SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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 Sources
Model Labels
Label |
Examples |
left |
- 'Tennessee has an annual sales tax-free holiday weekend that\xa0begins\xa0on the last Friday of July.\xa0'
- 'In what could be construed as an act of treason,\xa0President Trump recently ordered such\xa0paramilitary groups and right-wing thugs\xa0to take up arms and to threaten Democratic-led state governments such as Michigan's in order to force them to "reopen" their state.'
- 'Trump, not surprisingly, used the speech as an opportunity to attack former President Barack Obama, claiming that he did nothing to promote criminal justice reform when he was in office.\xa0'
|
right |
- 'In the Joe Biden-Bernie Sanders “Unity” platform, Democrats are vowing to provide free, American taxpayer-funded health care to illegal aliens who are able to enroll in former President Obama’s Deferred Action for Childhood Arrivals (DACA) program.'
- 'The new numbers from Gallup are an unwelcome sight for Democrats after kicking off the week with a disaster caucus in Iowa who and simultaneously anticipating a Trump acquittal in the Senate. Trump will also now have the opportunity to shine in his newfound approval in Tuesday night’s address to the nation while Democrats are in disarray.'
- 'Though Trump has successfully increased wages by four percent over the last 12 months for America’s blue collar and working class by decreasing foreign competition through a crackdown on illegal immigration, experts have warned that those wage hikes will not continue heading into the 2020 election should current illegal immigration levels keep rising at record levels.'
|
center |
- 'LeBron James shares thoughts on his Los Angeles house getting vandalized pic twitter com 4RFLK42xhu'
- 'O’Rourke, a native of the U.S.-Mexican border town El Paso, has blasted Trump’s use of tariffs as a “huge mistake” and has vowed to suspend them on his first day in office.'
- 'Here are a few people we will be reminding you about in every article that pertains to a film they re tied to '
|
Evaluation
Metrics
Label |
Accuracy |
Precision |
Recall |
F1 |
all |
0.7010 |
0.7024 |
0.7010 |
0.7016 |
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
