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gsjang/
sroberta-embedding

Name: sroberta-embedding
Rating: 2.5 (6 reviews)

setfit

pytorch

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

jhgan/ko-sroberta-multitask

finetune

model-index

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SetFit with jhgan/ko-sroberta-multitask

This is a SetFit model that can be used for Text Classification. This SetFit model uses jhgan/ko-sroberta-multitask 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: jhgan/ko-sroberta-multitask
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens
Number of Classes: 2 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

rag

'QR코드 스캔 후 필요한 서류와 절차는?'
'연구용역사업의 원가계산서 관련, 일정 금액 이상 지출 승인은 누구에게 받나요?'
'계약부서 승인 없이 지급신청 시 주의할 점은?'

general

'아래 글의 요지 좀 설명해줘.\n \n 다른 문화권에서 온 여자와 데이트. 관계에 대해 좋은 점이 많이 있습니다. 공통된 직업적 관심사, 동일한 성욕, 그리고 서로를 존중한다는 점은 제게는 새로운 관계입니다(항상 남성에 대해 안 좋은 태도를 가진 여자들과만 사귀어 왔죠). 그녀는 저를 정말 사랑해요. \n \n 하지만 장기적인 생존 가능성에 대해 몇 가지 심각한 우려가 있습니다. 하나는 부모님에 관한 것입니다. 제 부모님은 우리가 사귀는 사이라는 사실을 알게 되자 "네가 미국에 머물 수 있는 티켓이라는 걸 기억하라"고 말씀하셨어요. 우리가 진짜 사귀는 사이라는 사실을 알게 된 부모님은 제가 얼마나 버는지 알고 싶어 하셨고(저는 대학원생입니다), 존경의 표시로 은퇴한 부모님을 부양하는 전통에 대해 제가 괜찮은지 확인하고 싶어 하셨습니다(부모님은 그런 도움이 필요 없을 만큼 잘 살고 계시지만요). 여자친구는 이에 대해 부모님의 의견에 동의하며 제가 괜찮지 않다면 돈을 더 벌어서 직접 해야 한다고 말했습니다. 또한 여자친구는 제가 이전에 결혼했고 지금은 이혼했다는 사실을 부모님이 '절대 알 수 없다'고 말합니다. \n \n 제가 극복하거나 간과할 수 있었던 다른 문제들도 있지만(한 가지 예로, 그녀는 사교적이지 않지만 저는 사교적입니다), 이러한 문제들이 결합되어 그녀와의 미래는 앞으로 많은 문제가 예고되어 있고 위험하다고 느낍니다. 이전 결혼 생활에서 저는 그런 징후를 무시하고 대가를 치렀고, 그 역사를 반복하고 싶지 않습니다. 동시에 저와 성적으로도 잘 어울리는 파트너가 있다는 것은 정말 좋은 일입니다. \n \n 다른 사람들은 이런 다문화적인 상황에서 어떤 경험을 했는지, 특히 장기적인 경험이 있다면 어떤지 궁금합니다.'
'너는 누구냐니까'
'문제와 몇 가지 답 옵션("A", "B", "C", "D"와 연관된)이 주어집니다. 상식적인 지식을 바탕으로 정답을 선택해야 합니다. 연상에 기반한 답은 피하고, 답안 세트는 연상을 넘어서는 상식을 파악하기 위해 의도적으로 선택된 것입니다. 'A', 'B', 'C', 'D', 'E' 중 하나를 제외하고는 다른 문자를 생성하지 말고 각 문제에 대해 하나의 답만 작성하세요.\n\n폰이라는 이름은 매우 다재다능할 수 있지만, 모든 부품이 중요한 것은 무엇일까요?\n(A)체스 게임 (B)계획 (C)체스 세트 (D)체커 (E)노스 캐롤라이나'

Evaluation

Metrics

Label	Accuracy
all	0.9952

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("NTIS/sroberta-embedding")
# Run inference
preds = model("원장 인계 전 필요한 절차는?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	24.824	722

