SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L3-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-MiniLM-L3-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 53 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 |
---|---|
Integer |
|
Country Name |
|
License Plate |
|
Date |
|
Latitude |
|
Month Number |
|
Floating Point Number |
|
Time |
|
Place |
|
Full Name |
|
U.S. State Abbreviation |
|
Price |
|
U.S. State |
|
Gender |
|
Longitude |
|
URL |
|
Day of Week |
|
Slug |
|
Timestamp |
|
Coordinate |
|
Likert scale |
|
Categorical |
|
Secondary Address |
|
Year |
|
Zip Code |
|
Region |
|
AM/PM |
|
Race/Ethnicity |
|
Street Name |
|
Day of Month |
|
Boolean |
|
Color |
|
Location |
|
Last Name |
|
Company Name |
|
Street Address |
|
Short text |
|
Occupation |
|
Very short text |
|
Numeric |
|
URI |
|
Letter grade |
|
Month Name |
|
Age |
|
Partial timestamp |
|
Abbreviation |
|
Country ISO Code |
|
City Name |
|
Continents |
|
Postal Code |
|
Marital status |
|
First Name |
|
Currency Code |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7630 |
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("quantisan/paraphrase-MiniLM-L3-v2-93dataset-v2labels")
# Run inference
preds = model("variety: Western, Eastern")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 2 | 22.1604 | 378 |
Label | Training Sample Count |
---|---|
Categorical | 8 |
Numeric | 8 |
Timestamp | 5 |
Date | 8 |
Integer | 8 |
Partial timestamp | 3 |
Short text | 8 |
Very short text | 3 |
AM/PM | 1 |
Boolean | 8 |
City Name | 4 |
Color | 3 |
Company Name | 1 |
Coordinate | 1 |
Country ISO Code | 3 |
Country Name | 8 |
Currency Code | 1 |
Day of Month | 3 |
Day of Week | 2 |
First Name | 1 |
Floating Point Number | 8 |
Full Name | 8 |
Last Name | 1 |
Latitude | 4 |
License Plate | 1 |
Longitude | 4 |
Month Name | 4 |
Month Number | 4 |
Occupation | 3 |
Postal Code | 1 |
Price | 1 |
Secondary Address | 1 |
Slug | 8 |
Street Address | 1 |
Street Name | 2 |
Time | 1 |
U.S. State | 8 |
U.S. State Abbreviation | 6 |
URI | 1 |
URL | 8 |
Year | 8 |
Zip Code | 3 |
Likert scale | 8 |
Gender | 8 |
Letter grade | 4 |
Race/Ethnicity | 3 |
Marital status | 2 |
Continents | 1 |
Region | 5 |
Age | 3 |
Place | 1 |
Abbreviation | 1 |
Location | 3 |
Training Hyperparameters
- batch_size: (8, 8)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0002 | 1 | 0.1497 | - |
0.0092 | 50 | 0.1834 | - |
0.0183 | 100 | 0.1917 | - |
0.0275 | 150 | 0.1712 | - |
0.0366 | 200 | 0.1505 | - |
0.0458 | 250 | 0.146 | - |
0.0549 | 300 | 0.1465 | - |
0.0641 | 350 | 0.1297 | - |
0.0732 | 400 | 0.1238 | - |
