metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
so the representative presentative that i talked to a nine o'clock this
morning but it said that they were gonna call the customer service my
service or whatever that they were gonna call to to see if they dispute it
and i didn't get a call back bags i'm a mother of three kids i worked were
two job i can't have a phone that that's not on and i don't have the money
the money to pay a hundred and fifty six eighty six dollars you guys want
me to pay right now it's restore my service
- text: >-
i understand kelly yes let me send you a little bit about be a moment okay
is is that the pin number that you have the four days four digits that you
have with us is
- text: >-
yeah 'cause that that's that's really that's ridiculous you know that's
ridiculous for her to do that i like i have you know all the time in the
world okay all right okay okay no i got my card no cathy she asked me for
my last time and i did not have it i'm
- text: >-
ma'am uh thanks for holding for holding by the way everything upon
checking i'm checking here a record ma'am aah for your for your current a
hot spot you said you said you still have twenty gigabytes left
- text: >-
and then when i asked i didn't even get to speak to the supervisor
provider i just got hung up on
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.5463414634146342
name: Accuracy
SetFit with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-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/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 tokens
- Number of Classes: 64 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 |
|
20 |
|
11 |
|
16 |
|
35 |
|
21 |
|
46 |
|
47 |
|
6 |
|
51 |
|
27 |
|
19 |
|
62 |
|
12 |
|
13 |
|
56 |
|
31 |
|
49 |
|
15 |
|
60 |
|
7 |
|
33 |
|
4 |
|
3 |
|
61 |
|
22 |
|
30 |
|
48 |
|
39 |
|
10 |
|
57 |
|
50 |
|
8 |
|
36 |
|
54 |
|
63 |
|
43 |
|
55 |
|
5 |
|
14 |
|
2 |
|
58 |
|
37 |
|
23 |
|
52 |
|
28 |
|
59 |
|
53 |
|
45 |
|
41 |
|
17 |
|
34 |
|
25 |
|
24 |
|
9 |
|
1 |
|
29 |
|
32 |
|
18 |
|
44 |
|
26 |
|
42 |
|
40 |
|
38 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.5463 |
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("Jalajkx/all_mpnetcric-setfit-model")
# Run inference
preds = model("and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 32.4224 | 283 |
Label | Training Sample Count |
---|---|
0 | 36 |
1 | 36 |