Label	Training Sample Count
rag	553
general	447

Training Hyperparameters

batch_size: (64, 64)
num_epochs: (4, 4)
max_steps: -1
sampling_strategy: oversampling
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
0.0001	1	0.2655	-
0.0063	50	0.2091	-
0.0126	100	0.2327	-
0.0189	150	0.1578	-
0.0253	200	0.0836	-
0.0316	250	0.0274	-
0.0379	300	0.0068	-
0.0442	350	0.0032	-
0.0505	400	0.0013	-
0.0568	450	0.0012	-
0.0632	500	0.0009	-
0.0695	550	0.0006	-
0.0758	600	0.0004	-
0.0821	650	0.0004	-
0.0884	700	0.0003	-
0.0947	750	0.0003	-
0.1011	800	0.0003	-
0.1074	850	0.0002	-
0.1137	900	0.0002	-
0.1200	950	0.0002	-
0.1263	1000	0.0002	-
0.1326	1050	0.0001	-
0.1390	1100	0.0001	-
0.1453	1150	0.0001	-
0.1516	1200	0.0001	-
0.1579	1250	0.0001	-
0.1642	1300	0.0001	-
0.1705	1350	0.0001	-
0.1769	1400	0.0001	-
0.1832	1450	0.0001	-
0.1895	1500	0.0001	-
0.1958	1550	0.0001	-
0.2021	1600	0.0	-
0.2084	1650	0.0001	-
0.2148	1700	0.0001	-
0.2211	1750	0.0	-
0.2274	1800	0.0001	-
0.2337	1850	0.0	-
0.2400	1900	0.0	-
0.2463	1950	0.0	-
0.2527	2000	0.0	-
0.2590	2050	0.0	-
0.2653	2100	0.0	-
0.2716	2150	0.0	-
0.2779	2200	0.0	-
0.2842	2250	0.0	-
0.2906	2300	0.0	-
0.2969	2350	0.0	-
0.3032	2400	0.0	-
0.3095	2450	0.0	-
0.3158	2500	0.0	-
0.3221	2550	0.0	-
0.3284	2600	0.0	-
0.3348	2650	0.0	-
0.3411	2700	0.0	-
0.3474	2750	0.0	-
0.3537	2800	0.0	-
0.3600	2850	0.0	-
0.3663	2900	0.0	-
0.3727	2950	0.0	-
0.3790	3000	0.0	-
0.3853	3050	0.0	-
0.3916	3100	0.0	-
0.3979	3150	0.0	-
0.4042	3200	0.0	-
0.4106	3250	0.0	-
0.4169	3300	0.0	-
0.4232	3350	0.0	-
0.4295	3400	0.0	-
0.4358	3450	0.0	-
0.4421	3500	0.0	-
0.4485	3550	0.0	-
0.4548	3600	0.0	-
0.4611	3650	0.0	-
0.4674	3700	0.0	-
0.4737	3750	0.0	-
0.4800	3800	0.0	-
0.4864	3850	0.0	-
0.4927	3900	0.0	-
0.4990	3950	0.0	-
0.5053	4000	0.0	-
0.5116	4050	0.0	-
0.5179	4100	0.0	-
0.5243	4150	0.0	-
0.5306	4200	0.0	-
0.5369	4250	0.0	-
0.5432	4300	0.0	-
0.5495	4350	0.0004	-
0.5558	4400	0.0001	-
0.5622	4450	0.0	-
0.5685	4500	0.0096	-
0.5748	4550	0.0	-
0.5811	4600	0.0	-
0.5874	4650	0.0	-
0.5937	4700	0.0	-
0.6001	4750	0.0	-
0.6064	4800	0.0	-
0.6127	4850	0.0	-
0.6190	4900	0.0	-
0.6253	4950	0.0	-
0.6316	5000	0.0	-
0.6379	5050	0.0	-
0.6443	5100	0.0	-
0.6506	5150	0.0	-
0.6569	5200	0.0	-
0.6632	5250	0.0	-
0.6695	5300	0.0	-
0.6758	5350	0.0	-
0.6822	5400	0.0	-
0.6885	5450	0.0	-
0.6948	5500	0.0	-
0.7011	5550	0.0	-
0.7074	5600	0.0	-
0.7137	5650	0.0	-
0.7201	5700	0.0	-
0.7264	5750	0.0	-
0.7327	5800	0.0	-
0.7390	5850	0.0	-
0.7453	5900	0.0	-
0.7516	5950	0.0	-
0.7580	6000	0.0	-
0.7643	6050	0.0	-