model = SetFitModel.from_pretrained("JordanTallon/Unifeed")
preds = model("A Black man, Floyd died in police custody May 25 after a Minneapolis cop kneeled on his neck for more than eight minutes.")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
9 |
32.9560 |
90 |
Label |
Training Sample Count |
center |
777 |
left |
780 |
right |
808 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (200, 200)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- 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: True
- warmup_proportion: 0.1
- seed: 326
- run_name: unifeed_bias_training
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch |
Step |
Training Loss |
Validation Loss |
0.0002 |
1 |
0.2486 |
- |
1.0 |
4878 |
0.0092 |
0.308 |
2.0 |
9756 |
0.0004 |
0.3228 |
3.0 |
14634 |
0.0002 |
0.3326 |
4.0 |
19512 |
0.0002 |
0.3191 |
5.0 |
24390 |
0.0001 |
0.3279 |
6.0 |
29268 |
0.0001 |
0.3384 |
7.0 |
34146 |
0.0001 |
0.3311 |
8.0 |
39024 |
0.0001 |
0.3316 |
0.0068 |
1 |
0.0007 |
- |
1.0 |
148 |
0.0006 |
0.3042 |
2.0 |
296 |
0.0006 |
0.3352 |
3.0 |
444 |
0.0382 |
0.3059 |
4.0 |
592 |
0.0022 |
0.3055 |
5.0 |
740 |
0.0044 |
0.3034 |
6.0 |
888 |
0.0006 |
0.3185 |
7.0 |
1036 |
0.0005 |
0.3066 |
8.0 |
1184 |
0.0008 |
0.3196 |
9.0 |
1332 |
0.0004 |
0.326 |
10.0 |
1480 |
0.0004 |
0.352 |
11.0 |
1628 |
0.0005 |
0.3122 |
12.0 |
1776 |
0.0003 |
0.3268 |
13.0 |
1924 |
0.0004 |
0.2928 |
14.0 |
2072 |
0.0004 |
0.3148 |
15.0 |
2220 |
0.0003 |
0.3153 |
16.0 |
2368 |
0.0004 |
0.3385 |
17.0 |
2516 |
0.0004 |
0.3107 |
18.0 |
2664 |
0.0004 |
0.3225 |
19.0 |
2812 |
0.0003 |
0.3073 |
20.0 |
2960 |
0.0003 |
0.316 |
21.0 |
3108 |
0.0003 |
0.3053 |
22.0 |
3256 |
0.0004 |
0.3227 |
23.0 |
3404 |
0.0004 |
0.3099 |
24.0 |
3552 |
0.0003 |
0.3043 |
25.0 |
3700 |
0.0003 |
0.3316 |
0.0034 |
1 |
0.0004 |
- |
1.0 |
296 |
0.0003 |
0.3321 |
2.0 |
592 |
0.0016 |
0.3202 |
3.0 |
888 |
0.0005 |
0.3376 |
4.0 |
1184 |
0.0004 |
0.3167 |
5.0 |
1480 |
0.0003 |
0.3342 |
6.0 |
1776 |
0.0003 |
0.3183 |
7.0 |
2072 |
0.0003 |
0.3086 |
8.0 |
2368 |
0.0003 |
0.312 |
9.0 |
2664 |
0.0003 |
0.3169 |
10.0 |
2960 |
0.0003 |
0.3317 |
11.0 |
3256 |
0.0004 |
0.3126 |
12.0 |
3552 |
0.0003 |
0.3003 |
13.0 |
3848 |
0.0003 |
0.3119 |
14.0 |
4144 |
0.0003 |
0.316 |
15.0 |
4440 |
0.0002 |
0.3183 |
16.0 |
4736 |
0.0003 |
0.313 |
17.0 |
5032 |
0.0003 |
0.3187 |
18.0 |