2 | 36 |
3 | 36 |
4 | 36 |
5 | 36 |
6 | 36 |
7 | 36 |
8 | 36 |
9 | 6 |
10 | 36 |
11 | 36 |
12 | 36 |
13 | 9 |
14 | 36 |
15 | 36 |
16 | 17 |
17 | 36 |
18 | 4 |
19 | 29 |
20 | 30 |
21 | 36 |
22 | 25 |
23 | 36 |
24 | 36 |
25 | 36 |
26 | 4 |
27 | 36 |
28 | 36 |
29 | 4 |
30 | 8 |
31 | 36 |
32 | 4 |
33 | 36 |
34 | 11 |
35 | 36 |
36 | 36 |
37 | 36 |
38 | 10 |
39 | 13 |
40 | 2 |
41 | 36 |
42 | 9 |
43 | 36 |
44 | 10 |
45 | 36 |
46 | 36 |
47 | 14 |
48 | 36 |
49 | 36 |
50 | 36 |
51 | 36 |
52 | 36 |
53 | 36 |
54 | 36 |
55 | 36 |
56 | 36 |
57 | 36 |
58 | 36 |
59 | 8 |
60 | 36 |
61 | 36 |
62 | 36 |
63 | 36 |
Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 25
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 | 0.2196 | - |
0.0022 | 50 | 0.2183 | - |
0.0044 | 100 | 0.3574 | - |
0.0065 | 150 | 0.1756 | - |
0.0087 | 200 | 0.1396 | - |
0.0109 | 250 | 0.2875 | - |
0.0131 | 300 | 0.1307 | - |
0.0152 | 350 | 0.1465 | - |
0.0174 | 400 | 0.1503 | - |
0.0196 | 450 | 0.1579 | - |
0.0218 | 500 | 0.3216 | - |
0.0240 | 550 | 0.2399 | - |
0.0261 | 600 | 0.2824 | - |
0.0283 | 650 | 0.1217 | - |
0.0305 | 700 | 0.0647 | - |
0.0327 | 750 | 0.2651 | - |
0.0348 | 800 | 0.1792 | - |
0.0370 | 850 | 0.1461 | - |
0.0392 | 900 | 0.0256 | - |
0.0414 | 950 | 0.1175 | - |
0.0435 | 1000 | 0.2394 | - |
0.0457 | 1050 | 0.1582 | - |
0.0479 | 1100 | 0.2785 | - |
0.0501 | 1150 | 0.0611 | - |
0.0523 | 1200 | 0.1937 | - |
0.0544 | 1250 | 0.0804 | - |
0.0566 | 1300 | 0.0811 | - |
0.0588 | 1350 | 0.0663 | - |
0.0610 | 1400 | 0.2148 | - |
0.0631 | 1450 | 0.0428 | - |
0.0653 | 1500 | 0.0083 | - |
0.0675 | 1550 | 0.0884 | - |
0.0697 | 1600 | 0.1341 | - |
0.0719 | 1650 | 0.0949 | - |
0.0740 | 1700 | 0.1839 | - |
0.0762 | 1750 | 0.2244 | - |
0.0784 | 1800 | 0.0309 | - |
0.0806 | 1850 | 0.0277 | - |
0.0827 | 1900 | 0.2016 | - |
0.0849 | 1950 | 0.1174 | - |
0.0871 | 2000 | 0.0942 | - |
0.0893 | 2050 | 0.0483 | - |
0.0915 | 2100 | 0.2057 | - |
0.0936 | 2150 | 0.0151 | - |
0.0958 | 2200 | 0.023 | - |
0.0980 | 2250 | 0.0514 | - |
0.1002 | 2300 | 0.1541 | - |
0.1023 | 2350 | 0.1426 | - |
0.1045 | 2400 | 0.0187 | - |
0.1067 | 2450 | 0.0386 | - |
0.1089 | 2500 | 0.274 | - |
0.1110 | 2550 | 0.0723 | - |
0.1132 | 2600 | 0.0115 | - |
0.1154 | 2650 | 0.053 | - |
0.1176 | 2700 | 0.2371 | - |
0.1198 | 2750 | 0.2472 | - |
0.1219 | 2800 | 0.0386 | - |
0.1241 | 2850 | 0.0159 | - |
0.1263 | 2900 | 0.0276 | - |
0.1285 | 2950 | 0.1229 | - |