0.7706	6100	0.0	-
0.7769	6150	0.0	-
0.7832	6200	0.0	-
0.7895	6250	0.0	-
0.7959	6300	0.0	-
0.8022	6350	0.0	-
0.8085	6400	0.0	-
0.8148	6450	0.0	-
0.8211	6500	0.0	-
0.8274	6550	0.0	-
0.8338	6600	0.0	-
0.8401	6650	0.0	-
0.8464	6700	0.0	-
0.8527	6750	0.0	-
0.8590	6800	0.0	-
0.8653	6850	0.0	-
0.8717	6900	0.0	-
0.8780	6950	0.0	-
0.8843	7000	0.0	-
0.8906	7050	0.0	-
0.8969	7100	0.0	-
0.9032	7150	0.0	-
0.9096	7200	0.0	-
0.9159	7250	0.0	-
0.9222	7300	0.0	-
0.9285	7350	0.0	-
0.9348	7400	0.0	-
0.9411	7450	0.0	-
0.9474	7500	0.0	-
0.9538	7550	0.0	-
0.9601	7600	0.0	-
0.9664	7650	0.0	-
0.9727	7700	0.0	-
0.9790	7750	0.0	-
0.9853	7800	0.0	-
0.9917	7850	0.0	-
0.9980	7900	0.0	-
1.0	7916	-	0.0096
1.0043	7950	0.0	-
1.0106	8000	0.0	-
1.0169	8050	0.0	-
1.0232	8100	0.0	-
1.0296	8150	0.0	-
1.0359	8200	0.0	-
1.0422	8250	0.0	-
1.0485	8300	0.0	-
1.0548	8350	0.0	-
1.0611	8400	0.0	-
1.0675	8450	0.0	-
1.0738	8500	0.0	-
1.0801	8550	0.0	-
1.0864	8600	0.0	-
1.0927	8650	0.0	-
1.0990	8700	0.0	-
1.1054	8750	0.0	-
1.1117	8800	0.0	-
1.1180	8850	0.0	-
1.1243	8900	0.0	-
1.1306	8950	0.0	-
1.1369	9000	0.0	-
1.1433	9050	0.0	-
1.1496	9100	0.0	-
1.1559	9150	0.0	-
1.1622	9200	0.0	-
1.1685	9250	0.0	-
1.1748	9300	0.0	-
1.1812	9350	0.0	-
1.1875	9400	0.0	-
1.1938	9450	0.0	-
1.2001	9500	0.0	-
1.2064	9550	0.0	-
1.2127	9600	0.0	-
1.2191	9650	0.0	-
1.2254	9700	0.0	-
1.2317	9750	0.0	-
1.2380	9800	0.0	-
1.2443	9850	0.0	-
1.2506	9900	0.0	-
1.2569	9950	0.0	-
1.2633	10000	0.0	-
1.2696	10050	0.0	-
1.2759	10100	0.0	-
1.2822	10150	0.0	-
1.2885	10200	0.0	-
1.2948	10250	0.0	-
1.3012	10300	0.0	-
1.3075	10350	0.0	-
1.3138	10400	0.0	-
1.3201	10450	0.0	-
1.3264	10500	0.0	-
1.3327	10550	0.0	-
1.3391	10600	0.0	-
1.3454	10650	0.0	-
1.3517	10700	0.0	-
1.3580	10750	0.0	-
1.3643	10800	0.0	-
1.3706	10850	0.0	-
1.3770	10900	0.0	-
1.3833	10950	0.0	-
1.3896	11000	0.0	-
1.3959	11050	0.0	-
1.4022	11100	0.0	-
1.4085	11150	0.0	-
1.4149	11200	0.0	-
1.4212	11250	0.0	-
1.4275	11300	0.0	-
1.4338	11350	0.0	-
1.4401	11400	0.0	-
1.4464	11450	0.0	-
1.4528	11500	0.0	-
1.4591	11550	0.0	-
1.4654	11600	0.0	-
1.4717	11650	0.0	-
1.4780	11700	0.0	-
1.4843	11750	0.0	-
1.4907	11800	0.0	-
1.4970	11850	0.0	-
1.5033	11900	0.0	-
1.5096	11950	0.0	-
1.5159	12000	0.0	-
1.5222	12050	0.0	-
1.5285	12100	0.0	-
1.5349	12150	0.0	-
1.5412	12200	0.0	-
1.5475	12250	0.0	-
1.5538	12300	0.0	-
1.5601	12350	0.0	-
1.5664	12400	0.0	-
1.5728	12450	0.0	-
1.5791	12500	0.0	-
1.5854	12550	0.0	-
1.5917	12600	0.0	-
1.5980	12650	0.0	-
1.6043	12700	0.0	-
1.6107	12750	0.0	-