5328 |
0.0002 |
0.3295 |
19.0 |
5624 |
0.0002 |
0.3487 |
20.0 |
5920 |
0.0003 |
0.3458 |
21.0 |
6216 |
0.0002 |
0.331 |
22.0 |
6512 |
0.0002 |
0.3499 |
23.0 |
6808 |
0.0003 |
0.3296 |
24.0 |
7104 |
0.0003 |
0.3097 |
25.0 |
7400 |
0.0003 |
0.3197 |
0.0068 |
1 |
0.0003 |
- |
1.0 |
148 |
0.0003 |
0.3219 |
2.0 |
296 |
0.0003 |
0.3185 |
3.0 |
444 |
0.0003 |
0.3114 |
4.0 |
592 |
0.0003 |
0.2989 |
5.0 |
740 |
0.0003 |
0.335 |
6.0 |
888 |
0.0004 |
0.3132 |
7.0 |
1036 |
0.0003 |
0.3264 |
8.0 |
1184 |
0.0004 |
0.3461 |
9.0 |
1332 |
0.0002 |
0.3185 |
10.0 |
1480 |
0.0002 |
0.3336 |
11.0 |
1628 |
0.0003 |
0.3282 |
12.0 |
1776 |
0.0003 |
0.3206 |
13.0 |
1924 |
0.0002 |
0.3303 |
14.0 |
2072 |
0.0002 |
0.3362 |
15.0 |
2220 |
0.0002 |
0.3382 |
16.0 |
2368 |
0.0002 |
0.3241 |
17.0 |
2516 |
0.0002 |
0.3303 |
18.0 |
2664 |
0.0002 |
0.3301 |
19.0 |
2812 |
0.0002 |
0.319 |
20.0 |
2960 |
0.0002 |
0.3304 |
21.0 |
3108 |
0.0002 |
0.3379 |
22.0 |
3256 |
0.0002 |
0.3424 |
23.0 |
3404 |
0.0002 |
0.3273 |
24.0 |
3552 |
0.0002 |
0.3213 |
25.0 |
3700 |
0.0002 |
0.3191 |
0.0068 |
1 |
0.0003 |
- |
1.0 |
148 |
0.0003 |
0.3245 |
2.0 |
296 |
0.0002 |
0.3148 |
3.0 |
444 |
0.0002 |
0.3174 |
4.0 |
592 |
0.0003 |
0.3242 |
5.0 |
740 |
0.0003 |
0.3352 |
6.0 |
888 |
0.0003 |
0.3112 |
7.0 |
1036 |
0.0003 |
0.3204 |
8.0 |
1184 |
0.0003 |
0.3734 |
9.0 |
1332 |
0.0002 |
0.3383 |
10.0 |
1480 |
0.0003 |
0.3272 |
11.0 |
1628 |
0.0002 |
0.3106 |
12.0 |
1776 |
0.0003 |
0.3307 |
13.0 |
1924 |
0.0003 |
0.3359 |
14.0 |
2072 |
0.0002 |
0.3264 |
15.0 |
2220 |
0.0002 |
0.3254 |
16.0 |
2368 |
0.0002 |
0.3349 |
17.0 |
2516 |
0.0132 |
0.3399 |
18.0 |
2664 |
0.0002 |
0.343 |
19.0 |
2812 |
0.0002 |
0.3306 |
20.0 |
2960 |
0.0002 |
0.3472 |
21.0 |
3108 |
0.0002 |
0.3234 |
22.0 |
3256 |
0.002 |
0.3281 |
23.0 |
3404 |
0.0002 |
0.3289 |
24.0 |
3552 |
0.0002 |
0.2974 |
25.0 |
3700 |
0.0002 |
0.3153 |
26.0 |
3848 |
0.0002 |
0.3273 |
27.0 |
3996 |
0.0002 |
0.313 |
28.0 |
4144 |
0.0002 |
0.3303 |
29.0 |
4292 |
0.0002 |
0.3106 |
30.0 |
4440 |
0.0002 |
0.3155 |
31.0 |
4588 |
0.0002 |
0.3553 |
32.0 |
4736 |
0.0002 |
0.3039 |
33.0 |
4884 |
0.0001 |
0.3133 |
34.0 |
5032 |
0.0002 |
0.3323 |
35.0 |
5180 |
0.0002 |
0.3264 |
36.0 |
5328 |
0.0002 |
0.3133 |
37.0 |
5476 |
0.0002 |
0.3308 |
38.0 |
5624 |
0.0002 |
0.3137 |
39.0 |
5772 |
0.0002 |
0.3062 |
