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kaustubhgap/
kaustubh_setfit

Name: kaustubh_setfit
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-mpnet-base-v2

finetune

model-index

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SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 119 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
79	'peony middle notes' 'lemon middle notes' 'coconut middle notes'
86	'no print/no pattern' 'two tone' 'diagonal stripe'
37	'eel skin leather' 'metal' 'raffia'
82	'collarless' 'peaked lapel' 'front keyhole'
95	'standard toe' 'wide toe' 'extra wide toe'
83	'indoor' 'hike' 'beach'
107	'surplice' 'messenger bag' 'camera bag'
19	'mary jane' 'zip around wallet' 'tongue buckle'
102	'slits at knee' 'slits above hips' 'front slit at hem'
35	'tie' 'gem embellishment' 'caged'
18	'rolo chain' 'cord bracelet' 'figaro'
65	'wheat protein' 'rosemary ingredient' 'pea protein'
68	'bath towel' 'art print' 'reusable bottle'
40	'polyfill' 'silk fill' 'feather fill'
50	'palm grip' 'carpenter hook' 'storm flap'
113	'wide waistband' 'elastic inset' 'belt loops'
75	'glass' 'acrylic' 'opal'
11	'foam cups' 'wire' 'molded cups'
38	'dual layer fabric' '2 way stretch' '4 way stretch'
63	'light support' 'medium supprt' 'high support'
44	'face' 'hand' 'neck/dècolletage'
115	'soy wax' 'paraffin wax'
42	'regular' 'tailored' 'fitted'
97	'king' 'euro' 'standard'
70	'wrist length' 'above thigh' 'below bust'
34	'feminine' 'religious' 'boho'
10	'slim' 'regular'
15	'6-10 oz' '11-20 oz'
77	'rose gold metal' 'gold plated' 'alloy'
43	'contrast inner lining' 'simple seaming' 'princess seams'
7	'neroli base notes' 'amber base notes' 'musk base notes'
17	'spot clean' 'dry clean' 'microwave safe'
8	'nourishing' 'firming' 'soothing/healing'
103	'lugged soles' 'non marking soles'
26	'wall control' 'switch control'
99	'fitted sleeves' 'fitted sleeve' 'structured sleeves'
33	'rim' 'feet' '5 panel construction'
64	'mineral oil free' 'propylene glycol free' 'paraffin free'
96	'double strap' 'spaghetti straps' 'thin straps'
1	'shoulder back' 'full coverage' 'low back'
62	'rustic' 'coastal' 'scandinavian'
39	'metallic' 'swiss dot' 'base layer'
60	'halloween' 'christmas holiday'
92	'seamless' 'mid rise waist seam' 'flat seam'
114	'ultra high rise' 'mid rise' 'high waisted'
105	'top handle' 'detachable straps' 'chain strap'
90	'floral' 'psychedelic print' 'paisley'
91	'night' 'day'
45	'serum formulation' 'cream/creme' 'solid'
59	'strong hold' 'flexible hold'
46	'leather' 'fresh aquatic' 'green aromatic'
21	'matte' 'metallic' 'olive'
69	'cinnamon key notes' 'violet key notes' 'pepper key notes'
101	'dropped shoulder' 'puff shoulder' 'flutter sleeve'
61	'summer' 'everyday' 'indoor'
104	'wedding guest' 'bridal' 'halloween'
32	'indigo wash' 'acid wash' 'stonewash'
51	'still life graphic' 'sports graphic' 'star wars'
48	'beige' 'black' 'rose gold frame'
87	'medium pile' 'low pile'
22	'bright' 'pastel' 'light'
41	'matte finish' 'shiny finish'
93	'no buckle' 'geometric shape' 'straight silhouette'
71	'polarized' 'color tinted' 'mirrored'
2	'split back' 'racer back' 'open back'
89	'round stitch pocket' 'seam pocket' 'kangaroo pocket'
20	'removable hoodie' 'packable hood collar' 'hooded'
52	'thick' 'medium thick'
55	'amber head notes' 'lime head notes' 'musk head notes'
58	'back curved hem' 'twist hem' 'ribbed hem'
118	'light wood' 'medium wood'
25	'gifts for him' 'apres ski' 'cozy'
109	'closed toe' 'square toe' 'round toe'
30	'extended cuffs' 'storm cuffs' 'elastic cuff'
24	'ingrown hairs' 'frizz' 'redness'
9	'high cut' 'string bikini'
94	'opaque' 'sheer'
16	'2 card slot' 'card slots'
78	'gothcore' 'vanilla girl' 'dyed out'
4	'layered' 'bangle' 'cuff'
23	'parfum' 'eau de toilette'
111	'delicate' 'statement'
12	'flat brim' 'curved brim' 'fold over brim'
98	'dry' 'acne prone' 'mature'
57	'stacked heel' 'kitten heel' 'cone heel'
67	'id slot' 'interior pocket' 'interior zipper pocket'
31	'light wash' 'medium wash' 'colored'
85	'detailed stitching pant' 'simple seaming'
116	'knotted' 'percale' 'waffle weave'
88	'shag' 'cut pile'
74	'study hall' 'y2k' 'enchanted'
72	'fur' 'fleece' 'mesh'
108	'animal' 'love'
73	'unlined' 'fully lined' 'partially lined'
13	'wide brim' 'medium brim'
76	'bpa free material' 'scratch resistant material'
54	'straight handle' 'curved handle'
100	'rolled up sleeves' '3/4 sleeve' 'bracelet length'
84	'manual open' 'auto open'
14	'wide' 'medium'
27	'superhero' 'disney'
49	'half rim' 'full rim'
29	'tall crown' 'short crown'
106	'low stretch' 'non stretch'
112	'mid vamp' 'high vamp'
66	'large interior' 'medium interior' 'small interior'
53	'all hair types' 'damaged/dry hair'
117	'light weight' 'mid weight'
81	'low cut' 'mid chest neckline' 'open front'
5	'thin band' 'soft band elastic' 'elastic band'
28	'flat top crown' 'round crown' 'no crown'
56	'ultra high heel' 'mid heel' 'high heel'
110	'relaxed' 'tailored'
47	'uplifting' 'bold'
3	'changing pad' 'bottle pocket'
0	'squeeze dispenser' 'dropper'
80	'wall mount' 'ceiling mount'
6	'medium' 'wide'
36	'exterior pocket' 'exterior snap pocket'