1.6170	12800	0.0	-
1.6233	12850	0.0	-
1.6296	12900	0.0	-
1.6359	12950	0.0	-
1.6422	13000	0.0	-
1.6486	13050	0.0	-
1.6549	13100	0.0	-
1.6612	13150	0.0	-
1.6675	13200	0.0	-
1.6738	13250	0.0	-
1.6801	13300	0.0	-
1.6865	13350	0.0	-
1.6928	13400	0.0	-
1.6991	13450	0.0	-
1.7054	13500	0.0	-
1.7117	13550	0.0	-
1.7180	13600	0.0	-
1.7244	13650	0.0	-
1.7307	13700	0.0	-
1.7370	13750	0.0	-
1.7433	13800	0.0	-
1.7496	13850	0.0	-
1.7559	13900	0.0	-
1.7623	13950	0.0	-
1.7686	14000	0.0	-
1.7749	14050	0.0	-
1.7812	14100	0.0	-
1.7875	14150	0.0	-
1.7938	14200	0.0	-
1.8002	14250	0.0	-
1.8065	14300	0.0	-
1.8128	14350	0.0	-
1.8191	14400	0.0	-
1.8254	14450	0.0	-
1.8317	14500	0.0	-
1.8380	14550	0.0	-
1.8444	14600	0.0	-
1.8507	14650	0.0	-
1.8570	14700	0.0	-
1.8633	14750	0.0	-
1.8696	14800	0.0	-
1.8759	14850	0.0	-
1.8823	14900	0.0	-
1.8886	14950	0.0	-
1.8949	15000	0.0	-
1.9012	15050	0.0	-
1.9075	15100	0.0	-
1.9138	15150	0.0	-
1.9202	15200	0.0	-
1.9265	15250	0.0	-
1.9328	15300	0.0	-
1.9391	15350	0.0	-
1.9454	15400	0.0	-
1.9517	15450	0.0	-
1.9581	15500	0.0	-
1.9644	15550	0.0	-
1.9707	15600	0.0	-
1.9770	15650	0.0	-
1.9833	15700	0.0	-
1.9896	15750	0.0	-
1.9960	15800	0.0	-
2.0	15832	-	0.0096
2.0023	15850	0.0	-
2.0086	15900	0.0	-
2.0149	15950	0.0	-
2.0212	16000	0.0	-
2.0275	16050	0.0	-
2.0339	16100	0.0	-
2.0402	16150	0.0	-
2.0465	16200	0.0	-
2.0528	16250	0.0	-
2.0591	16300	0.0	-
2.0654	16350	0.0	-
2.0718	16400	0.0	-
2.0781	16450	0.0	-
2.0844	16500	0.0	-
2.0907	16550	0.0	-
2.0970	16600	0.0	-
2.1033	16650	0.0	-
2.1097	16700	0.0	-
2.1160	16750	0.0	-
2.1223	16800	0.0	-
2.1286	16850	0.0	-
2.1349	16900	0.0	-
2.1412	16950	0.0	-
2.1475	17000	0.0	-
2.1539	17050	0.0	-
2.1602	17100	0.0	-
2.1665	17150	0.0	-
2.1728	17200	0.0	-
2.1791	17250	0.0	-
2.1854	17300	0.0	-
2.1918	17350	0.0	-
2.1981	17400	0.0	-
2.2044	17450	0.0	-
2.2107	17500	0.0	-
2.2170	17550	0.0	-
2.2233	17600	0.0	-
2.2297	17650	0.0	-
2.2360	17700	0.0	-
2.2423	17750	0.0	-
2.2486	17800	0.0	-
2.2549	17850	0.0	-
2.2612	17900	0.0	-
2.2676	17950	0.0	-
2.2739	18000	0.0	-
2.2802	18050	0.0	-
2.2865	18100	0.0	-
2.2928	18150	0.0	-
2.2991	18200	0.0	-
2.3055	18250	0.0	-
2.3118	18300	0.0	-
2.3181	18350	0.0	-
2.3244	18400	0.0	-
2.3307	18450	0.0	-
2.3370	18500	0.0	-
2.3434	18550	0.0	-
2.3497	18600	0.0	-
2.3560	18650	0.0	-
2.3623	18700	0.0	-
2.3686	18750	0.0	-
2.3749	18800	0.0	-
2.3813	18850	0.0	-
2.3876	18900	0.0	-
2.3939	18950	0.0	-
2.4002	19000	0.0	-
2.4065	19050	0.0	-
2.4128	19100	0.0	-
2.4192	19150	0.0	-
2.4255	19200	0.0	-
2.4318	19250	0.0	-
2.4381	19300	0.0	-