40.0 |
5920 |
0.0002 |
0.3438 |
41.0 |
6068 |
0.0002 |
0.3426 |
42.0 |
6216 |
0.0002 |
0.326 |
43.0 |
6364 |
0.0002 |
0.322 |
44.0 |
6512 |
0.0002 |
0.3202 |
45.0 |
6660 |
0.0002 |
0.3253 |
46.0 |
6808 |
0.0002 |
0.3272 |
47.0 |
6956 |
0.0002 |
0.3258 |
48.0 |
7104 |
0.0002 |
0.3252 |
49.0 |
7252 |
0.0002 |
0.3233 |
50.0 |
7400 |
0.0002 |
0.3234 |
0.0135 |
1 |
0.0002 |
- |
1.0 |
74 |
0.0002 |
- |
0.0068 |
1 |
0.0002 |
- |
1.0 |
148 |
0.0002 |
0.3036 |
2.0 |
296 |
0.0002 |
0.3555 |
3.0 |
444 |
0.0002 |
0.3331 |
4.0 |
592 |
0.0002 |
0.3086 |
5.0 |
740 |
0.0002 |
0.3036 |
6.0 |
888 |
0.0002 |
0.3217 |
7.0 |
1036 |
0.0002 |
0.3416 |
8.0 |
1184 |
0.0002 |
0.3309 |
9.0 |
1332 |
0.0002 |
0.3424 |
10.0 |
1480 |
0.0003 |
0.3655 |
11.0 |
1628 |
0.0002 |
0.3042 |
12.0 |
1776 |
0.0019 |
0.326 |
13.0 |
1924 |
0.0002 |
0.3161 |
14.0 |
2072 |
0.0002 |
0.3286 |
15.0 |
2220 |
0.0002 |
0.3563 |
16.0 |
2368 |
0.0002 |
0.326 |
17.0 |
2516 |
0.0002 |
0.3114 |
18.0 |
2664 |
0.0002 |
0.3366 |
19.0 |
2812 |
0.0002 |
0.329 |
20.0 |
2960 |
0.0002 |
0.3217 |
21.0 |
3108 |
0.0002 |
0.325 |
22.0 |
3256 |
0.0002 |
0.3243 |
23.0 |
3404 |
0.0002 |
0.3341 |
24.0 |
3552 |
0.0002 |
0.3237 |
25.0 |
3700 |
0.0002 |
0.3433 |
26.0 |
3848 |
0.0002 |
0.3196 |
27.0 |
3996 |
0.0001 |
0.3372 |
28.0 |
4144 |
0.0001 |
0.3191 |
29.0 |
4292 |
0.0001 |
0.328 |
30.0 |
4440 |
0.0002 |
0.3416 |
31.0 |
4588 |
0.0002 |
0.3132 |
32.0 |
4736 |
0.0002 |
0.3429 |
33.0 |
4884 |
0.0002 |
0.336 |
34.0 |
5032 |
0.0002 |
0.3507 |
35.0 |
5180 |
0.0001 |
0.3483 |
36.0 |
5328 |
0.0002 |
0.3325 |
37.0 |
5476 |
0.0001 |
0.3406 |
38.0 |
5624 |
0.0003 |
0.3538 |
39.0 |
5772 |
0.0002 |
0.3422 |
40.0 |
5920 |
0.0002 |
0.3359 |
41.0 |
6068 |
0.0002 |
0.3252 |
42.0 |
6216 |
0.0002 |
0.326 |
43.0 |
6364 |
0.0002 |
0.3613 |
44.0 |
6512 |
0.0001 |
0.332 |
45.0 |
6660 |
0.0002 |
0.3295 |
46.0 |
6808 |
0.0002 |
0.3265 |
47.0 |
6956 |
0.0002 |
0.2982 |
48.0 |
7104 |
0.0002 |
0.3017 |
49.0 |
7252 |
0.0001 |
0.309 |
50.0 |
7400 |
0.0001 |
0.3199 |
51.0 |
7548 |
0.0001 |
0.325 |
52.0 |
7696 |
0.0002 |
0.3222 |
53.0 |
7844 |
0.0001 |
0.3189 |
54.0 |
7992 |
0.0001 |
0.3329 |
55.0 |
8140 |
0.0001 |
0.3272 |
56.0 |
8288 |
0.0001 |
0.3292 |
57.0 |
8436 |
0.0001 |
0.3283 |
58.0 |
8584 |
0.0001 |
0.3301 |
59.0 |
8732 |
0.0001 |
0.3334 |
60.0 |
8880 |
0.0001 |