0.0824 | 450 | 0.111 | - |
0.0916 | 500 | 0.1035 | - |
0.1007 | 550 | 0.1008 | - |
0.1099 | 600 | 0.0914 | - |
0.1190 | 650 | 0.0869 | - |
0.1282 | 700 | 0.0792 | - |
0.1373 | 750 | 0.0712 | - |
0.1465 | 800 | 0.0709 | - |
0.1556 | 850 | 0.0808 | - |
0.1648 | 900 | 0.0659 | - |
0.1740 | 950 | 0.0611 | - |
0.1831 | 1000 | 0.0611 | - |
0.1923 | 1050 | 0.0607 | - |
0.2014 | 1100 | 0.0611 | - |
0.2106 | 1150 | 0.0507 | - |
0.2197 | 1200 | 0.0577 | - |
0.2289 | 1250 | 0.0508 | - |
0.2381 | 1300 | 0.0399 | - |
0.2472 | 1350 | 0.0442 | - |
0.2564 | 1400 | 0.0516 | - |
0.2655 | 1450 | 0.0441 | - |
0.2747 | 1500 | 0.0472 | - |
0.2838 | 1550 | 0.0284 | - |
0.2930 | 1600 | 0.0492 | - |
0.3021 | 1650 | 0.035 | - |
0.3113 | 1700 | 0.0338 | - |
0.3205 | 1750 | 0.0286 | - |
0.3296 | 1800 | 0.0296 | - |
0.3388 | 1850 | 0.0328 | - |
0.3479 | 1900 | 0.0277 | - |
0.3571 | 1950 | 0.0269 | - |
0.3662 | 2000 | 0.0262 | - |
0.3754 | 2050 | 0.0311 | - |
0.3845 | 2100 | 0.0277 | - |
0.3937 | 2150 | 0.022 | - |
0.4029 | 2200 | 0.0216 | - |
0.4120 | 2250 | 0.0213 | - |
0.4212 | 2300 | 0.0231 | - |
0.4303 | 2350 | 0.0255 | - |
0.4395 | 2400 | 0.02 | - |
0.4486 | 2450 | 0.0181 | - |
0.4578 | 2500 | 0.0196 | - |
0.4669 | 2550 | 0.0182 | - |
0.4761 | 2600 | 0.0199 | - |
0.4853 | 2650 | 0.0171 | - |
0.4944 | 2700 | 0.0171 | - |
0.5036 | 2750 | 0.0169 | - |
0.5127 | 2800 | 0.0161 | - |
0.5219 | 2850 | 0.0104 | - |
0.5310 | 2900 | 0.0133 | - |
0.5402 | 2950 | 0.0137 | - |
0.5493 | 3000 | 0.0241 | - |
0.5585 | 3050 | 0.0156 | - |
0.5677 | 3100 | 0.0155 | - |
0.5768 | 3150 | 0.0158 | - |
0.5860 | 3200 | 0.0165 | - |
0.5951 | 3250 | 0.0141 | - |
0.6043 | 3300 | 0.0129 | - |
0.6134 | 3350 | 0.0129 | - |
0.6226 | 3400 | 0.0103 | - |
0.6318 | 3450 | 0.011 | - |
0.6409 | 3500 | 0.0117 | - |
0.6501 | 3550 | 0.0128 | - |
0.6592 | 3600 | 0.0125 | - |
0.6684 | 3650 | 0.0138 | - |
0.6775 | 3700 | 0.0101 | - |
0.6867 | 3750 | 0.0123 | - |
0.6958 | 3800 | 0.0127 | - |
0.7050 | 3850 | 0.0088 | - |
0.7142 | 3900 | 0.0097 | - |
0.7233 | 3950 | 0.0078 | - |
0.7325 | 4000 | 0.0056 | - |
0.7416 | 4050 | 0.0096 | - |
0.7508 | 4100 | 0.0114 | - |
0.7599 | 4150 | 0.0105 | - |
0.7691 | 4200 | 0.0101 | - |
0.7782 | 4250 | 0.0077 | - |
0.7874 | 4300 | 0.0104 | - |
0.7966 | 4350 | 0.007 | - |
0.8057 | 4400 | 0.0112 | - |
0.8149 | 4450 | 0.008 | - |
0.8240 | 4500 | 0.0063 | - |
0.8332 | 4550 | 0.0153 | - |
0.8423 | 4600 | 0.0081 | - |
0.8515 | 4650 | 0.007 | - |
0.8606 | 4700 | 0.0052 | - |
0.8698 | 4750 | 0.0054 | - |
0.8790 | 4800 | 0.0063 | - |
0.8881 | 4850 | 0.0131 | - |
0.8973 | 4900 | 0.0086 | - |
