SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
0 |
|
1 |
|
2 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7727 |
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("alelov/test-model-label1-MiniLMVERSION2")
# Run inference
preds = model("Não apenas isso. A bola de neve do endividamento")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 103.4095 | 2340 |
Label | Training Sample Count |
---|---|
0 | 311 |
1 | 27 |
2 | 21 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0002 | 1 | 0.2894 | - |
0.0081 | 50 | 0.2562 | - |
0.0163 | 100 | 0.3346 | - |
0.0244 | 150 | 0.3106 | - |
0.0326 | 200 | 0.2452 | - |
0.0407 | 250 | 0.2848 | - |
0.0489 | 300 | 0.188 | - |
0.0570 | 350 | 0.1865 | - |
0.0651 | 400 | 0.1345 | - |
0.0733 | 450 | 0.1494 | - |
0.0814 | 500 | 0.1723 | - |
0.0896 | 550 | 0.0241 | - |
0.0977 | 600 | 0.0298 | - |
0.1058 | 650 | 0.01 | - |
0.1140 | 700 | 0.0354 | - |
0.1221 | 750 | 0.004 | - |
0.1303 | 800 | 0.0016 | - |
0.1384 | 850 | 0.0022 | - |
0.1466 | 900 | 0.0032 | - |
0.1547 | 950 | 0.0029 | - |
0.1628 | 1000 | 0.0009 | - |
0.1710 | 1050 | 0.0031 | - |
0.1791 | 1100 | 0.0525 | - |
0.1873 | 1150 | 0.0006 | - |
0.1954 | 1200 | 0.0007 | - |
0.2035 | 1250 | 0.0007 | - |
0.2117 | 1300 | 0.0014 | - |
0.2198 | 1350 | 0.0006 | - |
0.2280 | 1400 | 0.0071 | - |
0.2361 | 1450 | 0.0004 | - |
0.2443 | 1500 | 0.0003 | - |
0.2524 | 1550 | 0.0004 | - |
0.2605 | 1600 | 0.0019 | - |
0.2687 | 1650 | 0.0499 | - |
0.2768 | 1700 | 0.0004 | - |
0.2850 | 1750 | 0.0259 | - |
0.2931 | 1800 | 0.0002 | - |
0.3013 | 1850 | 0.0001 | - |
0.3094 | 1900 | 0.0003 | - |
0.3175 | 1950 | 0.0002 | - |
0.3257 | 2000 | 0.0003 | - |
0.3338 | 2050 | 0.0038 | - |
0.3420 | 2100 | 0.0001 | - |
0.3501 | 2150 | 0.0002 | - |
0.3582 | 2200 | 0.0002 | - |
0.3664 | 2250 | 0.0001 | - |
0.3745 | 2300 | 0.0001 | - |
0.3827 | 2350 | 0.0001 | - |
0.3908 | 2400 | 0.0044 | - |
0.3990 | 2450 | 0.0436 | - |
0.4071 | 2500 | 0.0002 | - |
0.4152 | 2550 | 0.0007 | - |
0.4234 | 2600 | 0.0001 | - |
0.4315 | 2650 | 0.0001 | - |
0.4397 | 2700 | 0.0001 | - |
0.4478 | 2750 | 0.0023 | - |
0.4560 | 2800 | 0.0001 | - |
0.4641 | 2850 | 0.0009 | - |
0.4722 | 2900 | 0.0001 | - |
0.4804 | 2950 | 0.0001 | - |
0.4885 | 3000 | 0.003 | - |
0.4967 | 3050 | 0.0001 | - |
0.5048 | 3100 | 0.0004 | - |
0.5129 | 3150 | 0.0 | - |
