metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Thank you for your email. Please go ahead and issue. Please invoice in KES
  - text: >-
      Hi, We are missing some invoices, can you please provide it. 02 - 12 -
      2020 AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160
      $22.00 02 - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT
      FEE 8900783383420 $21.00
  - text: >-
      We need your assistance with the payment for the recent office supplies
      order. Let us know once it's done.
  - text: >-
      I have reported this in November and not only was the trip supposed to be
      cancelled and credited I was double billed and the billing has not been
      corrected. The total credit should be $667.20. Please confirm this will be
      done.
  - text: >-
      The invoice for the travel arrangements needs to be settled. Kindly
      provide payment confirmation.
inference: true

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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/all-MiniLM-L6-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 256 tokens
Number of Classes: 14 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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("mann2107/BCMPIIRAB_MiniLM_ALLNewV2")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	25.6577	136

Label	Training Sample Count
0	24
1	24
2	24
3	24
4	24
5	24
6	24
7	24
8	24
9	24
10	24
11	24
12	24
13	24

Training Hyperparameters

batch_size: (8, 8)
num_epochs: (5, 5)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 68
body_learning_rate: (1.44030579311381e-05, 1.44030579311381e-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
max_length: 512
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0002	1	0.2917	-
0.0088	50	0.2434	-
0.0175	100	0.2053	-
0.0263	150	0.1789	-
0.0350	200	0.2249	-
0.0438	250	0.1773	-
0.0525	300	0.1648	-
0.0613	350	0.2617	-
0.0700	400	0.1342	-
0.0788	450	0.1064	-
0.0875	500	0.1273	-
0.0963	550	0.1248	-
0.1050	600	0.2013	-
0.1138	650	0.1979	-
0.1225	700	0.1631	-
0.1313	750	0.1079	-
0.1401	800	0.0858	-
0.1488	850	0.0999	-
0.1576	900	0.0638	-
0.1663	950	0.1287	-
0.1751	1000	0.1408	-
0.1838	1050	0.1902	-
0.1926	1100	0.0648	-
0.2013	1150	0.1383	-
0.2101	1200	0.0609	-
0.2188	1250	0.0865	-
0.2276	1300	0.1069	-
0.2363	1350	0.051	-
0.2451	1400	0.0692	-
0.2539	1450	0.123	-
0.2626	1500	0.0758	-
0.2714	1550	0.0835	-
0.2801	1600	0.0523	-
0.2889	1650	0.0946	-
0.2976	1700	0.0445	-
0.3064	1750	0.0248	-
0.3151	1800	0.0373	-
0.3239	1850	0.0248	-
0.3326	1900	0.0446	-
0.3414	1950	0.0142	-
0.3501	2000	0.023	-
0.3589	2050	0.0119	-
0.3676	2100	0.0383	-
0.3764	2150	0.0188	-
0.3852	2200	0.0204	-
0.3939	2250	0.0109	-
0.4027	2300	0.0273	-
0.4114	2350	0.0216	-
0.4202	2400	0.0073	-
0.4289	2450	0.0338	-
0.4377	2500	0.0047	-
0.4464	2550	0.0096	-
0.4552	2600	0.0069	-
0.4639	2650	0.0078	-