Evaluation

Metrics

Label	Accuracy
all	0.5762

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("kaustubhgap/kaustubh_setfit")
# Run inference
preds = model("tube")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	1.7047	6

Label	Training Sample Count
0	2
1	5
2	12
3	2
4	6
5	3
6	2
7	12
8	16
9	2
10	2
11	11
12	4
13	2
14	2
15	2
16	2
17	6
18	9
19	63
20	8
21	31
22	6
23	2
24	13
25	5
26	2
27	2
28	3
29	2
30	13
31	3
32	7
33	22
34	12
35	102
36	2
37	119
38	34
39	32
40	6
41	2
42	13
43	17
44	5
45	10
46	6
47	2
48	10
49	2
50	91
51	13
52	2
53	2
54	2
55	12
56	4
57	7
58	17
59	2
60	2
61	7
62	9
63	3
64	14
65	53
66	3
67	6
68	41
69	41
70	33
71	5
72	5
73	4
74	7
75	49
76	2
77	23
78	11
79	12
80	2
81	5
82	33
83	33
84	2
85	2
86	17
87	2
88	2
89	10
90	29
91	2
92	8
93	21
94	2
95	3
96	5
97	10
98	5
99	6
100	6
101	12
102	13
103	2
104	10
105	28
106	2
107	321
108	2
109	10
110	2
111	2
112	2
113	15
114	4
115	2
116	5
117	2
118	2