2.4444	19350	0.0	-
2.4507	19400	0.0	-
2.4570	19450	0.0	-
2.4634	19500	0.0	-
2.4697	19550	0.0	-
2.4760	19600	0.0	-
2.4823	19650	0.0	-
2.4886	19700	0.0	-
2.4949	19750	0.0	-
2.5013	19800	0.0	-
2.5076	19850	0.0	-
2.5139	19900	0.0	-
2.5202	19950	0.0	-
2.5265	20000	0.0	-
2.5328	20050	0.0	-
2.5392	20100	0.0	-
2.5455	20150	0.0	-
2.5518	20200	0.0	-
2.5581	20250	0.0	-
2.5644	20300	0.0	-
2.5707	20350	0.0	-
2.5771	20400	0.0	-
2.5834	20450	0.0	-
2.5897	20500	0.0	-
2.5960	20550	0.0	-
2.6023	20600	0.0	-
2.6086	20650	0.0	-
2.6150	20700	0.0	-
2.6213	20750	0.0	-
2.6276	20800	0.0	-
2.6339	20850	0.0	-
2.6402	20900	0.0	-
2.6465	20950	0.0	-
2.6529	21000	0.0	-
2.6592	21050	0.0	-
2.6655	21100	0.0	-
2.6718	21150	0.0	-
2.6781	21200	0.0	-
2.6844	21250	0.0	-
2.6908	21300	0.0	-
2.6971	21350	0.0	-
2.7034	21400	0.0	-
2.7097	21450	0.0	-
2.7160	21500	0.0	-
2.7223	21550	0.0	-
2.7287	21600	0.0	-
2.7350	21650	0.0	-
2.7413	21700	0.0	-
2.7476	21750	0.0	-
2.7539	21800	0.0	-
2.7602	21850	0.0	-
2.7665	21900	0.0	-
2.7729	21950	0.0	-
2.7792	22000	0.0	-
2.7855	22050	0.0	-
2.7918	22100	0.0	-
2.7981	22150	0.0	-
2.8044	22200	0.0	-
2.8108	22250	0.0	-
2.8171	22300	0.0	-
2.8234	22350	0.0	-
2.8297	22400	0.0	-
2.8360	22450	0.0	-
2.8423	22500	0.0	-
2.8487	22550	0.0	-
2.8550	22600	0.0	-
2.8613	22650	0.0	-
2.8676	22700	0.0	-
2.8739	22750	0.0	-
2.8802	22800	0.0	-
2.8866	22850	0.0	-
2.8929	22900	0.0	-
2.8992	22950	0.0	-
2.9055	23000	0.0	-
2.9118	23050	0.0	-
2.9181	23100	0.0	-
2.9245	23150	0.0	-
2.9308	23200	0.0	-
2.9371	23250	0.0	-
2.9434	23300	0.0	-
2.9497	23350	0.0	-
2.9560	23400	0.0	-
2.9624	23450	0.0	-
2.9687	23500	0.0	-
2.9750	23550	0.0	-
2.9813	23600	0.0	-
2.9876	23650	0.0	-
2.9939	23700	0.0	-
3.0	23748	-	0.0128
3.0003	23750	0.0	-
3.0066	23800	0.0	-
3.0129	23850	0.0	-
3.0192	23900	0.0	-
3.0255	23950	0.0	-
3.0318	24000	0.0	-
3.0382	24050	0.0	-
3.0445	24100	0.0	-
3.0508	24150	0.0	-
3.0571	24200	0.0	-
3.0634	24250	0.0	-
3.0697	24300	0.0	-
3.0760	24350	0.0	-
3.0824	24400	0.0	-
3.0887	24450	0.0	-
3.0950	24500	0.0	-
3.1013	24550	0.0	-
3.1076	24600	0.0	-
3.1139	24650	0.0	-
3.1203	24700	0.0	-
3.1266	24750	0.0	-
3.1329	24800	0.0	-
3.1392	24850	0.0	-
3.1455	24900	0.0	-
3.1518	24950	0.0	-
3.1582	25000	0.0	-
3.1645	25050	0.0	-
3.1708	25100	0.0	-
3.1771	25150	0.0	-
3.1834	25200	0.0	-
3.1897	25250	0.0	-
3.1961	25300	0.0	-
3.2024	25350	0.0	-
3.2087	25400	0.0	-
3.2150	25450	0.0	-
3.2213	25500	0.0	-
3.2276	25550	0.0	-
3.2340	25600	0.0	-
3.2403	25650	0.0	-
3.2466	25700	0.0	-
3.2529	25750	0.0	-
3.2592	25800	0.0	-