0.4727	2700	0.0122	-
0.4814	2750	0.0578	-
0.4902	2800	0.0074	-
0.4989	2850	0.0103	-
0.5077	2900	0.0092	-
0.5165	2950	0.004	-
0.5252	3000	0.0061	-
0.5340	3050	0.0214	-
0.5427	3100	0.0048	-
0.5515	3150	0.0036	-
0.5602	3200	0.0041	-
0.5690	3250	0.0151	-
0.5777	3300	0.0042	-
0.5865	3350	0.0029	-
0.5952	3400	0.0021	-
0.6040	3450	0.0018	-
0.6127	3500	0.0058	-
0.6215	3550	0.0011	-
0.6303	3600	0.0078	-
0.6390	3650	0.0011	-
0.6478	3700	0.0017	-
0.6565	3750	0.0022	-
0.6653	3800	0.0016	-
0.6740	3850	0.002	-
0.6828	3900	0.0023	-
0.6915	3950	0.0011	-
0.7003	4000	0.0012	-
0.7090	4050	0.0007	-
0.7178	4100	0.0021	-
0.7265	4150	0.0019	-
0.7353	4200	0.002	-
0.7440	4250	0.0018	-
0.7528	4300	0.0029	-
0.7616	4350	0.0015	-
0.7703	4400	0.0022	-
0.7791	4450	0.0012	-
0.7878	4500	0.0007	-
0.7966	4550	0.0015	-
0.8053	4600	0.0011	-
0.8141	4650	0.0016	-
0.8228	4700	0.0009	-
0.8316	4750	0.0007	-
0.8403	4800	0.0011	-
0.8491	4850	0.001	-
0.8578	4900	0.0008	-
0.8666	4950	0.0014	-
0.8754	5000	0.0022	-
0.8841	5050	0.0012	-
0.8929	5100	0.0007	-
0.9016	5150	0.0014	-
0.9104	5200	0.0007	-
0.9191	5250	0.0012	-
0.9279	5300	0.0011	-
0.9366	5350	0.0012	-
0.9454	5400	0.0029	-
0.9541	5450	0.001	-
0.9629	5500	0.0011	-
0.9716	5550	0.0004	-
0.9804	5600	0.0009	-
0.9891	5650	0.0004	-
0.9979	5700	0.003	-
1.0	5712	-	0.0459
1.0067	5750	0.0014	-
1.0154	5800	0.0008	-
1.0242	5850	0.0009	-
1.0329	5900	0.0007	-
1.0417	5950	0.0007	-
1.0504	6000	0.0006	-
1.0592	6050	0.0008	-
1.0679	6100	0.0006	-
1.0767	6150	0.0006	-
1.0854	6200	0.0007	-
1.0942	6250	0.0025	-
1.1029	6300	0.0006	-
1.1117	6350	0.0009	-
1.1204	6400	0.0009	-
1.1292	6450	0.0009	-
1.1380	6500	0.0006	-
1.1467	6550	0.0004	-
1.1555	6600	0.0014	-
1.1642	6650	0.0029	-
1.1730	6700	0.0004	-
1.1817	6750	0.0027	-
1.1905	6800	0.0003	-
1.1992	6850	0.0003	-
1.2080	6900	0.0006	-
1.2167	6950	0.0015	-
1.2255	7000	0.0005	-
1.2342	7050	0.0005	-
1.2430	7100	0.0016	-
1.2518	7150	0.0005	-
1.2605	7200	0.0003	-
1.2693	7250	0.0006	-
1.2780	7300	0.0007	-
1.2868	7350	0.0004	-
1.2955	7400	0.0007	-
1.3043	7450	0.0007	-
1.3130	7500	0.0007	-
1.3218	7550	0.0003	-
1.3305	7600	0.0002	-
1.3393	7650	0.0002	-
1.3480	7700	0.0005	-
1.3568	7750	0.0014	-
1.3655	7800	0.0012	-
1.3743	7850	0.0002	-
1.3831	7900	0.0002	-
1.3918	7950	0.0003	-
1.4006	8000	0.0005	-
1.4093	8050	0.0006	-
1.4181	8100	0.0003	-
1.4268	8150	0.0009	-
1.4356	8200	0.0004	-
1.4443	8250	0.0002	-
1.4531	8300	0.0004	-
1.4618	8350	0.0008	-
1.4706	8400	0.0002	-
1.4793	8450	0.0004	-
1.4881	8500	0.0006	-
1.4968	8550	0.0011	-
1.5056	8600	0.0003	-
1.5144	8650	0.0003	-
1.5231	8700	0.0004	-
1.5319	8750	0.0004	-
1.5406	8800	0.0002	-
1.5494	8850	0.0007	-
1.5581	8900	0.0003	-
1.5669	8950	0.0002	-
1.5756	9000	0.0007	-
1.5844	9050	0.0005	-
1.5931	9100	0.0005	-
1.6019	9150	0.0011	-
1.6106	9200	0.0004	-
1.6194	9250	0.0004	-
1.6282	9300	0.0003	-
1.6369	9350	0.0002	-
1.6457	9400	0.0003	-
1.6544	9450	0.0006	-