0.9064 | 4950 | 0.0086 | - |
0.9156 | 5000 | 0.008 | - |
0.9247 | 5050 | 0.0097 | - |
0.9339 | 5100 | 0.0081 | - |
0.9431 | 5150 | 0.0052 | - |
0.9522 | 5200 | 0.008 | - |
0.9614 | 5250 | 0.0055 | - |
0.9705 | 5300 | 0.0048 | - |
0.9797 | 5350 | 0.0055 | - |
0.9888 | 5400 | 0.0064 | - |
0.9980 | 5450 | 0.0043 | - |
1.0 | 5461 | - | 0.0926 |
1.0071 | 5500 | 0.0064 | - |
1.0163 | 5550 | 0.0079 | - |
1.0255 | 5600 | 0.0037 | - |
1.0346 | 5650 | 0.0045 | - |
1.0438 | 5700 | 0.0072 | - |
1.0529 | 5750 | 0.0055 | - |
1.0621 | 5800 | 0.0046 | - |
1.0712 | 5850 | 0.0039 | - |
1.0804 | 5900 | 0.0063 | - |
1.0895 | 5950 | 0.0071 | - |
1.0987 | 6000 | 0.005 | - |
1.1079 | 6050 | 0.0066 | - |
1.1170 | 6100 | 0.0041 | - |
1.1262 | 6150 | 0.0056 | - |
1.1353 | 6200 | 0.0063 | - |
1.1445 | 6250 | 0.0057 | - |
1.1536 | 6300 | 0.004 | - |
1.1628 | 6350 | 0.0058 | - |
1.1719 | 6400 | 0.0067 | - |
1.1811 | 6450 | 0.0058 | - |
1.1903 | 6500 | 0.0081 | - |
1.1994 | 6550 | 0.0062 | - |
1.2086 | 6600 | 0.0062 | - |
1.2177 | 6650 | 0.0034 | - |
1.2269 | 6700 | 0.0031 | - |
1.2360 | 6750 | 0.0048 | - |
1.2452 | 6800 | 0.006 | - |
1.2543 | 6850 | 0.0054 | - |
1.2635 | 6900 | 0.007 | - |
1.2727 | 6950 | 0.0064 | - |
1.2818 | 7000 | 0.0055 | - |
1.2910 | 7050 | 0.0049 | - |
1.3001 | 7100 | 0.0063 | - |
1.3093 | 7150 | 0.0044 | - |
1.3184 | 7200 | 0.0063 | - |
1.3276 | 7250 | 0.003 | - |
1.3368 | 7300 | 0.0049 | - |
1.3459 | 7350 | 0.0047 | - |
1.3551 | 7400 | 0.0043 | - |
1.3642 | 7450 | 0.0023 | - |
1.3734 | 7500 | 0.0025 | - |
1.3825 | 7550 | 0.0047 | - |
1.3917 | 7600 | 0.0027 | - |
1.4008 | 7650 | 0.0036 | - |
1.4100 | 7700 | 0.0026 | - |
1.4192 | 7750 | 0.0019 | - |
1.4283 | 7800 | 0.0048 | - |
1.4375 | 7850 | 0.0047 | - |
1.4466 | 7900 | 0.0041 | - |
1.4558 | 7950 | 0.0073 | - |
1.4649 | 8000 | 0.0023 | - |
1.4741 | 8050 | 0.0054 | - |
1.4832 | 8100 | 0.0042 | - |
1.4924 | 8150 | 0.0078 | - |
1.5016 | 8200 | 0.0063 | - |
1.5107 | 8250 | 0.0033 | - |
1.5199 | 8300 | 0.0055 | - |
1.5290 | 8350 | 0.0043 | - |
1.5382 | 8400 | 0.0027 | - |
1.5473 | 8450 | 0.0021 | - |
1.5565 | 8500 | 0.0022 | - |
1.5656 | 8550 | 0.0063 | - |
1.5748 | 8600 | 0.0049 | - |
1.5840 | 8650 | 0.0049 | - |
1.5931 | 8700 | 0.0057 | - |
1.6023 | 8750 | 0.0035 | - |
1.6114 | 8800 | 0.0022 | - |
1.6206 | 8850 | 0.0029 | - |
1.6297 | 8900 | 0.0062 | - |
1.6389 | 8950 | 0.0022 | - |
1.6480 | 9000 | 0.0047 | - |
1.6572 | 9050 | 0.0024 | - |
1.6664 | 9100 | 0.0053 | - |
1.6755 | 9150 | 0.0021 | - |
1.6847 | 9200 | 0.0029 | - |
1.6938 | 9250 | 0.0031 | - |
1.7030 | 9300 | 0.0024 | - |
1.7121 | 9350 | 0.0034 | - |