0.1306 | 3000 | 0.0037 | - |
0.1328 | 3050 | 0.0029 | - |
0.1350 | 3100 | 0.0037 | - |
0.1372 | 3150 | 0.022 | - |
0.1394 | 3200 | 0.0389 | - |
0.1415 | 3250 | 0.0146 | - |
0.1437 | 3300 | 0.0034 | - |
0.1459 | 3350 | 0.0721 | - |
0.1481 | 3400 | 0.0462 | - |
0.1502 | 3450 | 0.0039 | - |
0.1524 | 3500 | 0.1225 | - |
0.1546 | 3550 | 0.0009 | - |
0.1568 | 3600 | 0.1005 | - |
0.1590 | 3650 | 0.008 | - |
0.1611 | 3700 | 0.121 | - |
0.1633 | 3750 | 0.2982 | - |
0.1655 | 3800 | 0.008 | - |
0.1677 | 3850 | 0.001 | - |
0.1698 | 3900 | 0.216 | - |
0.1720 | 3950 | 0.0458 | - |
0.1742 | 4000 | 0.0155 | - |
0.1764 | 4050 | 0.1235 | - |
0.1785 | 4100 | 0.0059 | - |
0.1807 | 4150 | 0.2421 | - |
0.1829 | 4200 | 0.2232 | - |
0.1851 | 4250 | 0.0396 | - |
0.1873 | 4300 | 0.2164 | - |
0.1894 | 4350 | 0.0839 | - |
0.1916 | 4400 | 0.0116 | - |
0.1938 | 4450 | 0.2666 | - |
0.1960 | 4500 | 0.0648 | - |
0.1981 | 4550 | 0.074 | - |
0.2003 | 4600 | 0.077 | - |
0.2025 | 4650 | 0.0739 | - |
0.2047 | 4700 | 0.0029 | - |
0.2069 | 4750 | 0.0679 | - |
0.2090 | 4800 | 0.0049 | - |
0.2112 | 4850 | 0.0281 | - |
0.2134 | 4900 | 0.049 | - |
0.2156 | 4950 | 0.0052 | - |
0.2177 | 5000 | 0.1657 | - |
0.2199 | 5050 | 0.0005 | - |
0.2221 | 5100 | 0.0041 | - |
0.2243 | 5150 | 0.0008 | - |
0.2265 | 5200 | 0.0587 | - |
0.2286 | 5250 | 0.0753 | - |
0.2308 | 5300 | 0.1744 | - |
0.2330 | 5350 | 0.0055 | - |
0.2352 | 5400 | 0.0023 | - |
0.2373 | 5450 | 0.0002 | - |
0.2395 | 5500 | 0.0472 | - |
0.2417 | 5550 | 0.0042 | - |
0.2439 | 5600 | 0.0137 | - |
0.2460 | 5650 | 0.1646 | - |
0.2482 | 5700 | 0.0509 | - |
0.2504 | 5750 | 0.0062 | - |
0.2526 | 5800 | 0.0019 | - |
0.2548 | 5850 | 0.0048 | - |
0.2569 | 5900 | 0.0031 | - |
0.2591 | 5950 | 0.0011 | - |
0.2613 | 6000 | 0.004 | - |
0.2635 | 6050 | 0.0498 | - |
0.2656 | 6100 | 0.0042 | - |
0.2678 | 6150 | 0.0018 | - |
0.2700 | 6200 | 0.0061 | - |
0.2722 | 6250 | 0.1355 | - |
0.2744 | 6300 | 0.0039 | - |
0.2765 | 6350 | 0.0044 | - |
0.2787 | 6400 | 0.001 | - |
0.2809 | 6450 | 0.0011 | - |
0.2831 | 6500 | 0.0302 | - |
0.2852 | 6550 | 0.1502 | - |
0.2874 | 6600 | 0.0029 | - |
0.2896 | 6650 | 0.0016 | - |
0.2918 | 6700 | 0.0232 | - |
0.2940 | 6750 | 0.176 | - |
0.2961 | 6800 | 0.0323 | - |
0.2983 | 6850 | 0.0818 | - |
0.3005 | 6900 | 0.0427 | - |
0.3027 | 6950 | 0.1716 | - |
0.3048 | 7000 | 0.0137 | - |
0.3070 | 7050 | 0.0032 | - |
0.3092 | 7100 | 0.0095 | - |
