SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L6-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-L6-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 75 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 |
---|---|
9 |
|
43 |
|
66 |
|
22 |
|
5 |
|
52 |
|
67 |
|
32 |
|
53 |
|
16 |
|
4 |
|
65 |
|
55 |
|
12 |
|
71 |
|
25 |
|
6 |
|
20 |
|
10 |
|
0 |
|
42 |
|
57 |
|
36 |
|
37 |
|
58 |
|
56 |
|
17 |
|
72 |
|
54 |
|
59 |
|
60 |
|
1 |
|
47 |
|
28 |
|
13 |
|
26 |
|
15 |
|
50 |
|
24 |
|
29 |
|
44 |
|
38 |
|
23 |
|
45 |
|
31 |
|
19 |
|
11 |
|
73 |
|
64 |
|
35 |
|
21 |
|
74 |
|
3 |
|
8 |
|
18 |
|
49 |
|
27 |
|
63 |
|
61 |
|
34 |
|
30 |
|
7 |
|
14 |
|
48 |
|
2 |
|
46 |
|
51 |
|
39 |
|
70 |
|
68 |
|
40 |
|
69 |
|
33 |
|
41 |
|
62 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.3463 |
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("Jazielinho/fabric_model")
# Run inference
preds = model("What fabric has a comfortable feel and is suitable for people with sensitive skin?")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 7 | 15.4858 | 30 |
Label | Training Sample Count |
---|---|
0 | 39 |
1 | 40 |
2 | 41 |
3 | 32 |
4 | 37 |
5 | 33 |
6 | 36 |
7 | 40 |
8 | 30 |
9 | 36 |
10 | 42 |
11 | 38 |
12 | 39 |
13 | 43 |
14 | 41 |
15 | 41 |
16 | 35 |
17 | 42 |
18 | 40 |
19 | 43 |
20 | 44 |
21 | 36 |
22 | 37 |
23 | 40 |
24 | 44 |
25 | 42 |
26 | 41 |
27 | 38 |
28 | 41 |
29 | 46 |
30 | 41 |
31 | 38 |
32 | 40 |
33 | 39 |
34 | 41 |
35 | 44 |
36 | 45 |
37 | 40 |
38 | 37 |
39 | 44 |
40 | 39 |
41 | 42 |
42 | 36 |
43 | 43 |
44 | 42 |
45 | 37 |
46 | 41 |
47 | 44 |
48 | 36 |
49 | 40 |
50 | 43 |
51 | 44 |
52 | 39 |
53 | 38 |
54 | 38 |
55 | 43 |
56 | 41 |
57 | 44 |
58 | 40 |
59 | 41 |
60 | 35 |
61 | 43 |
62 | 41 |
63 | 43 |
64 | 37 |
65 | 41 |
66 | 36 |
67 | 38 |
68 | 42 |
69 | 41 |
70 | 39 |
71 | 43 |
72 | 34 |
73 | 40 |
74 | 41 |
Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: undersampling
- 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 |
---|---|---|---|
0.0000 | 1 | 0.2732 | - |
0.0015 | 50 | 0.2545 | - |
0.0029 | 100 | 0.2538 | - |
0.0044 | 150 | 0.2633 | - |
0.0058 | 200 | 0.2598 | - |
0.0073 | 250 | 0.2624 | - |
0.0087 | 300 | 0.2537 | - |
0.0102 | 350 | 0.2592 | - |
0.0116 | 400 | 0.2475 | - |
0.0131 | 450 | 0.2483 | - |