1.7213 | 9400 | 0.0021 | - |
1.7305 | 9450 | 0.0025 | - |
1.7396 | 9500 | 0.0023 | - |
1.7488 | 9550 | 0.0029 | - |
1.7579 | 9600 | 0.0025 | - |
1.7671 | 9650 | 0.0021 | - |
1.7762 | 9700 | 0.0019 | - |
1.7854 | 9750 | 0.0034 | - |
1.7945 | 9800 | 0.0016 | - |
1.8037 | 9850 | 0.0019 | - |
1.8129 | 9900 | 0.0024 | - |
1.8220 | 9950 | 0.002 | - |
1.8312 | 10000 | 0.0021 | - |
1.8403 | 10050 | 0.0061 | - |
1.8495 | 10100 | 0.0019 | - |
1.8586 | 10150 | 0.0014 | - |
1.8678 | 10200 | 0.0021 | - |
1.8769 | 10250 | 0.0031 | - |
1.8861 | 10300 | 0.002 | - |
1.8953 | 10350 | 0.0014 | - |
1.9044 | 10400 | 0.0015 | - |
1.9136 | 10450 | 0.0014 | - |
1.9227 | 10500 | 0.0018 | - |
1.9319 | 10550 | 0.0014 | - |
1.9410 | 10600 | 0.0015 | - |
1.9502 | 10650 | 0.0014 | - |
1.9593 | 10700 | 0.0013 | - |
1.9685 | 10750 | 0.0032 | - |
1.9777 | 10800 | 0.0017 | - |
1.9868 | 10850 | 0.0015 | - |
1.9960 | 10900 | 0.0012 | - |
2.0 | 10922 | - | 0.1071 |
2.0051 | 10950 | 0.0013 | - |
2.0143 | 11000 | 0.0013 | - |
2.0234 | 11050 | 0.0015 | - |
2.0326 | 11100 | 0.0013 | - |
2.0418 | 11150 | 0.0013 | - |
2.0509 | 11200 | 0.0011 | - |
2.0601 | 11250 | 0.0013 | - |
2.0692 | 11300 | 0.0013 | - |
2.0784 | 11350 | 0.0034 | - |
2.0875 | 11400 | 0.0012 | - |
2.0967 | 11450 | 0.0012 | - |
2.1058 | 11500 | 0.0025 | - |
2.1150 | 11550 | 0.0026 | - |
2.1242 | 11600 | 0.0031 | - |
2.1333 | 11650 | 0.0012 | - |
2.1425 | 11700 | 0.0011 | - |
2.1516 | 11750 | 0.0013 | - |
2.1608 | 11800 | 0.0012 | - |
2.1699 | 11850 | 0.0013 | - |
2.1791 | 11900 | 0.0011 | - |
2.1882 | 11950 | 0.0011 | - |
2.1974 | 12000 | 0.0012 | - |
2.2066 | 12050 | 0.0014 | - |
2.2157 | 12100 | 0.003 | - |
2.2249 | 12150 | 0.001 | - |
2.2340 | 12200 | 0.0011 | - |
2.2432 | 12250 | 0.0028 | - |
2.2523 | 12300 | 0.0027 | - |
2.2615 | 12350 | 0.0013 | - |
2.2706 | 12400 | 0.0024 | - |
2.2798 | 12450 | 0.0011 | - |
2.2890 | 12500 | 0.001 | - |
2.2981 | 12550 | 0.0011 | - |
2.3073 | 12600 | 0.0011 | - |
2.3164 | 12650 | 0.0029 | - |
2.3256 | 12700 | 0.0029 | - |
2.3347 | 12750 | 0.0009 | - |
2.3439 | 12800 | 0.0013 | - |
2.3530 | 12850 | 0.0009 | - |
2.3622 | 12900 | 0.001 | - |
2.3714 | 12950 | 0.0011 | - |
2.3805 | 13000 | 0.0027 | - |
2.3897 | 13050 | 0.0009 | - |
2.3988 | 13100 | 0.0011 | - |
2.4080 | 13150 | 0.0012 | - |
2.4171 | 13200 | 0.0024 | - |
2.4263 | 13250 | 0.0039 | - |
2.4355 | 13300 | 0.001 | - |
2.4446 | 13350 | 0.0017 | - |
2.4538 | 13400 | 0.0012 | - |
2.4629 | 13450 | 0.0021 | - |
2.4721 | 13500 | 0.0021 | - |
2.4812 | 13550 | 0.0032 | - |
2.4904 | 13600 | 0.0012 | - |
2.4995 | 13650 | 0.0012 | - |