0.5211 | 3200 | 0.0001 | - |
0.5292 | 3250 | 0.0001 | - |
0.5374 | 3300 | 0.0 | - |
0.5455 | 3350 | 0.0 | - |
0.5537 | 3400 | 0.0001 | - |
0.5618 | 3450 | 0.0 | - |
0.5699 | 3500 | 0.0001 | - |
0.5781 | 3550 | 0.0 | - |
0.5862 | 3600 | 0.0 | - |
0.5944 | 3650 | 0.0 | - |
0.6025 | 3700 | 0.0 | - |
0.6106 | 3750 | 0.0 | - |
0.6188 | 3800 | 0.0001 | - |
0.6269 | 3850 | 0.0 | - |
0.6351 | 3900 | 0.0 | - |
0.6432 | 3950 | 0.0004 | - |
0.6514 | 4000 | 0.0004 | - |
0.6595 | 4050 | 0.0 | - |
0.6676 | 4100 | 0.0 | - |
0.6758 | 4150 | 0.0 | - |
0.6839 | 4200 | 0.0011 | - |
0.6921 | 4250 | 0.0006 | - |
0.7002 | 4300 | 0.0001 | - |
0.7084 | 4350 | 0.0 | - |
0.7165 | 4400 | 0.0 | - |
0.7246 | 4450 | 0.0 | - |
0.7328 | 4500 | 0.0 | - |
0.7409 | 4550 | 0.0 | - |
0.7491 | 4600 | 0.0 | - |
0.7572 | 4650 | 0.0 | - |
0.7653 | 4700 | 0.0 | - |
0.7735 | 4750 | 0.0041 | - |
0.7816 | 4800 | 0.0004 | - |
0.7898 | 4850 | 0.0006 | - |
0.7979 | 4900 | 0.0 | - |
0.8061 | 4950 | 0.0 | - |
0.8142 | 5000 | 0.0 | - |
0.8223 | 5050 | 0.0 | - |
0.8305 | 5100 | 0.0 | - |
0.8386 | 5150 | 0.0 | - |
0.8468 | 5200 | 0.0 | - |
0.8549 | 5250 | 0.0 | - |
0.8631 | 5300 | 0.0 | - |
0.8712 | 5350 | 0.0 | - |
0.8793 | 5400 | 0.0 | - |
0.8875 | 5450 | 0.0 | - |
0.8956 | 5500 | 0.0 | - |
0.9038 | 5550 | 0.0 | - |
0.9119 | 5600 | 0.0 | - |
0.9200 | 5650 | 0.0 | - |
0.9282 | 5700 | 0.0 | - |
0.9363 | 5750 | 0.0 | - |
0.9445 | 5800 | 0.0 | - |
0.9526 | 5850 | 0.0 | - |
0.9608 | 5900 | 0.0 | - |
0.9689 | 5950 | 0.0 | - |
0.9770 | 6000 | 0.0 | - |
0.9852 | 6050 | 0.0595 | - |
0.9933 | 6100 | 0.0001 | - |
1.0 | 6141 | - | 0.2767 |
1.0015 | 6150 | 0.0 | - |
1.0096 | 6200 | 0.0 | - |
1.0177 | 6250 | 0.0 | - |
1.0259 | 6300 | 0.0 | - |
1.0340 | 6350 | 0.0 | - |
1.0422 | 6400 | 0.0 | - |
1.0503 | 6450 | 0.0 | - |
1.0585 | 6500 | 0.0 | - |
1.0666 | 6550 | 0.0 | - |
1.0747 | 6600 | 0.0 | - |
1.0829 | 6650 | 0.0 | - |
1.0910 | 6700 | 0.0 | - |
1.0992 | 6750 | 0.0 | - |
1.1073 | 6800 | 0.0 | - |
1.1155 | 6850 | 0.0 | - |
1.1236 | 6900 | 0.0 | - |
1.1317 | 6950 | 0.0 | - |
1.1399 | 7000 | 0.0 | - |
1.1480 | 7050 | 0.0 | - |
1.1562 | 7100 | 0.0 | - |
1.1643 | 7150 | 0.0 | - |
1.1724 | 7200 | 0.0 | - |
1.1806 | 7250 | 0.0 | - |
1.1887 | 7300 | 0.0 | - |
1.1969 | 7350 | 0.0 | - |
1.2050 | 7400 | 0.0 | - |
1.2132 | 7450 | 0.0 | - |
1.2213 | 7500 | 0.0001 | - |