0.0145 | 500 | 0.2418 | - |
0.0160 | 550 | 0.2403 | - |
0.0174 | 600 | 0.2386 | - |
0.0189 | 650 | 0.2542 | - |
0.0203 | 700 | 0.237 | - |
0.0218 | 750 | 0.2423 | - |
0.0232 | 800 | 0.2421 | - |
0.0247 | 850 | 0.2409 | - |
0.0261 | 900 | 0.2453 | - |
0.0276 | 950 | 0.2404 | - |
0.0290 | 1000 | 0.2418 | - |
0.0305 | 1050 | 0.2454 | - |
0.0319 | 1100 | 0.2446 | - |
0.0001 | 1 | 0.2471 | - |
0.0058 | 50 | 0.2375 | - |
0.0116 | 100 | 0.2351 | - |
0.0174 | 150 | 0.2406 | - |
0.0232 | 200 | 0.2382 | - |
0.0290 | 250 | 0.2374 | - |
0.0000 | 1 | 0.2515 | - |
0.0007 | 50 | 0.2335 | - |
0.0015 | 100 | 0.229 | - |
0.0022 | 150 | 0.2387 | - |
0.0029 | 200 | 0.2209 | - |
0.0036 | 250 | 0.2367 | - |
0.0044 | 300 | 0.2521 | - |
0.0051 | 350 | 0.239 | - |
0.0058 | 400 | 0.2405 | - |
0.0065 | 450 | 0.2541 | - |
0.0073 | 500 | 0.2308 | - |
0.0080 | 550 | 0.2381 | - |
0.0087 | 600 | 0.2456 | - |
0.0094 | 650 | 0.2301 | - |
0.0102 | 700 | 0.2486 | - |
0.0109 | 750 | 0.2243 | - |
0.0116 | 800 | 0.2399 | - |
0.0123 | 850 | 0.2341 | - |
0.0131 | 900 | 0.2417 | - |
0.0138 | 950 | 0.215 | - |
0.0145 | 1000 | 0.2264 | - |
0.0152 | 1050 | 0.2161 | - |
0.0160 | 1100 | 0.2273 | - |
0.0167 | 1150 | 0.2345 | - |
0.0174 | 1200 | 0.2302 | - |
0.0181 | 1250 | 0.2337 | - |
0.0189 | 1300 | 0.2278 | - |
0.0196 | 1350 | 0.2345 | - |
0.0203 | 1400 | 0.2323 | - |
0.0210 | 1450 | 0.2371 | - |
0.0218 | 1500 | 0.2217 | - |
0.0225 | 1550 | 0.2282 | - |
0.0232 | 1600 | 0.224 | - |
0.0239 | 1650 | 0.2346 | - |
0.0247 | 1700 | 0.2087 | - |
0.0254 | 1750 | 0.2299 | - |
0.0261 | 1800 | 0.2154 | - |
0.0268 | 1850 | 0.2108 | - |
0.0276 | 1900 | 0.216 | - |
0.0283 | 1950 | 0.2128 | - |
0.0290 | 2000 | 0.2083 | - |
0.0297 | 2050 | 0.2053 | - |
0.0305 | 2100 | 0.2265 | - |
0.0312 | 2150 | 0.2245 | - |
0.0319 | 2200 | 0.2036 | - |
0.0326 | 2250 | 0.2192 | - |
0.0334 | 2300 | 0.2259 | - |
0.0341 | 2350 | 0.2038 | - |
0.0348 | 2400 | 0.2129 | - |
0.0355 | 2450 | 0.2029 | - |
0.0363 | 2500 | 0.1883 | - |
0.0370 | 2550 | 0.187 | - |
0.0377 | 2600 | 0.2083 | - |
0.0384 | 2650 | 0.2138 | - |
0.0392 | 2700 | 0.2057 | - |
0.0399 | 2750 | 0.2134 | - |
0.0406 | 2800 | 0.2008 | - |
0.0413 | 2850 | 0.2018 | - |
0.0421 | 2900 | 0.2226 | - |
0.0428 | 2950 | 0.1815 | - |
0.0435 | 3000 | 0.1943 | - |