1.6632	9500	0.0004	-
1.6719	9550	0.0004	-
1.6807	9600	0.0006	-
1.6894	9650	0.0001	-
1.6982	9700	0.0002	-
1.7069	9750	0.0004	-
1.7157	9800	0.0004	-
1.7244	9850	0.0001	-
1.7332	9900	0.0004	-
1.7419	9950	0.0004	-
1.7507	10000	0.0006	-
1.7595	10050	0.0003	-
1.7682	10100	0.0002	-
1.7770	10150	0.0004	-
1.7857	10200	0.0004	-
1.7945	10250	0.0002	-
1.8032	10300	0.0008	-
1.8120	10350	0.0004	-
1.8207	10400	0.0005	-
1.8295	10450	0.0004	-
1.8382	10500	0.0001	-
1.8470	10550	0.0003	-
1.8557	10600	0.0003	-
1.8645	10650	0.0005	-
1.8732	10700	0.0005	-
1.8820	10750	0.0003	-
1.8908	10800	0.0001	-
1.8995	10850	0.0002	-
1.9083	10900	0.0001	-
1.9170	10950	0.0003	-
1.9258	11000	0.0005	-
1.9345	11050	0.0003	-
1.9433	11100	0.0004	-
1.9520	11150	0.0007	-
1.9608	11200	0.0002	-
1.9695	11250	0.0003	-
1.9783	11300	0.0001	-
1.9870	11350	0.0001	-
1.9958	11400	0.0002	-
2.0	11424	-	0.042
2.0046	11450	0.0003	-
2.0133	11500	0.0002	-
2.0221	11550	0.0002	-
2.0308	11600	0.0002	-
2.0396	11650	0.0003	-
2.0483	11700	0.0003	-
2.0571	11750	0.0002	-
2.0658	11800	0.0002	-
2.0746	11850	0.0002	-
2.0833	11900	0.0002	-
2.0921	11950	0.0001	-
2.1008	12000	0.0003	-
2.1096	12050	0.0005	-
2.1183	12100	0.0002	-
2.1271	12150	0.0003	-
2.1359	12200	0.0002	-
2.1446	12250	0.0003	-
2.1534	12300	0.0003	-
2.1621	12350	0.0001	-
2.1709	12400	0.0002	-
2.1796	12450	0.0002	-
2.1884	12500	0.0002	-
2.1971	12550	0.0002	-
2.2059	12600	0.0001	-
2.2146	12650	0.0002	-
2.2234	12700	0.0003	-
2.2321	12750	0.0003	-
2.2409	12800	0.0004	-
2.2496	12850	0.0002	-
2.2584	12900	0.0002	-
2.2672	12950	0.0003	-
2.2759	13000	0.0002	-
2.2847	13050	0.0002	-
2.2934	13100	0.0002	-
2.3022	13150	0.0001	-
2.3109	13200	0.0002	-
2.3197	13250	0.0001	-
2.3284	13300	0.0002	-
2.3372	13350	0.0003	-
2.3459	13400	0.0002	-
2.3547	13450	0.0001	-
2.3634	13500	0.0002	-
2.3722	13550	0.0001	-
2.3810	13600	0.0006	-
2.3897	13650	0.0001	-
2.3985	13700	0.0002	-
2.4072	13750	0.0002	-
2.4160	13800	0.0004	-
2.4247	13850	0.0001	-
2.4335	13900	0.0003	-
2.4422	13950	0.0001	-
2.4510	14000	0.0001	-
2.4597	14050	0.0001	-
2.4685	14100	0.0005	-
2.4772	14150	0.0002	-
2.4860	14200	0.0001	-
2.4947	14250	0.0003	-
2.5035	14300	0.0005	-
2.5123	14350	0.0002	-
2.5210	14400	0.0002	-
2.5298	14450	0.0003	-
2.5385	14500	0.0001	-
2.5473	14550	0.0001	-
2.5560	14600	0.0002	-
2.5648	14650	0.0002	-
2.5735	14700	0.0001	-
2.5823	14750	0.0001	-
2.5910	14800	0.0001	-
2.5998	14850	0.0003	-
2.6085	14900	0.0002	-
2.6173	14950	0.0001	-
2.6261	15000	0.0001	-
2.6348	15050	0.0001	-
2.6436	15100	0.0001	-
2.6523	15150	0.0002	-
2.6611	15200	0.0001	-
2.6698	15250	0.0002	-
2.6786	15300	0.0002	-
2.6873	15350	0.0002	-
2.6961	15400	0.0002	-
2.7048	15450	0.0002	-
2.7136	15500	0.0001	-
2.7223	15550	0.0002	-
2.7311	15600	0.0002	-
2.7398	15650	0.0003	-
2.7486	15700	0.0002	-
2.7574	15750	0.0001	-
2.7661	15800	0.0002	-
2.7749	15850	0.0002	-
2.7836	15900	0.0003	-
2.7924	15950	0.0004	-