0.3144 |
61.0 |
9028 |
0.0002 |
0.3487 |
62.0 |
9176 |
0.0002 |
0.3602 |
63.0 |
9324 |
0.0001 |
0.3056 |
64.0 |
9472 |
0.0001 |
0.3415 |
65.0 |
9620 |
0.0002 |
0.3299 |
66.0 |
9768 |
0.0001 |
0.3254 |
67.0 |
9916 |
0.0001 |
0.3396 |
68.0 |
10064 |
0.0001 |
0.3501 |
69.0 |
10212 |
0.0001 |
0.3275 |
70.0 |
10360 |
0.0001 |
0.34 |
71.0 |
10508 |
0.0001 |
0.3351 |
72.0 |
10656 |
0.0001 |
0.3367 |
73.0 |
10804 |
0.0001 |
0.3548 |
74.0 |
10952 |
0.0001 |
0.33 |
75.0 |
11100 |
0.0001 |
0.3259 |
76.0 |
11248 |
0.0002 |
0.3283 |
77.0 |
11396 |
0.0001 |
0.3214 |
78.0 |
11544 |
0.0001 |
0.324 |
79.0 |
11692 |
0.0001 |
0.3247 |
80.0 |
11840 |
0.0001 |
0.3347 |
81.0 |
11988 |
0.0001 |
0.3292 |
82.0 |
12136 |
0.0002 |
0.3568 |
83.0 |
12284 |
0.0001 |
0.324 |
84.0 |
12432 |
0.0001 |
0.3245 |
85.0 |
12580 |
0.0001 |
0.3368 |
86.0 |
12728 |
0.0001 |
0.3372 |
87.0 |
12876 |
0.0001 |
0.3432 |
88.0 |
13024 |
0.0001 |
0.3048 |
89.0 |
13172 |
0.0001 |
0.3395 |
90.0 |
13320 |
0.0001 |
0.3204 |
91.0 |
13468 |
0.0001 |
0.3122 |
92.0 |
13616 |
0.0001 |
0.3372 |
93.0 |
13764 |
0.0001 |
0.3306 |
94.0 |
13912 |
0.0001 |
0.3362 |
95.0 |
14060 |
0.0001 |
0.3386 |
96.0 |
14208 |
0.0001 |
0.3198 |
97.0 |
14356 |
0.0001 |
0.3176 |
98.0 |
14504 |
0.0001 |
0.3604 |
99.0 |
14652 |
0.0001 |
0.3507 |
100.0 |
14800 |
0.0001 |
0.3272 |
0.0023 |
1 |
0.0001 |
- |
1.0 |
444 |
0.0002 |
0.3295 |
2.0 |
888 |
0.0001 |
0.3144 |
3.0 |
1332 |
0.0001 |
0.3213 |
4.0 |
1776 |
0.0001 |
0.3362 |
5.0 |
2220 |
0.0001 |
0.3398 |
6.0 |
2664 |
0.0001 |
0.3385 |
7.0 |
3108 |
0.0002 |
0.3406 |
8.0 |
3552 |
0.0001 |
0.3253 |
9.0 |
3996 |
0.0001 |
0.3253 |
10.0 |
4440 |
0.0001 |
0.3119 |
11.0 |
4884 |
0.0001 |
0.3204 |
12.0 |
5328 |
0.0001 |
0.3387 |
13.0 |
5772 |
0.0001 |
0.3387 |
14.0 |
6216 |
0.0001 |
0.3584 |
15.0 |
6660 |
0.0001 |
0.3548 |
16.0 |
7104 |
0.0001 |
0.3314 |
17.0 |
7548 |
0.0001 |
0.3335 |
18.0 |
7992 |
0.0001 |
0.3325 |
19.0 |
8436 |
0.0001 |
0.3545 |
20.0 |
8880 |
0.0001 |
0.3456 |
21.0 |
9324 |
0.0001 |
0.3532 |
22.0 |
9768 |
0.0001 |
0.3524 |
23.0 |
10212 |
0.0001 |
0.352 |
24.0 |
10656 |
0.0001 |
0.3502 |
25.0 |
11100 |
0.0 |
0.3275 |
0.0034 |
1 |
0.0001 |
- |
1.0 |
296 |
0.0001 |
0.3209 |
2.0 |
592 |
0.0001 |
0.3265 |
3.0 |
888 |
0.0001 |
0.3414 |
4.0 |
1184 |
0.0001 |
0.3314 |
5.0 |
1480 |
0.0002 |
0.3498 |
6.0 |