0.0442 | 3050 | 0.1926 | - |
0.0450 | 3100 | 0.1877 | - |
0.0457 | 3150 | 0.1764 | - |
0.0464 | 3200 | 0.2021 | - |
0.0471 | 3250 | 0.2071 | - |
0.0479 | 3300 | 0.1832 | - |
0.0486 | 3350 | 0.1714 | - |
0.0493 | 3400 | 0.1914 | - |
0.0500 | 3450 | 0.1749 | - |
0.0508 | 3500 | 0.1752 | - |
0.0515 | 3550 | 0.1829 | - |
0.0522 | 3600 | 0.175 | - |
0.0529 | 3650 | 0.1752 | - |
0.0537 | 3700 | 0.1973 | - |
0.0544 | 3750 | 0.1866 | - |
0.0551 | 3800 | 0.156 | - |
0.0558 | 3850 | 0.1923 | - |
0.0566 | 3900 | 0.1683 | - |
0.0573 | 3950 | 0.1642 | - |
0.0580 | 4000 | 0.1705 | - |
0.0587 | 4050 | 0.174 | - |
0.0595 | 4100 | 0.1609 | - |
0.0602 | 4150 | 0.17 | - |
0.0609 | 4200 | 0.1843 | - |
0.0616 | 4250 | 0.1855 | - |
0.0624 | 4300 | 0.1385 | - |
0.0631 | 4350 | 0.1765 | - |
0.0638 | 4400 | 0.1873 | - |
0.0645 | 4450 | 0.1654 | - |
0.0653 | 4500 | 0.1912 | - |
0.0660 | 4550 | 0.1533 | - |
0.0667 | 4600 | 0.1759 | - |
0.0674 | 4650 | 0.154 | - |
0.0682 | 4700 | 0.147 | - |
0.0689 | 4750 | 0.161 | - |
0.0696 | 4800 | 0.1603 | - |
0.0703 | 4850 | 0.1529 | - |
0.0711 | 4900 | 0.1538 | - |
0.0718 | 4950 | 0.1487 | - |
0.0725 | 5000 | 0.1593 | - |
0.0732 | 5050 | 0.1491 | - |
0.0740 | 5100 | 0.1389 | - |
0.0747 | 5150 | 0.1132 | - |
0.0754 | 5200 | 0.1622 | - |
0.0761 | 5250 | 0.1628 | - |
0.0769 | 5300 | 0.1598 | - |
0.0776 | 5350 | 0.1362 | - |
0.0783 | 5400 | 0.1637 | - |
0.0790 | 5450 | 0.1352 | - |
0.0798 | 5500 | 0.1523 | - |
0.0805 | 5550 | 0.1604 | - |
0.0812 | 5600 | 0.1534 | - |
0.0819 | 5650 | 0.1206 | - |
0.0827 | 5700 | 0.1331 | - |
0.0834 | 5750 | 0.1449 | - |
0.0841 | 5800 | 0.1376 | - |
0.0848 | 5850 | 0.1293 | - |
0.0856 | 5900 | 0.1258 | - |
0.0863 | 5950 | 0.1391 | - |
0.0870 | 6000 | 0.1678 | - |
0.0877 | 6050 | 0.1439 | - |
0.0885 | 6100 | 0.1329 | - |
0.0892 | 6150 | 0.1416 | - |
0.0899 | 6200 | 0.126 | - |
0.0906 | 6250 | 0.1072 | - |
0.0914 | 6300 | 0.1314 | - |
0.0921 | 6350 | 0.1282 | - |
0.0928 | 6400 | 0.1418 | - |
0.0935 | 6450 | 0.1418 | - |
0.0943 | 6500 | 0.1126 | - |
0.0950 | 6550 | 0.1118 | - |
0.0957 | 6600 | 0.1437 | - |
0.0964 | 6650 | 0.1265 | - |
0.0972 | 6700 | 0.1203 | - |
0.0979 | 6750 | 0.1267 | - |
0.0986 | 6800 | 0.11 | - |
0.0993 | 6850 | 0.1273 | - |
0.1001 | 6900 | 0.1253 | - |
0.1008 | 6950 | 0.1145 | - |