2.5087 | 13700 | 0.0014 | - |
2.5179 | 13750 | 0.001 | - |
2.5270 | 13800 | 0.0011 | - |
2.5362 | 13850 | 0.0009 | - |
2.5453 | 13900 | 0.0034 | - |
2.5545 | 13950 | 0.0015 | - |
2.5636 | 14000 | 0.0013 | - |
2.5728 | 14050 | 0.0069 | - |
2.5819 | 14100 | 0.001 | - |
2.5911 | 14150 | 0.0034 | - |
2.6003 | 14200 | 0.0028 | - |
2.6094 | 14250 | 0.001 | - |
2.6186 | 14300 | 0.0012 | - |
2.6277 | 14350 | 0.0013 | - |
2.6369 | 14400 | 0.0011 | - |
2.6460 | 14450 | 0.0009 | - |
2.6552 | 14500 | 0.001 | - |
2.6643 | 14550 | 0.0009 | - |
2.6735 | 14600 | 0.0012 | - |
2.6827 | 14650 | 0.0041 | - |
2.6918 | 14700 | 0.0008 | - |
2.7010 | 14750 | 0.0019 | - |
2.7101 | 14800 | 0.001 | - |
2.7193 | 14850 | 0.0012 | - |
2.7284 | 14900 | 0.0013 | - |
2.7376 | 14950 | 0.0012 | - |
2.7467 | 15000 | 0.0019 | - |
2.7559 | 15050 | 0.0009 | - |
2.7651 | 15100 | 0.0009 | - |
2.7742 | 15150 | 0.0008 | - |
2.7834 | 15200 | 0.0028 | - |
2.7925 | 15250 | 0.0009 | - |
2.8017 | 15300 | 0.0011 | - |
2.8108 | 15350 | 0.0029 | - |
2.8200 | 15400 | 0.0008 | - |
2.8292 | 15450 | 0.001 | - |
2.8383 | 15500 | 0.0019 | - |
2.8475 | 15550 | 0.0011 | - |
2.8566 | 15600 | 0.0022 | - |
2.8658 | 15650 | 0.0011 | - |
2.8749 | 15700 | 0.0009 | - |
2.8841 | 15750 | 0.0008 | - |
2.8932 | 15800 | 0.0009 | - |
2.9024 | 15850 | 0.0009 | - |
2.9116 | 15900 | 0.0011 | - |
2.9207 | 15950 | 0.0011 | - |
2.9299 | 16000 | 0.0017 | - |
2.9390 | 16050 | 0.001 | - |
2.9482 | 16100 | 0.0008 | - |
2.9573 | 16150 | 0.0009 | - |
2.9665 | 16200 | 0.0008 | - |
2.9756 | 16250 | 0.0009 | - |
2.9848 | 16300 | 0.0007 | - |
2.9940 | 16350 | 0.0011 | - |
3.0 | 16383 | - | 0.0990 |
3.0031 | 16400 | 0.0008 | - |
3.0123 | 16450 | 0.0008 | - |
3.0214 | 16500 | 0.0008 | - |
3.0306 | 16550 | 0.0008 | - |
3.0397 | 16600 | 0.0015 | - |
3.0489 | 16650 | 0.0007 | - |
3.0580 | 16700 | 0.0008 | - |
3.0672 | 16750 | 0.0009 | - |
3.0764 | 16800 | 0.0008 | - |
3.0855 | 16850 | 0.0008 | - |
3.0947 | 16900 | 0.0023 | - |
3.1038 | 16950 | 0.0007 | - |
3.1130 | 17000 | 0.0006 | - |
3.1221 | 17050 | 0.0024 | - |
3.1313 | 17100 | 0.0008 | - |
3.1405 | 17150 | 0.0017 | - |
3.1496 | 17200 | 0.0011 | - |
3.1588 | 17250 | 0.0008 | - |
3.1679 | 17300 | 0.0008 | - |
3.1771 | 17350 | 0.0007 | - |
3.1862 | 17400 | 0.0014 | - |
3.1954 | 17450 | 0.0008 | - |
3.2045 | 17500 | 0.0007 | - |
3.2137 | 17550 | 0.0007 | - |
3.2229 | 17600 | 0.0006 | - |
3.2320 | 17650 | 0.0007 | - |
3.2412 | 17700 | 0.0021 | - |
3.2503 | 17750 | 0.0006 | - |
3.2595 | 17800 | 0.0006 | - |
3.2686 | 17850 | 0.0007 | - |
3.2778 | 17900 | 0.0006 | - |
3.2869 | 17950 | 0.0008 | - |
