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NLBSE
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nlbse26_java

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
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nlbse26_java / README.md
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: '@link FSNamesystem#readLock() | FSPermissionChecker.java'
  - text: previous^checkpoint li | TestSaveNamespace.java
  - text: // the file doesn't have anything | TaskLog.java
  - text: " @param file the file the include directives point to\n\t * @param depth depth to which includes are followed, should be one of\n\t * {@link #DEPTH_ZERO} or {@link #DEPTH_INFINITE}\n\t * @return an array of include relations\n\t * @throws CoreException | IIndex.java"
  - text: // quotes are removed | ScannerUtility.java
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false

SetFit

This is a SetFit model that can be used for Text Classification. A MultiOutputClassifier 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 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 MultiOutputClassifier instance
  • Maximum Sequence Length: 128 tokens

Model Sources

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 SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("// quotes are removed | ScannerUtility.java")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 15.5217 299

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (5, 5)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • 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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.3106 -
0.0074 50 0.3017 -
0.0148 100 0.2776 -
0.0222 150 0.2638 -
0.0297 200 0.2522 -
0.0371 250 0.2441 -
0.0445 300 0.2316 -
0.0519 350 0.2212 -
0.0593 400 0.205 -
0.0667 450 0.1928 -
0.0742 500 0.1815 -
0.0816 550 0.1649 -
0.0890 600 0.1579 -
0.0964 650 0.1499 -
0.1038 700 0.1426 -
0.1112 750 0.1467 -
0.1186 800 0.1393 -
0.1261 850 0.1273 -
0.1335 900 0.1201 -
0.1409 950 0.1202 -
0.1483 1000 0.1144 -
0.1557 1050 0.112 -
0.1631 1100 0.1067 -
0.1705 1150 0.0966 -
0.1780 1200 0.1005 -
0.1854 1250 0.1001 -
0.1928 1300 0.0972 -
0.2002 1350 0.0866 -
0.2076 1400 0.0794 -
0.2150 1450 0.0863 -
0.2225 1500 0.0831 -
0.2299 1550 0.082 -
0.2373 1600 0.0856 -
0.2447 1650 0.0808 -
0.2521 1700 0.0741 -
0.2595 1750 0.0768 -
0.2669 1800 0.0743 -
0.2744 1850 0.0692 -
0.2818 1900 0.07 -
0.2892 1950 0.0651 -
0.2966 2000 0.0701 -
0.3040 2050 0.0655 -
0.3114 2100 0.0592 -
0.3188 2150 0.0613 -
0.3263 2200 0.0597 -
0.3337 2250 0.0579 -
0.3411 2300 0.0535 -
0.3485 2350 0.0491 -
0.3559 2400 0.0506 -
0.3633 2450 0.055 -
0.3708 2500 0.0512 -
0.3782 2550 0.0553 -
0.3856 2600 0.0525 -
0.3930 2650 0.0478 -
0.4004 2700 0.0401 -
0.4078 2750 0.0479 -
0.4152 2800 0.0421 -
0.4227 2850 0.0369 -
0.4301 2900 0.0418 -
0.4375 2950 0.0424 -
0.4449 3000 0.0378 -
0.4523 3050 0.0406 -