0.1015 | 7000 | 0.1054 | - |
0.1022 | 7050 | 0.1311 | - |
0.1030 | 7100 | 0.1238 | - |
0.1037 | 7150 | 0.0951 | - |
0.1044 | 7200 | 0.1187 | - |
0.1051 | 7250 | 0.1114 | - |
0.1059 | 7300 | 0.1038 | - |
0.1066 | 7350 | 0.1048 | - |
0.1073 | 7400 | 0.0965 | - |
0.1080 | 7450 | 0.1006 | - |
0.1088 | 7500 | 0.1273 | - |
0.1095 | 7550 | 0.12 | - |
0.1102 | 7600 | 0.1055 | - |
0.0001 | 1 | 0.1192 | - |
0.0029 | 50 | 0.1128 | - |
0.0057 | 100 | 0.0981 | - |
0.0021 | 1 | 0.1188 | - |
0.1040 | 50 | 0.1121 | - |
0.0021 | 1 | 0.1172 | - |
0.1040 | 50 | 0.1109 | - |
0.2079 | 100 | 0.0965 | - |
0.3119 | 150 | 0.1013 | - |
0.4158 | 200 | 0.1157 | - |
0.5198 | 250 | 0.1093 | - |
0.6237 | 300 | 0.1029 | - |
0.7277 | 350 | 0.0904 | - |
0.8316 | 400 | 0.1084 | - |
0.9356 | 450 | 0.1127 | - |
1.0 | 481 | - | 0.1883 |
1.0395 | 500 | 0.0853 | - |
1.1435 | 550 | 0.0907 | - |
1.2474 | 600 | 0.0814 | - |
1.3514 | 650 | 0.0967 | - |
1.4553 | 700 | 0.118 | - |
1.5593 | 750 | 0.0841 | - |
1.6632 | 800 | 0.0992 | - |
1.7672 | 850 | 0.0965 | - |
1.8711 | 900 | 0.092 | - |
1.9751 | 950 | 0.109 | - |
2.0 | 962 | - | 0.193 |
2.0790 | 1000 | 0.0847 | - |
2.1830 | 1050 | 0.0864 | - |
2.2869 | 1100 | 0.0843 | - |
2.3909 | 1150 | 0.0792 | - |
2.4948 | 1200 | 0.0808 | - |
2.5988 | 1250 | 0.0913 | - |
2.7027 | 1300 | 0.0848 | - |
2.8067 | 1350 | 0.0889 | - |
2.9106 | 1400 | 0.0673 | - |
3.0 | 1443 | - | 0.1983 |
3.0146 | 1450 | 0.0671 | - |
3.1185 | 1500 | 0.0643 | - |
3.2225 | 1550 | 0.0649 | - |
3.3264 | 1600 | 0.0827 | - |
3.4304 | 1650 | 0.0752 | - |
3.5343 | 1700 | 0.0785 | - |
3.6383 | 1750 | 0.0629 | - |
3.7422 | 1800 | 0.0726 | - |
3.8462 | 1850 | 0.0672 | - |
3.9501 | 1900 | 0.0704 | - |
4.0 | 1924 | - | 0.2015 |
4.0541 | 1950 | 0.0812 | - |
4.1580 | 2000 | 0.0709 | - |
4.2620 | 2050 | 0.0866 | - |
4.3659 | 2100 | 0.0747 | - |
4.4699 | 2150 | 0.0554 | - |
4.5738 | 2200 | 0.0636 | - |
4.6778 | 2250 | 0.0655 | - |
4.7817 | 2300 | 0.0562 | - |
4.8857 | 2350 | 0.0531 | - |
4.9896 | 2400 | 0.0518 | - |
5.0 | 2405 | - | 0.2056 |
5.0936 | 2450 | 0.0808 | - |
5.1975 | 2500 | 0.0571 | - |
5.3015 | 2550 | 0.066 | - |
5.4054 | 2600 | 0.071 | - |
5.5094 | 2650 | 0.0507 | - |
5.6133 | 2700 | 0.0603 | - |
5.7173 | 2750 | 0.0548 | - |
5.8212 | 2800 | 0.0714 | - |
5.9252 | 2850 | 0.0532 | - |