1.2294 | 7550 | 0.0 | - |
1.2376 | 7600 | 0.0 | - |
1.2457 | 7650 | 0.0 | - |
1.2539 | 7700 | 0.0 | - |
1.2620 | 7750 | 0.0 | - |
1.2702 | 7800 | 0.0001 | - |
1.2783 | 7850 | 0.0 | - |
1.2864 | 7900 | 0.0 | - |
1.2946 | 7950 | 0.0002 | - |
1.3027 | 8000 | 0.0 | - |
1.3109 | 8050 | 0.0003 | - |
1.3190 | 8100 | 0.0588 | - |
1.3271 | 8150 | 0.0 | - |
1.3353 | 8200 | 0.0002 | - |
1.3434 | 8250 | 0.0 | - |
1.3516 | 8300 | 0.0 | - |
1.3597 | 8350 | 0.0 | - |
1.3679 | 8400 | 0.0261 | - |
1.3760 | 8450 | 0.0 | - |
1.3841 | 8500 | 0.0 | - |
1.3923 | 8550 | 0.0 | - |
1.4004 | 8600 | 0.0 | - |
1.4086 | 8650 | 0.0 | - |
1.4167 | 8700 | 0.0 | - |
1.4248 | 8750 | 0.0 | - |
1.4330 | 8800 | 0.0 | - |
1.4411 | 8850 | 0.0 | - |
1.4493 | 8900 | 0.0 | - |
1.4574 | 8950 | 0.0 | - |
1.4656 | 9000 | 0.0 | - |
1.4737 | 9050 | 0.0 | - |
1.4818 | 9100 | 0.0 | - |
1.4900 | 9150 | 0.0153 | - |
1.4981 | 9200 | 0.0 | - |
1.5063 | 9250 | 0.0 | - |
1.5144 | 9300 | 0.0 | - |
1.5226 | 9350 | 0.0 | - |
1.5307 | 9400 | 0.0 | - |
1.5388 | 9450 | 0.0003 | - |
1.5470 | 9500 | 0.0 | - |
1.5551 | 9550 | 0.0003 | - |
1.5633 | 9600 | 0.0 | - |
1.5714 | 9650 | 0.0 | - |
1.5795 | 9700 | 0.0 | - |
1.5877 | 9750 | 0.0 | - |
1.5958 | 9800 | 0.0 | - |
1.6040 | 9850 | 0.0 | - |
1.6121 | 9900 | 0.0 | - |
1.6203 | 9950 | 0.0 | - |
1.6284 | 10000 | 0.0 | - |
1.6365 | 10050 | 0.0 | - |
1.6447 | 10100 | 0.0 | - |
1.6528 | 10150 | 0.0 | - |
1.6610 | 10200 | 0.0 | - |
1.6691 | 10250 | 0.0 | - |
1.6773 | 10300 | 0.0 | - |
1.6854 | 10350 | 0.0 | - |
1.6935 | 10400 | 0.0 | - |
1.7017 | 10450 | 0.0 | - |
1.7098 | 10500 | 0.0 | - |
1.7180 | 10550 | 0.0 | - |
1.7261 | 10600 | 0.0 | - |
1.7342 | 10650 | 0.0 | - |
1.7424 | 10700 | 0.0 | - |
1.7505 | 10750 | 0.0 | - |
1.7587 | 10800 | 0.0 | - |
1.7668 | 10850 | 0.0 | - |
1.7750 | 10900 | 0.0 | - |
1.7831 | 10950 | 0.0 | - |
1.7912 | 11000 | 0.0 | - |
1.7994 | 11050 | 0.0 | - |
1.8075 | 11100 | 0.0 | - |
1.8157 | 11150 | 0.0 | - |
1.8238 | 11200 | 0.0 | - |
1.8319 | 11250 | 0.0 | - |
1.8401 | 11300 | 0.0 | - |
1.8482 | 11350 | 0.0 | - |
1.8564 | 11400 | 0.0 | - |
1.8645 | 11450 | 0.0 | - |
1.8727 | 11500 | 0.0 | - |
1.8808 | 11550 | 0.0 | - |
1.8889 | 11600 | 0.0 | - |
1.8971 | 11650 | 0.0 | - |
1.9052 | 11700 | 0.0 | - |
1.9134 | 11750 | 0.0 | - |
1.9215 | 11800 | 0.0 | - |
1.9297 | 11850 | 0.0 | - |