0.4597 3100 0.0346 -
0.4672 3150 0.042 -
0.4746 3200 0.0331 -
0.4820 3250 0.0345 -
0.4894 3300 0.0388 -
0.4968 3350 0.0357 -
0.5042 3400 0.0269 -
0.5116 3450 0.0367 -
0.5191 3500 0.033 -
0.5265 3550 0.0298 -
0.5339 3600 0.0264 -
0.5413 3650 0.0347 -
0.5487 3700 0.032 -
0.5561 3750 0.0287 -
0.5635 3800 0.0276 -
0.5710 3850 0.0299 -
0.5784 3900 0.0286 -
0.5858 3950 0.027 -
0.5932 4000 0.0257 -
0.6006 4050 0.023 -
0.6080 4100 0.0249 -
0.6155 4150 0.0217 -
0.6229 4200 0.0234 -
0.6303 4250 0.0271 -
0.6377 4300 0.0211 -
0.6451 4350 0.0254 -
0.6525 4400 0.0189 -
0.6599 4450 0.0196 -
0.6674 4500 0.0222 -
0.6748 4550 0.0225 -
0.6822 4600 0.0175 -
0.6896 4650 0.0205 -
0.6970 4700 0.0191 -
0.7044 4750 0.0154 -
0.7118 4800 0.022 -
0.7193 4850 0.0149 -
0.7267 4900 0.0173 -
0.7341 4950 0.0181 -
0.7415 5000 0.0189 -
0.7489 5050 0.0195 -
0.7563 5100 0.0138 -
0.7638 5150 0.0169 -
0.7712 5200 0.0147 -
0.7786 5250 0.02 -
0.7860 5300 0.0156 -
0.7934 5350 0.0159 -
0.8008 5400 0.0151 -
0.8082 5450 0.0145 -
0.8157 5500 0.013 -
0.8231 5550 0.0139 -
0.8305 5600 0.0124 -
0.8379 5650 0.0165 -
0.8453 5700 0.0083 -
0.8527 5750 0.0105 -
0.8602 5800 0.01 -
0.8676 5850 0.0143 -
0.8750 5900 0.0155 -
0.8824 5950 0.0158 -
0.8898 6000 0.012 -
0.8972 6050 0.0166 -
0.9046 6100 0.0149 -
0.9121 6150 0.017 -
0.9195 6200 0.0183 -
0.9269 6250 0.0126 -
0.9343 6300 0.018 -
0.9417 6350 0.013 -
0.9491 6400 0.0165 -
0.9565 6450 0.0097 -
0.9640 6500 0.0088 -
0.9714 6550 0.0124 -
0.9788 6600 0.0139 -
0.9862 6650 0.0116 -
0.9936 6700 0.0124 -
1.0010 6750 0.0089 -
1.0085 6800 0.0099 -
1.0159 6850 0.0108 -
1.0233 6900 0.0123 -
1.0307 6950 0.0123 -
1.0381 7000 0.0138 -
1.0455 7050 0.0092 -
1.0529 7100 0.0111 -
1.0604 7150 0.0103 -
1.0678 7200 0.0102 -
1.0752 7250 0.0091 -
1.0826 7300 0.0143 -
1.0900 7350 0.0117 -
1.0974 7400 0.011 -
1.1048 7450 0.0116 -
1.1123 7500 0.0132 -
1.1197 7550 0.0125 -
1.1271 7600 0.0122 -
1.1345 7650 0.0081 -
1.1419 7700 0.012 -
1.1493 7750 0.0098 -
1.1568 7800 0.0118 -
1.1642 7850 0.0153 -
1.1716 7900 0.0125 -
1.1790 7950 0.01 -
1.1864 8000 0.0089 -
1.1938 8050 0.0103 -
1.2012 8100 0.0102 -
1.2087 8150 0.0124 -
1.2161 8200 0.0116 -
1.2235 8250 0.0072 -
1.2309 8300 0.0106 -
1.2383 8350 0.0085 -
1.2457 8400 0.009 -
1.2532 8450 0.0074 -
1.2606 8500 0.0109 -
1.2680 8550 0.0087 -
1.2754 8600 0.0112 -
1.2828 8650 0.0098 -
1.2902 8700 0.0092 -
1.2976 8750 0.0073 -
1.3051 8800 0.0113 -
1.3125 8850 0.01 -
1.3199 8900 0.0083 -
1.3273 8950 0.0119 -
1.3347 9000 0.0084 -
1.3421 9050 0.0116 -
1.3495 9100 0.0083 -
1.3570 9150 0.0111 -
1.3644 9200 0.0084 -
1.3718 9250 0.0107 -
1.3792 9300 0.0107 -
1.3866 9350 0.0124 -
1.3940 9400 0.0069 -
1.4015 9450 0.0066 -
1.4089 9500 0.0069 -
1.4163 9550 0.0088 -
1.4237 9600 0.008 -
1.4311 9650 0.0076 -
1.4385 9700 0.0106 -
1.4459 9750 0.0087 -
1.4534 9800 0.0062 -
1.4608 9850 0.0072 -
1.4682 9900 0.0093 -
1.4756 9950 0.0054 -