6.0 | 2886 | - | 0.208 |
6.0291 | 2900 | 0.0581 | - |
6.1331 | 2950 | 0.0663 | - |
6.2370 | 3000 | 0.0717 | - |
6.3410 | 3050 | 0.0549 | - |
6.4449 | 3100 | 0.0611 | - |
6.5489 | 3150 | 0.0515 | - |
6.6528 | 3200 | 0.0546 | - |
6.7568 | 3250 | 0.0406 | - |
6.8607 | 3300 | 0.0582 | - |
6.9647 | 3350 | 0.0565 | - |
7.0 | 3367 | - | 0.2176 |
7.0686 | 3400 | 0.0737 | - |
7.1726 | 3450 | 0.0554 | - |
7.2765 | 3500 | 0.0462 | - |
7.3805 | 3550 | 0.051 | - |
7.4844 | 3600 | 0.0441 | - |
7.5884 | 3650 | 0.0503 | - |
7.6923 | 3700 | 0.0531 | - |
7.7963 | 3750 | 0.0464 | - |
7.9002 | 3800 | 0.0443 | - |
8.0 | 3848 | - | 0.2234 |
8.0042 | 3850 | 0.0376 | - |
8.1081 | 3900 | 0.0542 | - |
8.2121 | 3950 | 0.0453 | - |
8.3160 | 4000 | 0.0448 | - |
8.4200 | 4050 | 0.0535 | - |
8.5239 | 4100 | 0.0645 | - |
8.6279 | 4150 | 0.0451 | - |
8.7318 | 4200 | 0.0472 | - |
8.8358 | 4250 | 0.0477 | - |
8.9397 | 4300 | 0.0327 | - |
9.0 | 4329 | - | 0.2272 |
9.0437 | 4350 | 0.0346 | - |
9.1476 | 4400 | 0.0435 | - |
9.2516 | 4450 | 0.0479 | - |
9.3555 | 4500 | 0.0508 | - |
9.4595 | 4550 | 0.0535 | - |
9.5634 | 4600 | 0.0631 | - |
9.6674 | 4650 | 0.0286 | - |
9.7713 | 4700 | 0.0564 | - |
9.8753 | 4750 | 0.0349 | - |
9.9792 | 4800 | 0.0487 | - |
10.0 | 4810 | - | 0.2288 |
10.0832 | 4850 | 0.0317 | - |
10.1871 | 4900 | 0.0546 | - |
10.2911 | 4950 | 0.0353 | - |
10.3950 | 5000 | 0.0437 | - |
10.4990 | 5050 | 0.056 | - |
10.6029 | 5100 | 0.0353 | - |
10.7069 | 5150 | 0.0304 | - |
10.8108 | 5200 | 0.0358 | - |
10.9148 | 5250 | 0.0481 | - |
11.0 | 5291 | - | 0.2282 |
11.0187 | 5300 | 0.0318 | - |
11.1227 | 5350 | 0.0373 | - |
11.2266 | 5400 | 0.0305 | - |
11.3306 | 5450 | 0.0443 | - |
11.4345 | 5500 | 0.0383 | - |
11.5385 | 5550 | 0.0425 | - |
11.6424 | 5600 | 0.039 | - |
11.7464 | 5650 | 0.0443 | - |
11.8503 | 5700 | 0.0503 | - |
11.9543 | 5750 | 0.0553 | - |
12.0 | 5772 | - | 0.2342 |
12.0582 | 5800 | 0.0362 | - |
12.1622 | 5850 | 0.0509 | - |
12.2661 | 5900 | 0.0337 | - |
12.3701 | 5950 | 0.0436 | - |
12.4740 | 6000 | 0.0462 | - |
12.5780 | 6050 | 0.034 | - |
12.6819 | 6100 | 0.0334 | - |
12.7859 | 6150 | 0.0365 | - |
12.8898 | 6200 | 0.047 | - |
12.9938 | 6250 | 0.0489 | - |
13.0 | 6253 | - | 0.2317 |
13.0977 | 6300 | 0.035 | - |
13.2017 | 6350 | 0.0412 | - |