1.9378 | 11900 | 0.0006 | - |
1.9459 | 11950 | 0.0 | - |
1.9541 | 12000 | 0.0 | - |
1.9622 | 12050 | 0.0 | - |
1.9704 | 12100 | 0.0 | - |
1.9785 | 12150 | 0.0 | - |
1.9866 | 12200 | 0.0 | - |
1.9948 | 12250 | 0.0 | - |
2.0 | 12282 | - | 0.2742 |
2.0029 | 12300 | 0.0 | - |
2.0111 | 12350 | 0.0 | - |
2.0192 | 12400 | 0.0 | - |
2.0274 | 12450 | 0.0 | - |
2.0355 | 12500 | 0.0 | - |
2.0436 | 12550 | 0.0 | - |
2.0518 | 12600 | 0.0 | - |
2.0599 | 12650 | 0.0 | - |
2.0681 | 12700 | 0.0 | - |
2.0762 | 12750 | 0.0 | - |
2.0844 | 12800 | 0.0 | - |
2.0925 | 12850 | 0.0 | - |
2.1006 | 12900 | 0.0 | - |
2.1088 | 12950 | 0.0 | - |
2.1169 | 13000 | 0.0 | - |
2.1251 | 13050 | 0.0 | - |
2.1332 | 13100 | 0.0 | - |
2.1413 | 13150 | 0.0 | - |
2.1495 | 13200 | 0.0 | - |
2.1576 | 13250 | 0.0 | - |
2.1658 | 13300 | 0.0 | - |
2.1739 | 13350 | 0.0 | - |
2.1821 | 13400 | 0.0 | - |
2.1902 | 13450 | 0.0 | - |
2.1983 | 13500 | 0.0 | - |
2.2065 | 13550 | 0.0 | - |
2.2146 | 13600 | 0.0 | - |
2.2228 | 13650 | 0.0 | - |
2.2309 | 13700 | 0.0 | - |
2.2390 | 13750 | 0.0 | - |
2.2472 | 13800 | 0.0 | - |
2.2553 | 13850 | 0.0 | - |
2.2635 | 13900 | 0.0 | - |
2.2716 | 13950 | 0.0 | - |
2.2798 | 14000 | 0.0 | - |
2.2879 | 14050 | 0.0013 | - |
2.2960 | 14100 | 0.0 | - |
2.3042 | 14150 | 0.0 | - |
2.3123 | 14200 | 0.0 | - |
2.3205 | 14250 | 0.0 | - |
2.3286 | 14300 | 0.0 | - |
2.3368 | 14350 | 0.0 | - |
2.3449 | 14400 | 0.0 | - |
2.3530 | 14450 | 0.0019 | - |
2.3612 | 14500 | 0.0 | - |
2.3693 | 14550 | 0.0 | - |
2.3775 | 14600 | 0.0 | - |
2.3856 | 14650 | 0.0 | - |
2.3937 | 14700 | 0.0 | - |
2.4019 | 14750 | 0.0 | - |
2.4100 | 14800 | 0.0 | - |
2.4182 | 14850 | 0.0 | - |
2.4263 | 14900 | 0.0 | - |
2.4345 | 14950 | 0.0 | - |
2.4426 | 15000 | 0.0 | - |
2.4507 | 15050 | 0.0 | - |
2.4589 | 15100 | 0.0 | - |
2.4670 | 15150 | 0.0 | - |
2.4752 | 15200 | 0.0 | - |
2.4833 | 15250 | 0.0 | - |
2.4915 | 15300 | 0.0 | - |
2.4996 | 15350 | 0.0 | - |
2.5077 | 15400 | 0.0 | - |
2.5159 | 15450 | 0.0 | - |
2.5240 | 15500 | 0.0 | - |
2.5322 | 15550 | 0.0 | - |
2.5403 | 15600 | 0.0 | - |
2.5484 | 15650 | 0.0 | - |
2.5566 | 15700 | 0.0 | - |
2.5647 | 15750 | 0.0 | - |
2.5729 | 15800 | 0.0 | - |
2.5810 | 15850 | 0.0 | - |
2.5892 | 15900 | 0.0001 | - |
2.5973 | 15950 | 0.0 | - |
2.6054 | 16000 | 0.0 | - |
2.6136 | 16050 | 0.0 | - |
2.6217 | 16100 | 0.0 | - |