1.4830 10000 0.0112 -
1.4904 10050 0.0087 -
1.4978 10100 0.0069 -
1.5053 10150 0.0086 -
1.5127 10200 0.0089 -
1.5201 10250 0.0089 -
1.5275 10300 0.0081 -
1.5349 10350 0.0109 -
1.5423 10400 0.0098 -
1.5498 10450 0.0078 -
1.5572 10500 0.0086 -
1.5646 10550 0.0085 -
1.5720 10600 0.0083 -
1.5794 10650 0.0087 -
1.5868 10700 0.0089 -
1.5942 10750 0.0081 -
1.6017 10800 0.0067 -
1.6091 10850 0.0054 -
1.6165 10900 0.0096 -
1.6239 10950 0.0074 -
1.6313 11000 0.0069 -
1.6387 11050 0.0089 -
1.6462 11100 0.0103 -
1.6536 11150 0.008 -
1.6610 11200 0.0084 -
1.6684 11250 0.0081 -
1.6758 11300 0.0063 -
1.6832 11350 0.0073 -
1.6906 11400 0.0066 -
1.6981 11450 0.0088 -
1.7055 11500 0.0069 -
1.7129 11550 0.0085 -
1.7203 11600 0.0096 -
1.7277 11650 0.0063 -
1.7351 11700 0.0093 -
1.7425 11750 0.0068 -
1.7500 11800 0.0079 -
1.7574 11850 0.0073 -
1.7648 11900 0.0068 -
1.7722 11950 0.0099 -
1.7796 12000 0.0069 -
1.7870 12050 0.0061 -
1.7945 12100 0.0096 -
1.8019 12150 0.0065 -
1.8093 12200 0.0119 -
1.8167 12250 0.0067 -
1.8241 12300 0.0084 -
1.8315 12350 0.0053 -
1.8389 12400 0.0074 -
1.8464 12450 0.0067 -
1.8538 12500 0.0061 -
1.8612 12550 0.0056 -
1.8686 12600 0.0051 -
1.8760 12650 0.0043 -
1.8834 12700 0.0066 -
1.8908 12750 0.0065 -
1.8983 12800 0.0048 -
1.9057 12850 0.0047 -
1.9131 12900 0.0065 -
1.9205 12950 0.0064 -
1.9279 13000 0.0056 -
1.9353 13050 0.0088 -
1.9428 13100 0.009 -
1.9502 13150 0.0086 -
1.9576 13200 0.0097 -
1.9650 13250 0.0062 -
1.9724 13300 0.0079 -
1.9798 13350 0.0094 -
1.9872 13400 0.0056 -
1.9947 13450 0.0041 -
2.0021 13500 0.0062 -
2.0095 13550 0.0063 -
2.0169 13600 0.0056 -
2.0243 13650 0.0056 -
2.0317 13700 0.0063 -
2.0392 13750 0.0052 -
2.0466 13800 0.0058 -
2.0540 13850 0.006 -
2.0614 13900 0.0071 -
2.0688 13950 0.0095 -
2.0762 14000 0.0063 -
2.0836 14050 0.0056 -
2.0911 14100 0.0044 -
2.0985 14150 0.0051 -
2.1059 14200 0.0075 -
2.1133 14250 0.0055 -
2.1207 14300 0.0048 -
2.1281 14350 0.0052 -
2.1355 14400 0.0094 -
2.1430 14450 0.0068 -
2.1504 14500 0.004 -
2.1578 14550 0.004 -
2.1652 14600 0.0046 -
2.1726 14650 0.006 -
2.1800 14700 0.0075 -
2.1875 14750 0.0055 -
2.1949 14800 0.0038 -
2.2023 14850 0.0073 -
2.2097 14900 0.0067 -
2.2171 14950 0.0066 -
2.2245 15000 0.007 -
2.2319 15050 0.0047 -
2.2394 15100 0.0057 -
2.2468 15150 0.0041 -
2.2542 15200 0.0058 -
2.2616 15250 0.0082 -
2.2690 15300 0.0049 -
2.2764 15350 0.0053 -
2.2838 15400 0.0055 -
2.2913 15450 0.0056 -
2.2987 15500 0.004 -
2.3061 15550 0.0055 -
2.3135 15600 0.0076 -
2.3209 15650 0.0089 -
2.3283 15700 0.0058 -
2.3358 15750 0.0055 -
2.3432 15800 0.0047 -
2.3506 15850 0.0052 -
2.3580 15900 0.005 -
2.3654 15950 0.0044 -
2.3728 16000 0.0086 -
2.3802 16050 0.0046 -
2.3877 16100 0.0036 -
2.3951 16150 0.0061 -
2.4025 16200 0.0054 -
2.4099 16250 0.0062 -
2.4173 16300 0.0055 -
2.4247 16350 0.0042 -
2.4322 16400 0.0058 -
2.4396 16450 0.0064 -
2.4470 16500 0.0042 -
2.4544 16550 0.0047 -