2.8011	16000	0.0007	-
2.8099	16050	0.0001	-
2.8186	16100	0.0001	-
2.8274	16150	0.0002	-
2.8361	16200	0.0002	-
2.8449	16250	0.0001	-
2.8536	16300	0.0001	-
2.8624	16350	0.0002	-
2.8711	16400	0.0002	-
2.8799	16450	0.0001	-
2.8887	16500	0.0002	-
2.8974	16550	0.0002	-
2.9062	16600	0.0001	-
2.9149	16650	0.0001	-
2.9237	16700	0.0001	-
2.9324	16750	0.0003	-
2.9412	16800	0.0002	-
2.9499	16850	0.0003	-
2.9587	16900	0.0001	-
2.9674	16950	0.0002	-
2.9762	17000	0.0001	-
2.9849	17050	0.0001	-
2.9937	17100	0.0001	-
3.0	17136	-	0.0419
3.0025	17150	0.0002	-
3.0112	17200	0.0002	-
3.0200	17250	0.0003	-
3.0287	17300	0.0001	-
3.0375	17350	0.0002	-
3.0462	17400	0.0001	-
3.0550	17450	0.0002	-
3.0637	17500	0.0002	-
3.0725	17550	0.0002	-
3.0812	17600	0.0001	-
3.0900	17650	0.0001	-
3.0987	17700	0.0001	-
3.1075	17750	0.0001	-
3.1162	17800	0.0001	-
3.125	17850	0.0001	-
3.1338	17900	0.0002	-
3.1425	17950	0.0001	-
3.1513	18000	0.0003	-
3.1600	18050	0.0001	-
3.1688	18100	0.0001	-
3.1775	18150	0.0001	-
3.1863	18200	0.0002	-
3.1950	18250	0.0002	-
3.2038	18300	0.0001	-
3.2125	18350	0.0001	-
3.2213	18400	0.0001	-
3.2300	18450	0.0002	-
3.2388	18500	0.0001	-
3.2475	18550	0.0002	-
3.2563	18600	0.0001	-
3.2651	18650	0.0002	-
3.2738	18700	0.0001	-
3.2826	18750	0.0001	-
3.2913	18800	0.0001	-
3.3001	18850	0.0001	-
3.3088	18900	0.0003	-
3.3176	18950	0.0002	-
3.3263	19000	0.0001	-
3.3351	19050	0.0003	-
3.3438	19100	0.0001	-
3.3526	19150	0.0001	-
3.3613	19200	0.0001	-
3.3701	19250	0.0001	-
3.3789	19300	0.0001	-
3.3876	19350	0.0002	-
3.3964	19400	0.0001	-
3.4051	19450	0.0001	-
3.4139	19500	0.0001	-
3.4226	19550	0.0001	-
3.4314	19600	0.0001	-
3.4401	19650	0.0001	-
3.4489	19700	0.0002	-
3.4576	19750	0.0001	-
3.4664	19800	0.0001	-
3.4751	19850	0.0001	-
3.4839	19900	0.0001	-
3.4926	19950	0.0001	-
3.5014	20000	0.0001	-
3.5102	20050	0.0002	-
3.5189	20100	0.0003	-
3.5277	20150	0.0001	-
3.5364	20200	0.0002	-
3.5452	20250	0.0001	-
3.5539	20300	0.0001	-
3.5627	20350	0.0001	-
3.5714	20400	0.0004	-
3.5802	20450	0.0001	-
3.5889	20500	0.0001	-
3.5977	20550	0.0001	-
3.6064	20600	0.0002	-
3.6152	20650	0.0001	-
3.6239	20700	0.0001	-
3.6327	20750	0.0	-
3.6415	20800	0.0002	-
3.6502	20850	0.0001	-
3.6590	20900	0.0001	-
3.6677	20950	0.0002	-
3.6765	21000	0.0001	-
3.6852	21050	0.0001	-
3.6940	21100	0.0001	-
3.7027	21150	0.0002	-
3.7115	21200	0.0004	-
3.7202	21250	0.0001	-
3.7290	21300	0.0002	-
3.7377	21350	0.0001	-
3.7465	21400	0.0004	-
3.7553	21450	0.0002	-
3.7640	21500	0.0001	-
3.7728	21550	0.0001	-
3.7815	21600	0.0001	-
3.7903	21650	0.0001	-
3.7990	21700	0.0001	-
3.8078	21750	0.0001	-
3.8165	21800	0.0	-
3.8253	21850	0.0	-
3.8340	21900	0.0001	-
3.8428	21950	0.0003	-
3.8515	22000	0.0001	-
3.8603	22050	0.0001	-
3.8690	22100	0.0002	-
3.8778	22150	0.0001	-
3.8866	22200	0.0003	-
3.8953	22250	0.0001	-
3.9041	22300	0.0	-
3.9128	22350	0.0001	-
3.9216	22400	0.0002	-
3.9303	22450	0.0001	-