2.6299 | 16150 | 0.0 | - |
2.6380 | 16200 | 0.0 | - |
2.6461 | 16250 | 0.0 | - |
2.6543 | 16300 | 0.0 | - |
2.6624 | 16350 | 0.0 | - |
2.6706 | 16400 | 0.0 | - |
2.6787 | 16450 | 0.0 | - |
2.6869 | 16500 | 0.0 | - |
2.6950 | 16550 | 0.0 | - |
2.7031 | 16600 | 0.0 | - |
2.7113 | 16650 | 0.0002 | - |
2.7194 | 16700 | 0.0 | - |
2.7276 | 16750 | 0.0 | - |
2.7357 | 16800 | 0.0 | - |
2.7439 | 16850 | 0.0 | - |
2.7520 | 16900 | 0.0 | - |
2.7601 | 16950 | 0.0 | - |
2.7683 | 17000 | 0.0291 | - |
2.7764 | 17050 | 0.0 | - |
2.7846 | 17100 | 0.0 | - |
2.7927 | 17150 | 0.0 | - |
2.8008 | 17200 | 0.0 | - |
2.8090 | 17250 | 0.0 | - |
2.8171 | 17300 | 0.0 | - |
2.8253 | 17350 | 0.0 | - |
2.8334 | 17400 | 0.0 | - |
2.8416 | 17450 | 0.0 | - |
2.8497 | 17500 | 0.0 | - |
2.8578 | 17550 | 0.0 | - |
2.8660 | 17600 | 0.0 | - |
2.8741 | 17650 | 0.0 | - |
2.8823 | 17700 | 0.0 | - |
2.8904 | 17750 | 0.0 | - |
2.8986 | 17800 | 0.0 | - |
2.9067 | 17850 | 0.0 | - |
2.9148 | 17900 | 0.0 | - |
2.9230 | 17950 | 0.0 | - |
2.9311 | 18000 | 0.0 | - |
2.9393 | 18050 | 0.0 | - |
2.9474 | 18100 | 0.0 | - |
2.9555 | 18150 | 0.0 | - |
2.9637 | 18200 | 0.0 | - |
2.9718 | 18250 | 0.0 | - |
2.9800 | 18300 | 0.0 | - |
2.9881 | 18350 | 0.0 | - |
2.9963 | 18400 | 0.0 | - |
3.0 | 18423 | - | 0.2642 |
3.0044 | 18450 | 0.0012 | - |
3.0125 | 18500 | 0.0 | - |
3.0207 | 18550 | 0.0 | - |
3.0288 | 18600 | 0.0 | - |
3.0370 | 18650 | 0.0 | - |
3.0451 | 18700 | 0.0041 | - |
3.0532 | 18750 | 0.0 | - |
3.0614 | 18800 | 0.0 | - |
3.0695 | 18850 | 0.0 | - |
3.0777 | 18900 | 0.0 | - |
3.0858 | 18950 | 0.0 | - |
3.0940 | 19000 | 0.0 | - |
3.1021 | 19050 | 0.0 | - |
3.1102 | 19100 | 0.0 | - |
3.1184 | 19150 | 0.0 | - |
3.1265 | 19200 | 0.0 | - |
3.1347 | 19250 | 0.0 | - |
3.1428 | 19300 | 0.0 | - |
3.1510 | 19350 | 0.0 | - |
3.1591 | 19400 | 0.0 | - |
3.1672 | 19450 | 0.0 | - |
3.1754 | 19500 | 0.0014 | - |
3.1835 | 19550 | 0.0 | - |
3.1917 | 19600 | 0.0 | - |
3.1998 | 19650 | 0.0 | - |
3.2079 | 19700 | 0.0 | - |
3.2161 | 19750 | 0.0 | - |
3.2242 | 19800 | 0.0 | - |
3.2324 | 19850 | 0.0 | - |
3.2405 | 19900 | 0.0 | - |
3.2487 | 19950 | 0.0 | - |
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3.2649 | 20050 | 0.0 | - |
3.2731 | 20100 | 0.0 | - |
3.2812 | 20150 | 0.0 | - |
3.2894 | 20200 | 0.0453 | - |
3.2975 | 20250 | 0.0 | - |