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (5, 5)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0002	1	0.2895	-
0.0112	50	0.2531	-
0.0225	100	0.2622	-
0.0337	150	0.2535	-
0.0449	200	0.2144	-
0.0561	250	0.206	-
0.0674	300	0.1583	-
0.0786	350	0.1384	-
0.0898	400	0.1778	-
0.1011	450	0.2111	-
0.1123	500	0.1791	-
0.1235	550	0.2198	-
0.1347	600	0.0918	-
0.1460	650	0.1027	-
0.1572	700	0.1837	-
0.1684	750	0.1762	-
0.1797	800	0.1552	-
0.1909	850	0.2045	-
0.2021	900	0.1338	-
0.2133	950	0.0495	-
0.2246	1000	0.1136	-
0.2358	1050	0.0878	-
0.2470	1100	0.1671	-
0.2583	1150	0.0791	-
0.2695	1200	0.1332	-
0.2807	1250	0.0712	-
0.2919	1300	0.1853	-
0.3032	1350	0.134	-
0.3144	1400	0.1123	-
0.3256	1450	0.0525	-
0.3369	1500	0.0901	-
0.3481	1550	0.1554	-
0.3593	1600	0.0417	-
0.3705	1650	0.0762	-
0.3818	1700	0.0155	-
0.3930	1750	0.0115	-
0.4042	1800	0.0665	-
0.4155	1850	0.0578	-
0.4267	1900	0.0271	-
0.4379	1950	0.1374	-
0.4491	2000	0.1125	-
0.4604	2050	0.0304	-
0.4716	2100	0.0636	-
0.4828	2150	0.0668	-
0.4940	2200	0.1055	-
0.5053	2250	0.1147	-
0.5165	2300	0.0358	-
0.5277	2350	0.1516	-
0.5390	2400	0.008	-
0.5502	2450	0.082	-
0.5614	2500	0.0937	-
0.5726	2550	0.1382	-
0.5839	2600	0.0527	-
0.5951	2650	0.1091	-
0.6063	2700	0.0031	-
0.6176	2750	0.0181	-
0.6288	2800	0.1366	-
0.6400	2850	0.0178	-
0.6512	2900	0.0571	-
0.6625	2950	0.0271	-
0.6737	3000	0.0368	-
0.6849	3050	0.0652	-
0.6962	3100	0.0858	-
0.7074	3150	0.016	-
0.7186	3200	0.0318	-
0.7298	3250	0.0119	-
0.7411	3300	0.0314	-
0.7523	3350	0.008	-
0.7635	3400	0.0192	-
0.7748	3450	0.0363	-
0.7860	3500	0.0474	-
0.7972	3550	0.0172	-
0.8084	3600	0.0308	-
0.8197	3650	0.1168	-
0.8309	3700	0.0367	-
0.8421	3750	0.1572	-
0.8534	3800	0.0865	-
0.8646	3850	0.0124	-
0.8758	3900	0.0674	-
0.8870	3950	0.0534	-
0.8983	4000	0.0042	-
0.9095	4050	0.0503	-
0.9207	4100	0.0753	-
0.9320	4150	0.0079	-
0.9432	4200	0.1386	-
0.9544	4250	0.0693	-
0.9656	4300	0.0505	-
0.9769	4350	0.0153	-
0.9881	4400	0.0456	-
0.9993	4450	0.077	-
1.0	4453	-	0.1885
1.0106	4500	0.0107	-
1.0218	4550	0.0533	-
1.0330	4600	0.0069	-
1.0442	4650	0.0073	-
1.0555	4700	0.0521	-
1.0667	4750	0.0084	-
1.0779	4800	0.0443	-
1.0892	4850	0.0504	-
1.1004	4900	0.0445	-
1.1116	4950	0.0169	-
1.1228	5000	0.016	-
1.1341	5050	0.0046	-
1.1453	5100	0.0103	-
1.1565	5150	0.0404	-
1.1678	5200	0.0117	-
1.1790	5250	0.0399	-
1.1902	5300	0.0598	-
1.2014	5350	0.015	-
1.2127	5400	0.0048	-
1.2239	5450	0.0047	-
1.2351	5500	0.0042	-