1776 |
0.0001 |
0.337 |
7.0 |
2072 |
0.0001 |
0.3347 |
8.0 |
2368 |
0.0001 |
0.3494 |
9.0 |
2664 |
0.0001 |
0.3326 |
10.0 |
2960 |
0.0001 |
0.3259 |
11.0 |
3256 |
0.0002 |
0.3443 |
12.0 |
3552 |
0.0001 |
0.3431 |
13.0 |
3848 |
0.0001 |
0.324 |
14.0 |
4144 |
0.0001 |
0.3339 |
15.0 |
4440 |
0.0001 |
0.3255 |
16.0 |
4736 |
0.0001 |
0.3379 |
17.0 |
5032 |
0.0001 |
0.3285 |
18.0 |
5328 |
0.0001 |
0.3362 |
19.0 |
5624 |
0.0001 |
0.3319 |
20.0 |
5920 |
0.0001 |
0.3456 |
21.0 |
6216 |
0.0001 |
0.329 |
22.0 |
6512 |
0.0001 |
0.3386 |
23.0 |
6808 |
0.0001 |
0.3278 |
24.0 |
7104 |
0.0001 |
0.3078 |
25.0 |
7400 |
0.0001 |
0.3155 |
0.0068 |
1 |
0.0001 |
- |
1.0 |
148 |
0.0001 |
0.3225 |
2.0 |
296 |
0.0001 |
0.3526 |
3.0 |
444 |
0.0001 |
0.3265 |
4.0 |
592 |
0.0001 |
0.3206 |
5.0 |
740 |
0.0001 |
0.3126 |
6.0 |
888 |
0.0001 |
0.3306 |
7.0 |
1036 |
0.0001 |
0.3189 |
8.0 |
1184 |
0.0001 |
0.3246 |
9.0 |
1332 |
0.0001 |
0.3346 |
10.0 |
1480 |
0.0001 |
0.3528 |
11.0 |
1628 |
0.0001 |
0.3204 |
12.0 |
1776 |
0.0001 |
0.34 |
13.0 |
1924 |
0.0001 |
0.3291 |
14.0 |
2072 |
0.0001 |
0.3444 |
15.0 |
2220 |
0.0001 |
0.339 |
16.0 |
2368 |
0.0001 |
0.3533 |
17.0 |
2516 |
0.0001 |
0.3288 |
18.0 |
2664 |
0.0001 |
0.3475 |
19.0 |
2812 |
0.0001 |
0.3464 |
20.0 |
2960 |
0.0001 |
0.3351 |
21.0 |
3108 |
0.0001 |
0.3421 |
22.0 |
3256 |
0.0001 |
0.3351 |
23.0 |
3404 |
0.0001 |
0.3416 |
24.0 |
3552 |
0.0001 |
0.3414 |
25.0 |
3700 |
0.0001 |
0.3433 |
26.0 |
3848 |
0.0001 |
0.3339 |
27.0 |
3996 |
0.0001 |
0.35 |
28.0 |
4144 |
0.0001 |
0.3215 |
29.0 |
4292 |
0.0001 |
0.3278 |
30.0 |
4440 |
0.0001 |
0.3508 |
31.0 |
4588 |
0.0001 |
0.3356 |
32.0 |
4736 |
0.0001 |
0.3617 |
33.0 |
4884 |
0.0001 |
0.3368 |
34.0 |
5032 |
0.0001 |
0.3551 |
35.0 |
5180 |
0.0001 |
0.3582 |
36.0 |
5328 |
0.0001 |
0.333 |
37.0 |
5476 |
0.0 |
0.3461 |
38.0 |
5624 |
0.0001 |
0.3515 |
39.0 |
5772 |
0.0001 |
0.3601 |
40.0 |
5920 |
0.0001 |
0.347 |
41.0 |
6068 |
0.0001 |
0.3444 |
42.0 |
6216 |
0.0 |
0.3609 |
43.0 |
6364 |
0.0 |
0.3432 |
44.0 |
6512 |
0.0 |
0.3526 |
45.0 |
6660 |
0.0 |
0.3382 |
46.0 |
6808 |
0.0 |
0.353 |
47.0 |
6956 |
0.0001 |
0.3374 |
48.0 |
7104 |
0.0001 |
0.327 |
49.0 |
7252 |
0.0001 |
0.3202 |
50.0 |
7400 |
0.0 |
0.3386 |
51.0 |
7548 |
0.0001 |
0.3501 |
52.0 |
7696 |
0.0002 |
0.3341 |
53.0 |
7844 |
0.0001 |
0.3024 |
54.0 |
7992 |