3.2655	25850	0.0	-
3.2719	25900	0.0	-
3.2782	25950	0.0	-
3.2845	26000	0.0	-
3.2908	26050	0.0	-
3.2971	26100	0.0	-
3.3034	26150	0.0	-
3.3098	26200	0.0	-
3.3161	26250	0.0	-
3.3224	26300	0.0	-
3.3287	26350	0.0	-
3.3350	26400	0.0	-
3.3413	26450	0.0	-
3.3477	26500	0.0	-
3.3540	26550	0.0	-
3.3603	26600	0.0	-
3.3666	26650	0.0	-
3.3729	26700	0.0	-
3.3792	26750	0.0	-
3.3855	26800	0.0	-
3.3919	26850	0.0	-
3.3982	26900	0.0	-
3.4045	26950	0.0	-
3.4108	27000	0.0	-
3.4171	27050	0.0	-
3.4234	27100	0.0	-
3.4298	27150	0.0	-
3.4361	27200	0.0	-
3.4424	27250	0.0	-
3.4487	27300	0.0	-
3.4550	27350	0.0	-
3.4613	27400	0.0	-
3.4677	27450	0.0	-
3.4740	27500	0.0	-
3.4803	27550	0.0	-
3.4866	27600	0.0	-
3.4929	27650	0.0	-
3.4992	27700	0.0	-
3.5056	27750	0.0	-
3.5119	27800	0.0	-
3.5182	27850	0.0	-
3.5245	27900	0.0	-
3.5308	27950	0.0	-
3.5371	28000	0.0	-
3.5435	28050	0.0	-
3.5498	28100	0.0	-
3.5561	28150	0.0	-
3.5624	28200	0.0	-
3.5687	28250	0.0	-
3.5750	28300	0.0	-
3.5814	28350	0.0	-
3.5877	28400	0.0	-
3.5940	28450	0.0	-
3.6003	28500	0.0	-
3.6066	28550	0.0	-
3.6129	28600	0.0	-
3.6193	28650	0.0	-
3.6256	28700	0.0	-
3.6319	28750	0.0	-
3.6382	28800	0.0	-
3.6445	28850	0.0	-
3.6508	28900	0.0	-
3.6572	28950	0.0	-
3.6635	29000	0.0	-
3.6698	29050	0.0	-
3.6761	29100	0.0	-
3.6824	29150	0.0	-
3.6887	29200	0.0	-
3.6950	29250	0.0	-
3.7014	29300	0.0	-
3.7077	29350	0.0	-
3.7140	29400	0.0	-
3.7203	29450	0.0	-
3.7266	29500	0.0	-
3.7329	29550	0.0	-
3.7393	29600	0.0	-
3.7456	29650	0.0	-
3.7519	29700	0.0	-
3.7582	29750	0.0	-
3.7645	29800	0.0	-
3.7708	29850	0.0	-
3.7772	29900	0.0	-
3.7835	29950	0.0	-
3.7898	30000	0.0	-
3.7961	30050	0.0	-
3.8024	30100	0.0	-
3.8087	30150	0.0	-
3.8151	30200	0.0	-
3.8214	30250	0.0	-
3.8277	30300	0.0	-
3.8340	30350	0.0	-
3.8403	30400	0.0	-
3.8466	30450	0.0	-
3.8530	30500	0.0	-
3.8593	30550	0.0	-
3.8656	30600	0.0	-
3.8719	30650	0.0	-
3.8782	30700	0.0	-
3.8845	30750	0.0	-
3.8909	30800	0.0	-
3.8972	30850	0.0	-
3.9035	30900	0.0	-
3.9098	30950	0.0	-
3.9161	31000	0.0	-
3.9224	31050	0.0	-
3.9288	31100	0.0	-
3.9351	31150	0.0	-
3.9414	31200	0.0	-
3.9477	31250	0.0	-
3.9540	31300	0.0	-
3.9603	31350	0.0	-
3.9666	31400	0.0	-
3.9730	31450	0.0	-
3.9793	31500	0.0	-
3.9856	31550	0.0	-
3.9919	31600	0.0	-
3.9982	31650	0.0	-
4.0	31664	-	0.0117

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.9.18
SetFit: 1.0.3
Sentence Transformers: 2.2.1
Transformers: 4.32.1
PyTorch: 1.10.0
Datasets: 2.20.0
Tokenizers: 0.13.3

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}
}

gsjang/sroberta-embedding