0.3114 | 7150 | 0.177 | - |
0.3135 | 7200 | 0.0005 | - |
0.3157 | 7250 | 0.0157 | - |
0.3179 | 7300 | 0.0012 | - |
0.3201 | 7350 | 0.0027 | - |
0.3223 | 7400 | 0.1351 | - |
0.3244 | 7450 | 0.0019 | - |
0.3266 | 7500 | 0.0009 | - |
0.3288 | 7550 | 0.2017 | - |
0.3310 | 7600 | 0.0059 | - |
0.3331 | 7650 | 0.0013 | - |
0.3353 | 7700 | 0.0377 | - |
0.3375 | 7750 | 0.0056 | - |
0.3397 | 7800 | 0.0055 | - |
0.3419 | 7850 | 0.0745 | - |
0.3440 | 7900 | 0.0046 | - |
0.3462 | 7950 | 0.002 | - |
0.3484 | 8000 | 0.0355 | - |
0.3506 | 8050 | 0.0004 | - |
0.3527 | 8100 | 0.0004 | - |
0.3549 | 8150 | 0.0072 | - |
0.3571 | 8200 | 0.0013 | - |
0.3593 | 8250 | 0.0032 | - |
0.3615 | 8300 | 0.0006 | - |
0.3636 | 8350 | 0.0095 | - |
0.3658 | 8400 | 0.0006 | - |
0.3680 | 8450 | 0.0005 | - |
0.3702 | 8500 | 0.0004 | - |
0.3723 | 8550 | 0.0019 | - |
0.3745 | 8600 | 0.0002 | - |
0.3767 | 8650 | 0.0015 | - |
0.3789 | 8700 | 0.0117 | - |
0.3810 | 8750 | 0.002 | - |
0.3832 | 8800 | 0.0005 | - |
0.3854 | 8850 | 0.0009 | - |
0.3876 | 8900 | 0.0041 | - |
0.3898 | 8950 | 0.0484 | - |
0.3919 | 9000 | 0.0058 | - |
0.3941 | 9050 | 0.0027 | - |
0.3963 | 9100 | 0.0002 | - |
0.3985 | 9150 | 0.2323 | - |
0.4006 | 9200 | 0.0163 | - |
0.4028 | 9250 | 0.0333 | - |
0.4050 | 9300 | 0.0033 | - |
0.4072 | 9350 | 0.0023 | - |
0.4094 | 9400 | 0.0044 | - |
0.4115 | 9450 | 0.0142 | - |
0.4137 | 9500 | 0.0261 | - |
0.4159 | 9550 | 0.004 | - |
0.4181 | 9600 | 0.027 | - |
0.4202 | 9650 | 0.0104 | - |
0.4224 | 9700 | 0.0005 | - |
0.4246 | 9750 | 0.2452 | - |
0.4268 | 9800 | 0.0069 | - |
0.4290 | 9850 | 0.0245 | - |
0.4311 | 9900 | 0.0005 | - |
0.4333 | 9950 | 0.0041 | - |
0.4355 | 10000 | 0.1058 | - |
0.4377 | 10050 | 0.0009 | - |
0.4398 | 10100 | 0.0067 | - |
0.4420 | 10150 | 0.0832 | - |
0.4442 | 10200 | 0.0016 | - |
0.4464 | 10250 | 0.039 | - |
0.4485 | 10300 | 0.0078 | - |
0.4507 | 10350 | 0.0013 | - |
0.4529 | 10400 | 0.0003 | - |
0.4551 | 10450 | 0.0259 | - |
0.4573 | 10500 | 0.008 | - |
0.4594 | 10550 | 0.2137 | - |
0.4616 | 10600 | 0.0083 | - |
0.4638 | 10650 | 0.0206 | - |
0.4660 | 10700 | 0.0039 | - |
0.4681 | 10750 | 0.2205 | - |
0.4703 | 10800 | 0.0072 | - |
0.4725 | 10850 | 0.0436 | - |
0.4747 | 10900 | 0.071 | - |
0.4769 | 10950 | 0.0004 | - |
0.4790 | 11000 | 0.0147 | - |
0.4812 | 11050 | 0.0095 | - |
0.4834 | 11100 | 0.0069 | - |
0.4856 | 11150 | 0.0027 | - |
0.4877 | 11200 | 0.0151 | - |
0.4899 | 11250 | 0.0076 | - |