0.0001 |
0.3456 |
55.0 |
8140 |
0.0 |
0.3323 |
56.0 |
8288 |
0.0 |
0.3259 |
57.0 |
8436 |
0.0 |
0.3246 |
58.0 |
8584 |
0.0 |
0.3341 |
59.0 |
8732 |
0.0 |
0.3347 |
60.0 |
8880 |
0.0 |
0.322 |
61.0 |
9028 |
0.0001 |
0.3323 |
62.0 |
9176 |
0.0 |
0.3471 |
63.0 |
9324 |
0.0001 |
0.2913 |
64.0 |
9472 |
0.0 |
0.3144 |
65.0 |
9620 |
0.0001 |
0.3184 |
66.0 |
9768 |
0.0 |
0.3251 |
67.0 |
9916 |
0.0001 |
0.3342 |
68.0 |
10064 |
0.0 |
0.3486 |
69.0 |
10212 |
0.0 |
0.3381 |
70.0 |
10360 |
0.0 |
0.3161 |
71.0 |
10508 |
0.0 |
0.3036 |
72.0 |
10656 |
0.0 |
0.3141 |
73.0 |
10804 |
0.0 |
0.3307 |
74.0 |
10952 |
0.0 |
0.3153 |
75.0 |
11100 |
0.0 |
0.3016 |
76.0 |
11248 |
0.0001 |
0.3321 |
77.0 |
11396 |
0.0001 |
0.3194 |
78.0 |
11544 |
0.0001 |
0.3496 |
79.0 |
11692 |
0.0 |
0.3218 |
80.0 |
11840 |
0.0 |
0.3251 |
81.0 |
11988 |
0.0 |
0.3468 |
82.0 |
12136 |
0.0 |
0.3803 |
83.0 |
12284 |
0.0 |
0.3354 |
84.0 |
12432 |
0.0 |
0.351 |
85.0 |
12580 |
0.0 |
0.3231 |
86.0 |
12728 |
0.0 |
0.3027 |
87.0 |
12876 |
0.0 |
0.3309 |
88.0 |
13024 |
0.0 |
0.3194 |
89.0 |
13172 |
0.0 |
0.3611 |
90.0 |
13320 |
0.0 |
0.3288 |
91.0 |
13468 |
0.0 |
0.3261 |
92.0 |
13616 |
0.0 |
0.3268 |
93.0 |
13764 |
0.0 |
0.3433 |
94.0 |
13912 |
0.0 |
0.3438 |
95.0 |
14060 |
0.0 |
0.3288 |
96.0 |
14208 |
0.0 |
0.3263 |
97.0 |
14356 |
0.0 |
0.3331 |
98.0 |
14504 |
0.0 |
0.3334 |
99.0 |
14652 |
0.0 |
0.319 |
100.0 |
14800 |
0.0 |
0.3033 |
101.0 |
14948 |
0.0001 |
0.3051 |
102.0 |
15096 |
0.0 |
0.3321 |
103.0 |
15244 |
0.0 |
0.3181 |
104.0 |
15392 |
0.0 |
0.2943 |
105.0 |
15540 |
0.0 |
0.3137 |
106.0 |
15688 |
0.0 |
0.3111 |
107.0 |
15836 |
0.0 |
0.2968 |
108.0 |
15984 |
0.0 |
0.3072 |
109.0 |
16132 |
0.0 |
0.3154 |
110.0 |
16280 |
0.0001 |
0.3211 |
111.0 |
16428 |
0.0 |
0.2974 |
112.0 |
16576 |
0.0 |
0.3057 |
113.0 |
16724 |
0.0 |
0.296 |
114.0 |
16872 |
0.0 |
0.3104 |
115.0 |
17020 |
0.0 |
0.3029 |
116.0 |
17168 |
0.0 |
0.329 |
117.0 |
17316 |
0.0 |
0.3275 |
118.0 |
17464 |
0.0 |
0.3343 |
119.0 |
17612 |
0.0 |
0.3168 |
120.0 |
17760 |
0.0 |
0.3208 |
121.0 |
17908 |
0.0 |
0.2973 |
122.0 |
18056 |
0.0 |
0.3121 |
123.0 |
18204 |
0.0 |
0.3049 |
124.0 |
18352 |
0.0 |
0.3079 |
125.0 |
18500 |
0.0 |
0.2994 |
126.0 |
18648 |
0.0 |
0.3189 |
127.0 |
18796 |
0.0 |
0.3255 |
128.0 |
18944 |
0.0 |
0.3111 |
129.0 |
19092 |
0.0 |
0.3182 |
130.0 |