3.3057 | 20300 | 0.0 | - |
3.3138 | 20350 | 0.0 | - |
3.3219 | 20400 | 0.0 | - |
3.3301 | 20450 | 0.0 | - |
3.3382 | 20500 | 0.0 | - |
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3.3545 | 20600 | 0.0 | - |
3.3626 | 20650 | 0.0 | - |
3.3708 | 20700 | 0.0 | - |
3.3789 | 20750 | 0.0 | - |
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3.3952 | 20850 | 0.0 | - |
3.4034 | 20900 | 0.0 | - |
3.4115 | 20950 | 0.0 | - |
3.4196 | 21000 | 0.0 | - |
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3.4359 | 21100 | 0.0 | - |
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3.5011 | 21500 | 0.0 | - |
3.5092 | 21550 | 0.0 | - |
3.5173 | 21600 | 0.0 | - |
3.5255 | 21650 | 0.0 | - |
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3.5825 | 22000 | 0.0 | - |
3.5906 | 22050 | 0.0 | - |
3.5988 | 22100 | 0.0 | - |
3.6069 | 22150 | 0.0 | - |
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3.6232 | 22250 | 0.0 | - |
3.6313 | 22300 | 0.0 | - |
3.6395 | 22350 | 0.0 | - |
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3.6720 | 22550 | 0.0 | - |
3.6802 | 22600 | 0.0 | - |
3.6883 | 22650 | 0.0 | - |
3.6965 | 22700 | 0.0 | - |
3.7046 | 22750 | 0.0 | - |
3.7128 | 22800 | 0.0 | - |
3.7209 | 22850 | 0.0 | - |
3.7290 | 22900 | 0.0 | - |
3.7372 | 22950 | 0.0 | - |
3.7453 | 23000 | 0.0 | - |
3.7535 | 23050 | 0.0 | - |
3.7616 | 23100 | 0.0 | - |
3.7697 | 23150 | 0.0 | - |
3.7779 | 23200 | 0.0 | - |
3.7860 | 23250 | 0.0 | - |
3.7942 | 23300 | 0.0 | - |
3.8023 | 23350 | 0.0 | - |
3.8105 | 23400 | 0.0 | - |
3.8186 | 23450 | 0.0 | - |
3.8267 | 23500 | 0.0 | - |
3.8349 | 23550 | 0.0 | - |
3.8430 | 23600 | 0.0 | - |
3.8512 | 23650 | 0.0 | - |
3.8593 | 23700 | 0.0 | - |
3.8674 | 23750 | 0.0 | - |
3.8756 | 23800 | 0.0 | - |
3.8837 | 23850 | 0.0 | - |
3.8919 | 23900 | 0.0 | - |
3.9000 | 23950 | 0.0 | - |
3.9082 | 24000 | 0.0 | - |
3.9163 | 24050 | 0.0 | - |
3.9244 | 24100 | 0.0 | - |
3.9326 | 24150 | 0.0 | - |
3.9407 | 24200 | 0.0 | - |
3.9489 | 24250 | 0.0 | - |
3.9570 | 24300 | 0.0 | - |
3.9652 | 24350 | 0.0 | - |
3.9733 | 24400 | 0.0 | - |
3.9814 | 24450 | 0.0 | - |
3.9896 | 24500 | 0.0 | - |
3.9977 | 24550 | 0.0 | - |
4.0 | 24564 | - | 0.2671 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.1
- 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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