1.2464	5550	0.0106	-
1.2576	5600	0.0041	-
1.2688	5650	0.1593	-
1.2800	5700	0.0386	-
1.2913	5750	0.0059	-
1.3025	5800	0.0043	-
1.3137	5850	0.0039	-
1.3249	5900	0.0101	-
1.3362	5950	0.0043	-
1.3474	6000	0.0056	-
1.3586	6050	0.002	-
1.3699	6100	0.0064	-
1.3811	6150	0.0106	-
1.3923	6200	0.03	-
1.4035	6250	0.0945	-
1.4148	6300	0.0025	-
1.4260	6350	0.0631	-
1.4372	6400	0.0068	-
1.4485	6450	0.0583	-
1.4597	6500	0.0015	-
1.4709	6550	0.0042	-
1.4821	6600	0.0093	-
1.4934	6650	0.0046	-
1.5046	6700	0.009	-
1.5158	6750	0.0279	-
1.5271	6800	0.0357	-
1.5383	6850	0.0282	-
1.5495	6900	0.0188	-
1.5607	6950	0.0405	-
1.5720	7000	0.0645	-
1.5832	7050	0.0066	-
1.5944	7100	0.0205	-
1.6057	7150	0.0038	-
1.6169	7200	0.0696	-
1.6281	7250	0.0055	-
1.6393	7300	0.0034	-
1.6506	7350	0.006	-
1.6618	7400	0.015	-
1.6730	7450	0.0023	-
1.6843	7500	0.0173	-
1.6955	7550	0.0601	-
1.7067	7600	0.0039	-
1.7179	7650	0.0201	-
1.7292	7700	0.0206	-
1.7404	7750	0.0042	-
1.7516	7800	0.0156	-
1.7629	7850	0.002	-
1.7741	7900	0.0059	-
1.7853	7950	0.0327	-
1.7965	8000	0.0206	-
1.8078	8050	0.0698	-
1.8190	8100	0.0217	-
1.8302	8150	0.0309	-
1.8415	8200	0.0136	-
1.8527	8250	0.0455	-
1.8639	8300	0.0645	-
1.8751	8350	0.0127	-
1.8864	8400	0.0056	-
1.8976	8450	0.0127	-
1.9088	8500	0.0024	-
1.9201	8550	0.0117	-
1.9313	8600	0.0626	-
1.9425	8650	0.0357	-
1.9537	8700	0.056	-
1.9650	8750	0.0311	-
1.9762	8800	0.0123	-
1.9874	8850	0.0638	-
1.9987	8900	0.0328	-
2.0	8906	-	0.2196
2.0099	8950	0.0015	-
2.0211	9000	0.0178	-
2.0323	9050	0.08	-
2.0436	9100	0.0983	-
2.0548	9150	0.0049	-
2.0660	9200	0.0092	-
2.0773	9250	0.0619	-
2.0885	9300	0.0159	-
2.0997	9350	0.0598	-
2.1109	9400	0.0343	-
2.1222	9450	0.0092	-
2.1334	9500	0.0013	-
2.1446	9550	0.0042	-
2.1558	9600	0.0059	-
2.1671	9650	0.0076	-
2.1783	9700	0.0027	-
2.1895	9750	0.0174	-
2.2008	9800	0.0044	-
2.2120	9850	0.0164	-
2.2232	9900	0.0015	-
2.2344	9950	0.0026	-
2.2457	10000	0.0118	-
2.2569	10050	0.0054	-
2.2681	10100	0.0016	-
2.2794	10150	0.0095	-
2.2906	10200	0.0157	-
2.3018	10250	0.0465	-
2.3130	10300	0.0024	-
2.3243	10350	0.0009	-
2.3355	10400	0.0101	-
2.3467	10450	0.0266	-
2.3580	10500	0.0022	-
2.3692	10550	0.0016	-
2.3804	10600	0.0096	-
2.3916	10650	0.0052	-
2.4029	10700	0.0656	-
2.4141	10750	0.0481	-
2.4253	10800	0.0148	-
2.4366	10850	0.0024	-
2.4478	10900	0.0039	-
2.4590	10950	0.0011	-
2.4702	11000	0.0142	-
2.4815	11050	0.0617	-
2.4927	11100	0.0069	-
2.5039	11150	0.0063	-
2.5152	11200	0.0218	-
2.5264	11250	0.0018	-
