0.3058 17050 0.0138 -
0.3067 17100 0.0 -
0.3076 17150 0.0 -
0.3085 17200 0.0 -
0.3094 17250 0.0 -
0.3103 17300 0.0 -
0.3111 17350 0.0 -
0.3120 17400 0.0 -
0.3129 17450 0.0001 -
0.3138 17500 0.0 -
0.3147 17550 0.0 -
0.3156 17600 0.0 -
0.3165 17650 0.0 -
0.3174 17700 0.0 -
0.3183 17750 0.0 -
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0.3201 17850 0.0 -
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0.3219 17950 0.0001 -
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0.3255 18150 0.0 -
0.3264 18200 0.0 -
0.3273 18250 0.0 -
0.3282 18300 0.0 -
0.3291 18350 0.0 -
0.3300 18400 0.0 -
0.3309 18450 0.0003 -
0.3318 18500 0.0 -
0.3327 18550 0.0 -
0.3336 18600 0.0 -
0.3345 18650 0.0 -
0.3354 18700 0.0 -
0.3363 18750 0.0 -
0.3372 18800 0.0 -
0.3380 18850 0.0 -
0.3389 18900 0.0 -
0.3398 18950 0.0 -
0.3407 19000 0.0 -
0.3416 19050 0.0 -
0.3425 19100 0.0 -
0.3434 19150 0.0 -
0.3443 19200 0.0 -
0.3452 19250 0.0 -
0.3461 19300 0.0 -
0.3470 19350 0.0 -
0.3479 19400 0.0 -
0.3488 19450 0.0 -
0.3497 19500 0.0001 -
0.3506 19550 0.0131 -
0.3515 19600 0.0 -
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0.3542 19750 0.0 -
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0.3632 20250 0.0 -
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0.3990 22250 0.0 -
0.3999 22300 0.0 -
0.4008 22350 0.0 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • spaCy: 3.7.4
  • Transformers: 4.40.1
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.19.0
  • Tokenizers: 0.19.1

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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