13.3056 | 6400 | 0.0358 | - |
13.4096 | 6450 | 0.0366 | - |
13.5135 | 6500 | 0.0473 | - |
13.6175 | 6550 | 0.0481 | - |
13.7214 | 6600 | 0.0443 | - |
13.8254 | 6650 | 0.0454 | - |
13.9293 | 6700 | 0.0344 | - |
14.0 | 6734 | - | 0.2304 |
14.0333 | 6750 | 0.0327 | - |
14.1372 | 6800 | 0.0386 | - |
14.2412 | 6850 | 0.0503 | - |
14.3451 | 6900 | 0.0236 | - |
14.4491 | 6950 | 0.042 | - |
14.5530 | 7000 | 0.0405 | - |
14.6570 | 7050 | 0.0339 | - |
14.7609 | 7100 | 0.0435 | - |
14.8649 | 7150 | 0.0314 | - |
14.9688 | 7200 | 0.0263 | - |
15.0 | 7215 | - | 0.234 |
15.0728 | 7250 | 0.0369 | - |
15.1767 | 7300 | 0.0329 | - |
15.2807 | 7350 | 0.0366 | - |
15.3846 | 7400 | 0.0401 | - |
15.4886 | 7450 | 0.0321 | - |
15.5925 | 7500 | 0.0571 | - |
15.6965 | 7550 | 0.0353 | - |
15.8004 | 7600 | 0.0381 | - |
15.9044 | 7650 | 0.0347 | - |
16.0 | 7696 | - | 0.2334 |
16.0083 | 7700 | 0.0341 | - |
16.1123 | 7750 | 0.0276 | - |
16.2162 | 7800 | 0.0555 | - |
16.3202 | 7850 | 0.0338 | - |
16.4241 | 7900 | 0.0227 | - |
16.5281 | 7950 | 0.0256 | - |
16.6320 | 8000 | 0.0356 | - |
16.7360 | 8050 | 0.0413 | - |
16.8399 | 8100 | 0.032 | - |
16.9439 | 8150 | 0.0329 | - |
17.0 | 8177 | - | 0.2356 |
17.0478 | 8200 | 0.0382 | - |
17.1518 | 8250 | 0.0434 | - |
17.2557 | 8300 | 0.0411 | - |
17.3597 | 8350 | 0.0329 | - |
17.4636 | 8400 | 0.0388 | - |
17.5676 | 8450 | 0.0384 | - |
17.6715 | 8500 | 0.0306 | - |
17.7755 | 8550 | 0.0185 | - |
17.8794 | 8600 | 0.0357 | - |
17.9834 | 8650 | 0.0349 | - |
18.0 | 8658 | - | 0.2368 |
18.0873 | 8700 | 0.0515 | - |
18.1913 | 8750 | 0.0326 | - |
18.2952 | 8800 | 0.0367 | - |
18.3992 | 8850 | 0.0241 | - |
18.5031 | 8900 | 0.0313 | - |
18.6071 | 8950 | 0.0275 | - |
18.7110 | 9000 | 0.0378 | - |
18.8150 | 9050 | 0.0401 | - |
18.9189 | 9100 | 0.0285 | - |
19.0 | 9139 | - | 0.2347 |
19.0229 | 9150 | 0.0309 | - |
19.1268 | 9200 | 0.035 | - |
19.2308 | 9250 | 0.0415 | - |
19.3347 | 9300 | 0.0301 | - |
19.4387 | 9350 | 0.0293 | - |
19.5426 | 9400 | 0.0323 | - |
19.6466 | 9450 | 0.0342 | - |
19.7505 | 9500 | 0.0205 | - |
19.8545 | 9550 | 0.0331 | - |
19.9584 | 9600 | 0.0226 | - |
20.0 | 9620 | - | 0.237 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- 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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