0.4921 | 11300 | 0.0016 | - |
0.4943 | 11350 | 0.1457 | - |
0.4965 | 11400 | 0.1454 | - |
0.4986 | 11450 | 0.0013 | - |
0.5008 | 11500 | 0.0027 | - |
0.5030 | 11550 | 0.0583 | - |
0.5052 | 11600 | 0.0029 | - |
0.5073 | 11650 | 0.0139 | - |
0.5095 | 11700 | 0.0004 | - |
0.5117 | 11750 | 0.0098 | - |
0.5139 | 11800 | 0.0009 | - |
0.5160 | 11850 | 0.0003 | - |
0.5182 | 11900 | 0.0009 | - |
0.5204 | 11950 | 0.0088 | - |
0.5226 | 12000 | 0.0006 | - |
0.5248 | 12050 | 0.0014 | - |
0.5269 | 12100 | 0.0008 | - |
0.5291 | 12150 | 0.0008 | - |
0.5313 | 12200 | 0.0008 | - |
0.5335 | 12250 | 0.0005 | - |
0.5356 | 12300 | 0.0028 | - |
0.5378 | 12350 | 0.0011 | - |
0.5400 | 12400 | 0.0136 | - |
0.5422 | 12450 | 0.0318 | - |
0.5444 | 12500 | 0.0037 | - |
0.5465 | 12550 | 0.0029 | - |
0.5487 | 12600 | 0.0073 | - |
0.5509 | 12650 | 0.0099 | - |
0.5531 | 12700 | 0.015 | - |
0.5552 | 12750 | 0.0047 | - |
0.5574 | 12800 | 0.0891 | - |
0.5596 | 12850 | 0.0007 | - |
0.5618 | 12900 | 0.0784 | - |
0.5640 | 12950 | 0.0636 | - |
0.5661 | 13000 | 0.0029 | - |
0.5683 | 13050 | 0.0048 | - |
0.5705 | 13100 | 0.0698 | - |
0.5727 | 13150 | 0.0002 | - |
0.5748 | 13200 | 0.0734 | - |
0.5770 | 13250 | 0.0004 | - |
0.5792 | 13300 | 0.0135 | - |
0.5814 | 13350 | 0.0034 | - |
0.5835 | 13400 | 0.0018 | - |
0.5857 | 13450 | 0.0175 | - |
0.5879 | 13500 | 0.0003 | - |
0.5901 | 13550 | 0.0002 | - |
0.5923 | 13600 | 0.0032 | - |
0.5944 | 13650 | 0.0007 | - |
0.5966 | 13700 | 0.0021 | - |
0.5988 | 13750 | 0.0019 | - |
0.6010 | 13800 | 0.0006 | - |
0.6031 | 13850 | 0.0014 | - |
0.6053 | 13900 | 0.0011 | - |
0.6075 | 13950 | 0.2383 | - |
0.6097 | 14000 | 0.0009 | - |
0.6119 | 14050 | 0.0863 | - |
0.6140 | 14100 | 0.0005 | - |
0.6162 | 14150 | 0.0017 | - |
0.6184 | 14200 | 0.0003 | - |
0.6206 | 14250 | 0.0025 | - |
0.6227 | 14300 | 0.0008 | - |
0.6249 | 14350 | 0.0005 | - |
0.6271 | 14400 | 0.0006 | - |
0.6293 | 14450 | 0.0517 | - |
0.6315 | 14500 | 0.0005 | - |
0.6336 | 14550 | 0.0075 | - |
0.6358 | 14600 | 0.0004 | - |
0.6380 | 14650 | 0.0003 | - |
0.6402 | 14700 | 0.0003 | - |
0.6423 | 14750 | 0.0045 | - |
0.6445 | 14800 | 0.0005 | - |
0.6467 | 14850 | 0.0002 | - |
0.6489 | 14900 | 0.0125 | - |
0.6510 | 14950 | 0.0015 | - |
0.6532 | 15000 | 0.0017 | - |
0.6554 | 15050 | 0.0011 | - |
0.6576 | 15100 | 0.0207 | - |
0.6598 | 15150 | 0.0002 | - |
0.6619 | 15200 | 0.0252 | - |
0.6641 | 15250 | 0.0006 | - |