2.4618 16600 0.0062 -
2.4692 16650 0.0057 -
2.4766 16700 0.0048 -
2.4841 16750 0.0054 -
2.4915 16800 0.0061 -
2.4989 16850 0.0059 -
2.5063 16900 0.0041 -
2.5137 16950 0.0056 -
2.5211 17000 0.0058 -
2.5285 17050 0.0037 -
2.5360 17100 0.0064 -
2.5434 17150 0.0058 -
2.5508 17200 0.006 -
2.5582 17250 0.0089 -
2.5656 17300 0.0045 -
2.5730 17350 0.0046 -
2.5805 17400 0.0047 -
2.5879 17450 0.0029 -
2.5953 17500 0.0068 -
2.6027 17550 0.0036 -
2.6101 17600 0.0037 -
2.6175 17650 0.0042 -
2.6249 17700 0.0056 -
2.6324 17750 0.0084 -
2.6398 17800 0.0045 -
2.6472 17850 0.0065 -
2.6546 17900 0.0038 -
2.6620 17950 0.0051 -
2.6694 18000 0.0057 -
2.6769 18050 0.0037 -
2.6843 18100 0.0042 -
2.6917 18150 0.0052 -
2.6991 18200 0.0053 -
2.7065 18250 0.0054 -
2.7139 18300 0.0025 -
2.7213 18350 0.0045 -
2.7288 18400 0.0039 -
2.7362 18450 0.0064 -
2.7436 18500 0.0031 -
2.7510 18550 0.0057 -
2.7584 18600 0.0052 -
2.7658 18650 0.0049 -
2.7732 18700 0.0062 -
2.7807 18750 0.0041 -
2.7881 18800 0.0062 -
2.7955 18850 0.005 -
2.8029 18900 0.0057 -
2.8103 18950 0.0051 -
2.8177 19000 0.0035 -
2.8252 19050 0.0045 -
2.8326 19100 0.0048 -
2.8400 19150 0.002 -
2.8474 19200 0.0058 -
2.8548 19250 0.0041 -
2.8622 19300 0.0044 -
2.8696 19350 0.0062 -
2.8771 19400 0.0042 -
2.8845 19450 0.0036 -
2.8919 19500 0.005 -
2.8993 19550 0.0056 -
2.9067 19600 0.0056 -
2.9141 19650 0.0039 -
2.9215 19700 0.0058 -
2.9290 19750 0.0053 -
2.9364 19800 0.0058 -
2.9438 19850 0.0044 -
2.9512 19900 0.0028 -
2.9586 19950 0.0046 -
2.9660 20000 0.0059 -
2.9735 20050 0.0049 -
2.9809 20100 0.0048 -
2.9883 20150 0.0039 -
2.9957 20200 0.0062 -
3.0031 20250 0.0034 -
3.0105 20300 0.0048 -
3.0179 20350 0.0035 -
3.0254 20400 0.0048 -
3.0328 20450 0.0063 -
3.0402 20500 0.0047 -
3.0476 20550 0.0041 -
3.0550 20600 0.0046 -
3.0624 20650 0.0041 -
3.0699 20700 0.0052 -
3.0773 20750 0.0031 -
3.0847 20800 0.0042 -
3.0921 20850 0.0045 -
3.0995 20900 0.0049 -
3.1069 20950 0.0033 -
3.1143 21000 0.0064 -
3.1218 21050 0.0039 -
3.1292 21100 0.0058 -
3.1366 21150 0.004 -
3.1440 21200 0.0031 -
3.1514 21250 0.0028 -
3.1588 21300 0.0038 -
3.1662 21350 0.0048 -
3.1737 21400 0.0047 -
3.1811 21450 0.0057 -
3.1885 21500 0.0037 -
3.1959 21550 0.0039 -
3.2033 21600 0.0045 -
3.2107 21650 0.005 -
3.2182 21700 0.0034 -
3.2256 21750 0.0048 -
3.2330 21800 0.0034 -
3.2404 21850 0.0056 -
3.2478 21900 0.0041 -
3.2552 21950 0.0048 -
3.2626 22000 0.0066 -
3.2701 22050 0.0044 -
3.2775 22100 0.0046 -
3.2849 22150 0.0046 -
3.2923 22200 0.0038 -
3.2997 22250 0.0036 -
3.3071 22300 0.0023 -
3.3145 22350 0.0049 -
3.3220 22400 0.0041 -
3.3294 22450 0.0036 -
3.3368 22500 0.004 -
3.3442 22550 0.0054 -
3.3516 22600 0.0033 -
3.3590 22650 0.0054 -
3.3665 22700 0.0056 -
3.3739 22750 0.0051 -
3.3813 22800 0.0033 -
3.3887 22850 0.0046 -
3.3961 22900 0.0052 -
3.4035 22950 0.0043 -
3.4109 23000 0.0051 -
3.4184 23050 0.0036 -
3.4258 23100 0.0051 -