2.5376	11300	0.0017	-
2.5488	11350	0.0105	-
2.5601	11400	0.0019	-
2.5713	11450	0.0027	-
2.5825	11500	0.0616	-
2.5938	11550	0.0704	-
2.6050	11600	0.0047	-
2.6162	11650	0.0106	-
2.6274	11700	0.0067	-
2.6387	11750	0.0272	-
2.6499	11800	0.0476	-
2.6611	11850	0.0401	-
2.6724	11900	0.0017	-
2.6836	11950	0.0247	-
2.6948	12000	0.0173	-
2.7060	12050	0.0129	-
2.7173	12100	0.0041	-
2.7285	12150	0.0017	-
2.7397	12200	0.0137	-
2.7510	12250	0.0629	-
2.7622	12300	0.034	-
2.7734	12350	0.0533	-
2.7846	12400	0.057	-
2.7959	12450	0.0153	-
2.8071	12500	0.0023	-
2.8183	12550	0.0013	-
2.8296	12600	0.0014	-
2.8408	12650	0.0023	-
2.8520	12700	0.0026	-
2.8632	12750	0.0027	-
2.8745	12800	0.0064	-
2.8857	12850	0.0174	-
2.8969	12900	0.0017	-
2.9082	12950	0.0242	-
2.9194	13000	0.0487	-
2.9306	13050	0.0022	-
2.9418	13100	0.0108	-
2.9531	13150	0.0079	-
2.9643	13200	0.0108	-
2.9755	13250	0.0027	-
2.9868	13300	0.0053	-
2.9980	13350	0.0039	-
3.0	13359	-	0.2038
3.0092	13400	0.0089	-
3.0204	13450	0.0369	-
3.0317	13500	0.0107	-
3.0429	13550	0.0187	-
3.0541	13600	0.0038	-
3.0653	13650	0.0072	-
3.0766	13700	0.005	-
3.0878	13750	0.0192	-
3.0990	13800	0.0084	-
3.1103	13850	0.002	-
3.1215	13900	0.0011	-
3.1327	13950	0.0037	-
3.1439	14000	0.0087	-
3.1552	14050	0.0014	-
3.1664	14100	0.0029	-
3.1776	14150	0.0176	-
3.1889	14200	0.0028	-
3.2001	14250	0.012	-
3.2113	14300	0.0933	-
3.2225	14350	0.002	-
3.2338	14400	0.053	-
3.2450	14450	0.0117	-
3.2562	14500	0.0227	-
3.2675	14550	0.0055	-
3.2787	14600	0.008	-
3.2899	14650	0.0512	-
3.3011	14700	0.0025	-
3.3124	14750	0.0432	-
3.3236	14800	0.002	-
3.3348	14850	0.013	-
3.3461	14900	0.0026	-
3.3573	14950	0.0022	-
3.3685	15000	0.0225	-
3.3797	15050	0.0611	-
3.3910	15100	0.0261	-
3.4022	15150	0.0026	-
3.4134	15200	0.004	-
3.4247	15250	0.0054	-
3.4359	15300	0.0132	-
3.4471	15350	0.0017	-
3.4583	15400	0.0213	-
3.4696	15450	0.007	-
3.4808	15500	0.0507	-
3.4920	15550	0.0039	-
3.5033	15600	0.0059	-
3.5145	15650	0.0357	-
3.5257	15700	0.0009	-
3.5369	15750	0.0014	-
3.5482	15800	0.0011	-
3.5594	15850	0.0082	-
3.5706	15900	0.001	-
3.5819	15950	0.0045	-
3.5931	16000	0.0205	-
3.6043	16050	0.0096	-
3.6155	16100	0.0286	-
3.6268	16150	0.0043	-
3.6380	16200	0.0029	-
3.6492	16250	0.0079	-
3.6605	16300	0.0036	-
3.6717	16350	0.0013	-
3.6829	16400	0.0086	-
3.6941	16450	0.0049	-
3.7054	16500	0.0006	-
3.7166	16550	0.0467	-
3.7278	16600	0.002	-
3.7391	16650	0.0229	-
3.7503	16700	0.0532	-
3.7615	16750	0.001	-