3.2961 | 18000 | 0.0008 | - |
3.3053 | 18050 | 0.0008 | - |
3.3144 | 18100 | 0.0027 | - |
3.3236 | 18150 | 0.0008 | - |
3.3327 | 18200 | 0.0007 | - |
3.3419 | 18250 | 0.0007 | - |
3.3510 | 18300 | 0.0008 | - |
3.3602 | 18350 | 0.0007 | - |
3.3693 | 18400 | 0.0022 | - |
3.3785 | 18450 | 0.0007 | - |
3.3877 | 18500 | 0.0014 | - |
3.3968 | 18550 | 0.0006 | - |
3.4060 | 18600 | 0.0016 | - |
3.4151 | 18650 | 0.0007 | - |
3.4243 | 18700 | 0.0015 | - |
3.4334 | 18750 | 0.0006 | - |
3.4426 | 18800 | 0.001 | - |
3.4517 | 18850 | 0.0008 | - |
3.4609 | 18900 | 0.0008 | - |
3.4701 | 18950 | 0.0007 | - |
3.4792 | 19000 | 0.0015 | - |
3.4884 | 19050 | 0.0007 | - |
3.4975 | 19100 | 0.0006 | - |
3.5067 | 19150 | 0.0007 | - |
3.5158 | 19200 | 0.0014 | - |
3.5250 | 19250 | 0.0006 | - |
3.5342 | 19300 | 0.0011 | - |
3.5433 | 19350 | 0.0008 | - |
3.5525 | 19400 | 0.0007 | - |
3.5616 | 19450 | 0.0008 | - |
3.5708 | 19500 | 0.0021 | - |
3.5799 | 19550 | 0.0007 | - |
3.5891 | 19600 | 0.0007 | - |
3.5982 | 19650 | 0.0006 | - |
3.6074 | 19700 | 0.0007 | - |
3.6166 | 19750 | 0.0007 | - |
3.6257 | 19800 | 0.0007 | - |
3.6349 | 19850 | 0.001 | - |
3.6440 | 19900 | 0.0011 | - |
3.6532 | 19950 | 0.0007 | - |
3.6623 | 20000 | 0.0006 | - |
3.6715 | 20050 | 0.0022 | - |
3.6806 | 20100 | 0.0011 | - |
3.6898 | 20150 | 0.0007 | - |
3.6990 | 20200 | 0.0006 | - |
3.7081 | 20250 | 0.0007 | - |
3.7173 | 20300 | 0.0006 | - |
3.7264 | 20350 | 0.0006 | - |
3.7356 | 20400 | 0.0013 | - |
3.7447 | 20450 | 0.0009 | - |
3.7539 | 20500 | 0.0006 | - |
3.7630 | 20550 | 0.001 | - |
3.7722 | 20600 | 0.0007 | - |
3.7814 | 20650 | 0.0007 | - |
3.7905 | 20700 | 0.0006 | - |
3.7997 | 20750 | 0.0006 | - |
3.8088 | 20800 | 0.0015 | - |
3.8180 | 20850 | 0.0009 | - |
3.8271 | 20900 | 0.0009 | - |
3.8363 | 20950 | 0.0005 | - |
3.8454 | 21000 | 0.0008 | - |
3.8546 | 21050 | 0.0006 | - |
3.8638 | 21100 | 0.0008 | - |
3.8729 | 21150 | 0.0006 | - |
3.8821 | 21200 | 0.0006 | - |
3.8912 | 21250 | 0.0005 | - |
3.9004 | 21300 | 0.0006 | - |
3.9095 | 21350 | 0.0015 | - |
3.9187 | 21400 | 0.0017 | - |
3.9279 | 21450 | 0.0006 | - |
3.9370 | 21500 | 0.0007 | - |
3.9462 | 21550 | 0.0014 | - |
3.9553 | 21600 | 0.0012 | - |
3.9645 | 21650 | 0.0017 | - |
3.9736 | 21700 | 0.0008 | - |
3.9828 | 21750 | 0.0006 | - |
3.9919 | 21800 | 0.0006 | - |
4.0 | 21844 | - | 0.1004 |
Framework Versions
- Python: 3.11.10
- SetFit: 1.1.0
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.4.1+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
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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