19240 |
0.0 |
0.356 |
131.0 |
19388 |
0.0 |
0.3299 |
132.0 |
19536 |
0.0 |
0.3308 |
133.0 |
19684 |
0.0 |
0.3379 |
134.0 |
19832 |
0.0 |
0.3233 |
135.0 |
19980 |
0.0 |
0.327 |
136.0 |
20128 |
0.0 |
0.318 |
137.0 |
20276 |
0.0 |
0.2937 |
138.0 |
20424 |
0.0 |
0.3039 |
139.0 |
20572 |
0.0 |
0.3367 |
140.0 |
20720 |
0.0 |
0.3185 |
141.0 |
20868 |
0.0 |
0.3441 |
142.0 |
21016 |
0.0 |
0.3055 |
143.0 |
21164 |
0.0 |
0.3202 |
144.0 |
21312 |
0.0 |
0.3144 |
145.0 |
21460 |
0.0 |
0.3304 |
146.0 |
21608 |
0.0 |
0.3165 |
147.0 |
21756 |
0.0 |
0.309 |
148.0 |
21904 |
0.0 |
0.3086 |
149.0 |
22052 |
0.0 |
0.2987 |
150.0 |
22200 |
0.0 |
0.3198 |
151.0 |
22348 |
0.0 |
0.3372 |
152.0 |
22496 |
0.0 |
0.3156 |
153.0 |
22644 |
0.0 |
0.3206 |
154.0 |
22792 |
0.0 |
0.322 |
155.0 |
22940 |
0.0 |
0.3445 |
156.0 |
23088 |
0.0 |
0.3183 |
157.0 |
23236 |
0.0 |
0.3203 |
158.0 |
23384 |
0.0 |
0.3337 |
159.0 |
23532 |
0.0 |
0.3245 |
160.0 |
23680 |
0.0 |
0.3068 |
161.0 |
23828 |
0.0 |
0.3199 |
162.0 |
23976 |
0.0 |
0.3308 |
163.0 |
24124 |
0.0 |
0.3446 |
164.0 |
24272 |
0.0 |
0.341 |
165.0 |
24420 |
0.0 |
0.3155 |
166.0 |
24568 |
0.0 |
0.3306 |
167.0 |
24716 |
0.0 |
0.3422 |
168.0 |
24864 |
0.0 |
0.336 |
169.0 |
25012 |
0.0 |
0.3271 |
170.0 |
25160 |
0.0 |
0.3062 |
171.0 |
25308 |
0.0 |
0.305 |
172.0 |
25456 |
0.0 |
0.3047 |
173.0 |
25604 |
0.0 |
0.3281 |
174.0 |
25752 |
0.0 |
0.3059 |
175.0 |
25900 |
0.0 |
0.2993 |
176.0 |
26048 |
0.0 |
0.3206 |
177.0 |
26196 |
0.0 |
0.3274 |
178.0 |
26344 |
0.0 |
0.3249 |
179.0 |
26492 |
0.0 |
0.3049 |
180.0 |
26640 |
0.0 |
0.3131 |
181.0 |
26788 |
0.0 |
0.3119 |
182.0 |
26936 |
0.0 |
0.3457 |
183.0 |
27084 |
0.0 |
0.3242 |
184.0 |
27232 |
0.0 |
0.3006 |
185.0 |
27380 |
0.0 |
0.3054 |
186.0 |
27528 |
0.0 |
0.3135 |
187.0 |
27676 |
0.0 |
0.3102 |
188.0 |
27824 |
0.0 |
0.3394 |
189.0 |
27972 |
0.0 |
0.3256 |
190.0 |
28120 |
0.0 |
0.2973 |
191.0 |
28268 |
0.0 |
0.3124 |
192.0 |
28416 |
0.0 |
0.321 |
193.0 |
28564 |
0.0 |
0.3332 |
194.0 |
28712 |
0.0 |
0.3136 |
195.0 |
28860 |
0.0 |
0.32 |
196.0 |
29008 |
0.0 |
0.3486 |
197.0 |
29156 |
0.0 |
0.3259 |
198.0 |
29304 |
0.0 |
0.3134 |
199.0 |
29452 |
0.0 |
0.3437 |
200.0 |
29600 |
0.0 |
0.3029 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- Tokenizers: 0.15.2
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}
}