3.7727	16800	0.0034	-
3.7840	16850	0.0117	-
3.7952	16900	0.0424	-
3.8064	16950	0.0032	-
3.8177	17000	0.0024	-
3.8289	17050	0.0011	-
3.8401	17100	0.0024	-
3.8513	17150	0.0059	-
3.8626	17200	0.0005	-
3.8738	17250	0.0074	-
3.8850	17300	0.0517	-
3.8962	17350	0.0081	-
3.9075	17400	0.0131	-
3.9187	17450	0.051	-
3.9299	17500	0.0114	-
3.9412	17550	0.0008	-
3.9524	17600	0.0094	-
3.9636	17650	0.001	-
3.9748	17700	0.0069	-
3.9861	17750	0.002	-
3.9973	17800	0.003	-
4.0	17812	-	0.2278
4.0085	17850	0.0309	-
4.0198	17900	0.005	-
4.0310	17950	0.0028	-
4.0422	18000	0.0069	-
4.0534	18050	0.002	-
4.0647	18100	0.0384	-
4.0759	18150	0.0123	-
4.0871	18200	0.0657	-
4.0984	18250	0.0042	-
4.1096	18300	0.0043	-
4.1208	18350	0.0035	-
4.1320	18400	0.0389	-
4.1433	18450	0.0303	-
4.1545	18500	0.002	-
4.1657	18550	0.0009	-
4.1770	18600	0.0025	-
4.1882	18650	0.1035	-
4.1994	18700	0.0033	-
4.2106	18750	0.0038	-
4.2219	18800	0.0161	-
4.2331	18850	0.0415	-
4.2443	18900	0.003	-
4.2556	18950	0.0055	-
4.2668	19000	0.0064	-
4.2780	19050	0.0656	-
4.2892	19100	0.0011	-
4.3005	19150	0.0252	-
4.3117	19200	0.0076	-
4.3229	19250	0.0051	-
4.3342	19300	0.0042	-
4.3454	19350	0.0043	-
4.3566	19400	0.014	-
4.3678	19450	0.0047	-
4.3791	19500	0.0043	-
4.3903	19550	0.0014	-
4.4015	19600	0.0017	-
4.4128	19650	0.0811	-
4.4240	19700	0.0013	-
4.4352	19750	0.0332	-
4.4464	19800	0.0636	-
4.4577	19850	0.0068	-
4.4689	19900	0.0076	-
4.4801	19950	0.0217	-
4.4914	20000	0.0387	-
4.5026	20050	0.0077	-
4.5138	20100	0.0778	-
4.5250	20150	0.0523	-
4.5363	20200	0.0597	-
4.5475	20250	0.0092	-
4.5587	20300	0.0684	-
4.5700	20350	0.0151	-
4.5812	20400	0.0007	-
4.5924	20450	0.0018	-
4.6036	20500	0.0003	-
4.6149	20550	0.0051	-
4.6261	20600	0.0144	-
4.6373	20650	0.011	-
4.6486	20700	0.0061	-
4.6598	20750	0.0066	-
4.6710	20800	0.0046	-
4.6822	20850	0.0511	-
4.6935	20900	0.0198	-
4.7047	20950	0.001	-
4.7159	21000	0.0022	-
4.7272	21050	0.053	-
4.7384	21100	0.0025	-
4.7496	21150	0.034	-
4.7608	21200	0.0147	-
4.7721	21250	0.0684	-
4.7833	21300	0.0012	-
4.7945	21350	0.0029	-
4.8057	21400	0.0014	-
4.8170	21450	0.0522	-
4.8282	21500	0.0766	-
4.8394	21550	0.0031	-
4.8507	21600	0.0012	-
4.8619	21650	0.0011	-
4.8731	21700	0.0235	-
4.8843	21750	0.001	-
4.8956	21800	0.0178	-
4.9068	21850	0.0006	-
4.9180	21900	0.0092	-
4.9293	21950	0.025	-
4.9405	22000	0.017	-
4.9517	22050	0.0052	-
4.9629	22100	0.0437	-
4.9742	22150	0.0019	-
4.9854	22200	0.0039	-
4.9966	22250	0.0015	-
5.0	22265	-	0.2357