3.4332 23150 0.0061 -
3.4406 23200 0.004 -
3.4480 23250 0.0036 -
3.4554 23300 0.0035 -
3.4629 23350 0.0063 -
3.4703 23400 0.0051 -
3.4777 23450 0.0024 -
3.4851 23500 0.0033 -
3.4925 23550 0.0048 -
3.4999 23600 0.0035 -
3.5073 23650 0.0041 -
3.5148 23700 0.0035 -
3.5222 23750 0.0031 -
3.5296 23800 0.0031 -
3.5370 23850 0.0042 -
3.5444 23900 0.0038 -
3.5518 23950 0.0042 -
3.5592 24000 0.0048 -
3.5667 24050 0.0018 -
3.5741 24100 0.005 -
3.5815 24150 0.0073 -
3.5889 24200 0.0056 -
3.5963 24250 0.0037 -
3.6037 24300 0.0065 -
3.6112 24350 0.005 -
3.6186 24400 0.0051 -
3.6260 24450 0.0039 -
3.6334 24500 0.0055 -
3.6408 24550 0.0035 -
3.6482 24600 0.0039 -
3.6556 24650 0.0044 -
3.6631 24700 0.0045 -
3.6705 24750 0.0029 -
3.6779 24800 0.0025 -
3.6853 24850 0.0032 -
3.6927 24900 0.0035 -
3.7001 24950 0.0045 -
3.7075 25000 0.0043 -
3.7150 25050 0.0035 -
3.7224 25100 0.0039 -
3.7298 25150 0.0036 -
3.7372 25200 0.0035 -
3.7446 25250 0.0043 -
3.7520 25300 0.0027 -
3.7595 25350 0.0034 -
3.7669 25400 0.0045 -
3.7743 25450 0.0031 -
3.7817 25500 0.0033 -
3.7891 25550 0.0045 -
3.7965 25600 0.0046 -
3.8039 25650 0.0026 -
3.8114 25700 0.0053 -
3.8188 25750 0.0033 -
3.8262 25800 0.0046 -
3.8336 25850 0.0035 -
3.8410 25900 0.0045 -
3.8484 25950 0.0036 -
3.8559 26000 0.0035 -
3.8633 26050 0.0037 -
3.8707 26100 0.0024 -
3.8781 26150 0.0049 -
3.8855 26200 0.0028 -
3.8929 26250 0.0055 -
3.9003 26300 0.0029 -
3.9078 26350 0.0052 -
3.9152 26400 0.0043 -
3.9226 26450 0.0042 -
3.9300 26500 0.0059 -
3.9374 26550 0.0038 -
3.9448 26600 0.0047 -
3.9522 26650 0.0043 -
3.9597 26700 0.0034 -
3.9671 26750 0.005 -
3.9745 26800 0.004 -
3.9819 26850 0.0053 -
3.9893 26900 0.0046 -
3.9967 26950 0.0053 -
4.0042 27000 0.0038 -
4.0116 27050 0.0038 -
4.0190 27100 0.0032 -
4.0264 27150 0.0038 -
4.0338 27200 0.0042 -
4.0412 27250 0.005 -
4.0486 27300 0.0031 -
4.0561 27350 0.0041 -
4.0635 27400 0.0033 -
4.0709 27450 0.0028 -
4.0783 27500 0.0054 -
4.0857 27550 0.0038 -
4.0931 27600 0.0037 -
4.1005 27650 0.0037 -
4.1080 27700 0.0033 -
4.1154 27750 0.0041 -
4.1228 27800 0.0038 -
4.1302 27850 0.0029 -
4.1376 27900 0.0047 -
4.1450 27950 0.0038 -
4.1525 28000 0.0041 -
4.1599 28050 0.0036 -
4.1673 28100 0.003 -
4.1747 28150 0.005 -
4.1821 28200 0.0039 -
4.1895 28250 0.005 -
4.1969 28300 0.0035 -
4.2044 28350 0.0036 -
4.2118 28400 0.0053 -
4.2192 28450 0.0041 -
4.2266 28500 0.0042 -
4.2340 28550 0.0058 -
4.2414 28600 0.0035 -
4.2489 28650 0.0036 -
4.2563 28700 0.0041 -
4.2637 28750 0.0046 -
4.2711 28800 0.0048 -
4.2785 28850 0.0035 -
4.2859 28900 0.0041 -
4.2933 28950 0.0034 -
4.3008 29000 0.0022 -
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Framework Versions

  • Python: 3.10.8
  • SetFit: 1.1.2
  • Sentence Transformers: 5.0.0
  • Transformers: 4.54.1
  • PyTorch: 2.7.1+cu126
  • Datasets: 3.6.0
  • Tokenizers: 0.21.4

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}
}