0.6663 | 15300 | 0.0015 | - |
0.6685 | 15350 | 0.0018 | - |
0.6706 | 15400 | 0.0386 | - |
0.6728 | 15450 | 0.0011 | - |
0.6750 | 15500 | 0.0003 | - |
0.6772 | 15550 | 0.0007 | - |
0.6794 | 15600 | 0.0028 | - |
0.6815 | 15650 | 0.0056 | - |
0.6837 | 15700 | 0.0005 | - |
0.6859 | 15750 | 0.0002 | - |
0.6881 | 15800 | 0.0305 | - |
0.6902 | 15850 | 0.0005 | - |
0.6924 | 15900 | 0.0018 | - |
0.6946 | 15950 | 0.0011 | - |
0.6968 | 16000 | 0.0006 | - |
0.6990 | 16050 | 0.0072 | - |
0.7011 | 16100 | 0.0224 | - |
0.7033 | 16150 | 0.0011 | - |
0.7055 | 16200 | 0.0005 | - |
0.7077 | 16250 | 0.0007 | - |
0.7098 | 16300 | 0.0005 | - |
0.7120 | 16350 | 0.0028 | - |
0.7142 | 16400 | 0.0017 | - |
0.7164 | 16450 | 0.2294 | - |
0.7185 | 16500 | 0.0253 | - |
0.7207 | 16550 | 0.0122 | - |
0.7229 | 16600 | 0.0001 | - |
0.7251 | 16650 | 0.0327 | - |
0.7273 | 16700 | 0.0042 | - |
0.7294 | 16750 | 0.0008 | - |
0.7316 | 16800 | 0.0004 | - |
0.7338 | 16850 | 0.0003 | - |
0.7360 | 16900 | 0.0005 | - |
0.7381 | 16950 | 0.0003 | - |
0.7403 | 17000 | 0.0021 | - |
0.7425 | 17050 | 0.2041 | - |
0.7447 | 17100 | 0.0002 | - |
0.7469 | 17150 | 0.0006 | - |
0.7490 | 17200 | 0.0002 | - |
0.7512 | 17250 | 0.0008 | - |
0.7534 | 17300 | 0.068 | - |
0.7556 | 17350 | 0.0016 | - |
0.7577 | 17400 | 0.0006 | - |
0.7599 | 17450 | 0.0005 | - |
0.7621 | 17500 | 0.0011 | - |
0.7643 | 17550 | 0.2192 | - |
0.7665 | 17600 | 0.0006 | - |
0.7686 | 17650 | 0.0003 | - |
0.7708 | 17700 | 0.0017 | - |
0.7730 | 17750 | 0.0033 | - |
0.7752 | 17800 | 0.0001 | - |
0.7773 | 17850 | 0.0011 | - |
0.7795 | 17900 | 0.0302 | - |
0.7817 | 17950 | 0.0004 | - |
0.7839 | 18000 | 0.2921 | - |
0.7860 | 18050 | 0.0001 | - |
0.7882 | 18100 | 0.006 | - |
0.7904 | 18150 | 0.0164 | - |
0.7926 | 18200 | 0.0003 | - |
0.7948 | 18250 | 0.0021 | - |
0.7969 | 18300 | 0.0094 | - |
0.7991 | 18350 | 0.002 | - |
0.8013 | 18400 | 0.0405 | - |
0.8035 | 18450 | 0.001 | - |
0.8056 | 18500 | 0.2594 | - |
0.8078 | 18550 | 0.0075 | - |
0.8100 | 18600 | 0.0003 | - |
0.8122 | 18650 | 0.0009 | - |
0.8144 | 18700 | 0.0018 | - |
0.8165 | 18750 | 0.0007 | - |
0.8187 | 18800 | 0.0006 | - |
0.8209 | 18850 | 0.0009 | - |
0.8231 | 18900 | 0.0003 | - |
0.8252 | 18950 | 0.0006 | - |
0.8274 | 19000 | 0.0002 | - |
0.8296 | 19050 | 0.0004 | - |
0.8318 | 19100 | 0.0018 | - |
0.8340 | 19150 | 0.0007 | - |
0.8361 | 19200 | 0.0005 | - |
0.8383 | 19250 | 0.0206 | - |