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.36.1
PyTorch: 2.0.1+cu118
Datasets: 2.15.0
Tokenizers: 0.15.0

Citation

BibTeX

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}
}

Label	Training Sample Count
0	2
1	5
2	12
3	2
4	6
5	3
6	2
7	12
8	16
9	2
10	2
11	11
12	4
13	2
14	2
15	2
16	2
17	6
18	9
19	63
20	8
21	31
22	6
23	2
24	13
25	5
26	2
27	2
28	3
29	2
30	13
31	3
32	7
33	22
34	12
35	102
36	2
37	119
38	34
39	32
40	6
41	2
42	13
43	17
44	5
45	10
46	6
47	2
48	10
49	2
50	91
51	13
52	2
53	2
54	2
55	12
56	4
57	7
58	17
59	2
60	2
61	7
62	9
63	3
64	14
65	53
66	3
67	6
68	41
69	41
70	33
71	5
72	5
73	4
74	7
75	49
76	2
77	23
78	11
79	12
80	2
81	5
82	33
83	33
84	2
85	2
86	17
87	2
88	2
89	10
90	29
91	2
92	8
93	21
94	2
95	3
96	5
97	10
98	5
99	6
100	6
101	12
102	13
103	2
104	10
105	28
106	2
107	321
108	2
109	10
110	2
111	2
112	2
113	15
114	4
115	2
116	5
117	2
118	2

Label	Training Sample Count
0	2
1	5
2	12
3	2
4	6
5	3
6	2
7	12
8	16
9	2
10	2
11	11
12	4
13	2
14	2
15	2
16	2
17	6
18	9
19	63
20	8
21	31
22	6
23	2
24	13
25	5
26	2
27	2
28	3
29	2
30	13
31	3
32	7
33	22
34	12
35	102
36	2
37	119
38	34
39	32
40	6
41	2
42	13
43	17
44	5
45	10
46	6
47	2
48	10
49	2
50	91
51	13
52	2
53	2
54	2
55	12
56	4
57	7
58	17
59	2
60	2
61	7
62	9
63	3
64	14
65	53
66	3
67	6
68	41
69	41
70	33
71	5
72	5
73	4
74	7
75	49
76	2
77	23
78	11
79	12
80	2
81	5
82	33
83	33
84	2
85	2
86	17
87	2
88	2
89	10
90	29
91	2
92	8
93	21
94	2
95	3
96	5
97	10
98	5
99	6
100	6
101	12
102	13
103	2
104	10
105	28
106	2
107	321
108	2
109	10
110	2
111	2
112	2
113	15
114	4
115	2
116	5
117	2
118	2

Label	Training Sample Count
0	2
1	5
2	12
3	2
4	6
5	3
6	2
7	12
8	16
9	2
10	2
11	11
12	4
13	2
14	2
15	2
16	2
17	6
18	9
19	63
20	8
21	31
22	6
23	2
24	13
25	5
26	2
27	2
28	3
29	2
30	13
31	3
32	7
33	22
34	12
35	102
36	2
37	119
38	34
39	32
40	6
41	2
42	13
43	17
44	5
45	10
46	6
47	2
48	10
49	2
50	91
51	13
52	2
53	2
54	2
55	12
56	4
57	7
58	17
59	2
60	2
61	7
62	9
63	3
64	14
65	53
66	3
67	6
68	41
69	41
70	33
71	5
72	5
73	4
74	7
75	49
76	2
77	23
78	11
79	12
80	2
81	5
82	33
83	33
84	2
85	2
86	17
87	2
88	2
89	10
90	29
91	2
92	8
93	21
94	2
95	3
96	5
97	10
98	5
99	6
100	6
101	12
102	13
103	2
104	10
105	28
106	2
107	321
108	2
109	10
110	2
111	2
112	2
113	15
114	4
115	2
116	5
117	2
118	2