3.9391	22500	0.0001	-
3.9478	22550	0.0	-
3.9566	22600	0.0003	-
3.9653	22650	0.0001	-
3.9741	22700	0.0001	-
3.9828	22750	0.0001	-
3.9916	22800	0.0002	-
4.0	22848	-	0.0419
4.0004	22850	0.0	-
4.0091	22900	0.0001	-
4.0179	22950	0.0001	-
4.0266	23000	0.0001	-
4.0354	23050	0.0001	-
4.0441	23100	0.0002	-
4.0529	23150	0.0001	-
4.0616	23200	0.0001	-
4.0704	23250	0.0002	-
4.0791	23300	0.0	-
4.0879	23350	0.0001	-
4.0966	23400	0.0001	-
4.1054	23450	0.0001	-
4.1141	23500	0.0001	-
4.1229	23550	0.0002	-
4.1317	23600	0.0001	-
4.1404	23650	0.0001	-
4.1492	23700	0.0001	-
4.1579	23750	0.0002	-
4.1667	23800	0.0002	-
4.1754	23850	0.0001	-
4.1842	23900	0.0001	-
4.1929	23950	0.0001	-
4.2017	24000	0.0001	-
4.2104	24050	0.0001	-
4.2192	24100	0.0001	-
4.2279	24150	0.0	-
4.2367	24200	0.0001	-
4.2454	24250	0.0001	-
4.2542	24300	0.0003	-
4.2630	24350	0.0	-
4.2717	24400	0.0001	-
4.2805	24450	0.0	-
4.2892	24500	0.0001	-
4.2980	24550	0.0001	-
4.3067	24600	0.0002	-
4.3155	24650	0.0	-
4.3242	24700	0.0001	-
4.3330	24750	0.0001	-
4.3417	24800	0.0001	-
4.3505	24850	0.0001	-
4.3592	24900	0.0001	-
4.3680	24950	0.0	-
4.3768	25000	0.0002	-
4.3855	25050	0.0001	-
4.3943	25100	0.0001	-
4.4030	25150	0.0001	-
4.4118	25200	0.0001	-
4.4205	25250	0.0001	-
4.4293	25300	0.0002	-
4.4380	25350	0.0002	-
4.4468	25400	0.0001	-
4.4555	25450	0.0001	-
4.4643	25500	0.0001	-
4.4730	25550	0.0001	-
4.4818	25600	0.0001	-
4.4905	25650	0.0001	-
4.4993	25700	0.0001	-
4.5081	25750	0.0001	-
4.5168	25800	0.0001	-
4.5256	25850	0.0001	-
4.5343	25900	0.0001	-
4.5431	25950	0.0001	-
4.5518	26000	0.0	-
4.5606	26050	0.0	-
4.5693	26100	0.0001	-
4.5781	26150	0.0001	-
4.5868	26200	0.0001	-
4.5956	26250	0.0001	-
4.6043	26300	0.0001	-
4.6131	26350	0.0001	-
4.6218	26400	0.0002	-
4.6306	26450	0.0001	-
4.6394	26500	0.0001	-
4.6481	26550	0.0001	-
4.6569	26600	0.0001	-
4.6656	26650	0.0	-
4.6744	26700	0.0002	-
4.6831	26750	0.0	-
4.6919	26800	0.0001	-
4.7006	26850	0.0002	-
4.7094	26900	0.0002	-
4.7181	26950	0.0001	-
4.7269	27000	0.0001	-
4.7356	27050	0.0001	-
4.7444	27100	0.0	-
4.7532	27150	0.0001	-
4.7619	27200	0.0001	-
4.7707	27250	0.0001	-
4.7794	27300	0.0	-
4.7882	27350	0.0001	-
4.7969	27400	0.0001	-
4.8057	27450	0.0002	-
4.8144	27500	0.0	-
4.8232	27550	0.0001	-
4.8319	27600	0.0001	-
4.8407	27650	0.0001	-
4.8494	27700	0.0	-
4.8582	27750	0.0001	-
4.8669	27800	0.0001	-
4.8757	27850	0.0001	-
4.8845	27900	0.0001	-
4.8932	27950	0.0001	-
4.9020	28000	0.0001	-
4.9107	28050	0.0001	-
4.9195	28100	0.0	-
4.9282	28150	0.0001	-
4.9370	28200	0.0001	-
4.9457	28250	0.0001	-
4.9545	28300	0.0001	-
4.9632	28350	0.0001	-
4.9720	28400	0.0001	-
4.9807	28450	0.0001	-
4.9895	28500	0.0002	-
4.9982	28550	0.0	-
5.0	28560	-	0.0425

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.1.0.dev0
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Datasets: 2.20.0
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}
}