0.8405 | 19300 | 0.0005 | - |
0.8427 | 19350 | 0.1918 | - |
0.8448 | 19400 | 0.0093 | - |
0.8470 | 19450 | 0.0032 | - |
0.8492 | 19500 | 0.0004 | - |
0.8514 | 19550 | 0.1727 | - |
0.8535 | 19600 | 0.2034 | - |
0.8557 | 19650 | 0.0007 | - |
0.8579 | 19700 | 0.0004 | - |
0.8601 | 19750 | 0.0001 | - |
0.8623 | 19800 | 0.0024 | - |
0.8644 | 19850 | 0.0122 | - |
0.8666 | 19900 | 0.0003 | - |
0.8688 | 19950 | 0.0093 | - |
0.8710 | 20000 | 0.0003 | - |
0.8731 | 20050 | 0.0007 | - |
0.8753 | 20100 | 0.0044 | - |
0.8775 | 20150 | 0.0006 | - |
0.8797 | 20200 | 0.0002 | - |
0.8819 | 20250 | 0.0003 | - |
0.8840 | 20300 | 0.0024 | - |
0.8862 | 20350 | 0.0051 | - |
0.8884 | 20400 | 0.0767 | - |
0.8906 | 20450 | 0.0004 | - |
0.8927 | 20500 | 0.0002 | - |
0.8949 | 20550 | 0.0007 | - |
0.8971 | 20600 | 0.0012 | - |
0.8993 | 20650 | 0.0004 | - |
0.9015 | 20700 | 0.0003 | - |
0.9036 | 20750 | 0.0002 | - |
0.9058 | 20800 | 0.0005 | - |
0.9080 | 20850 | 0.0007 | - |
0.9102 | 20900 | 0.0006 | - |
0.9123 | 20950 | 0.2469 | - |
0.9145 | 21000 | 0.0002 | - |
0.9167 | 21050 | 0.0009 | - |
0.9189 | 21100 | 0.002 | - |
0.9210 | 21150 | 0.0027 | - |
0.9232 | 21200 | 0.0007 | - |
0.9254 | 21250 | 0.0008 | - |
0.9276 | 21300 | 0.0265 | - |
0.9298 | 21350 | 0.0019 | - |
0.9319 | 21400 | 0.0003 | - |
0.9341 | 21450 | 0.0064 | - |
0.9363 | 21500 | 0.0003 | - |
0.9385 | 21550 | 0.0015 | - |
0.9406 | 21600 | 0.0002 | - |
0.9428 | 21650 | 0.0015 | - |
0.9450 | 21700 | 0.1497 | - |
0.9472 | 21750 | 0.1422 | - |
0.9494 | 21800 | 0.0001 | - |
0.9515 | 21850 | 0.0007 | - |
0.9537 | 21900 | 0.0053 | - |
0.9559 | 21950 | 0.0002 | - |
0.9581 | 22000 | 0.0003 | - |
0.9602 | 22050 | 0.1234 | - |
0.9624 | 22100 | 0.2087 | - |
0.9646 | 22150 | 0.0005 | - |
0.9668 | 22200 | 0.0001 | - |
0.9690 | 22250 | 0.0003 | - |
0.9711 | 22300 | 0.0004 | - |
0.9733 | 22350 | 0.0014 | - |
0.9755 | 22400 | 0.0021 | - |
0.9777 | 22450 | 0.0105 | - |
0.9798 | 22500 | 0.0009 | - |
0.9820 | 22550 | 0.0003 | - |
0.9842 | 22600 | 0.0006 | - |
0.9864 | 22650 | 0.0007 | - |
0.9885 | 22700 | 0.0021 | - |
0.9907 | 22750 | 0.003 | - |
0.9929 | 22800 | 0.0099 | - |
0.9951 | 22850 | 0.001 | - |
0.9973 | 22900 | 0.0521 | - |
0.9994 | 22950 | 0.0003 | - |
Framework Versions
- Python: 3.10.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.2
- PyTorch: 2.0.1
- Datasets: 2.16.1
- Tokenizers: 0.15.0
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}
}