metadata

base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      What tables are included in the starhub_data_asset database that relate to
      customer complaints?
  - text: What are the tables that I can access in the starhub_data_asset database?
  - text: Can I have avg Cost_Efficiency
  - text: Analyze product category revenue impact.
  - text: Retrieve data_asset_kpi_ma_product details.
inference: true
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9914529914529915
            name: Accuracy

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 Type: SetFit
Sentence Transformer body: BAAI/bge-small-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 7 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
Generalreply	'Can you recommend a good movie to watch?' "Oh, that's a tough one! There are so many good memories to choose from. But if I had to pick just one, I think it would be spending summers at my grandparent's house. We would play board games, make homemade ice cream, and have big family dinners. It was always so much fun!" 'Oh, I love reading books! My favorite genre is definitely fantasy. How about you? What kind of books do you like to read?'
Lookup_1	'Get me data_asset_kpi_cf cash flow.' 'Display data_asset_001_pcc for electronics category.' 'Calculate Gross Profit Margin Trends.'
Lookup	"What are the products in the 'Clothing' category?" "Get me the phone numbers of customers with the last name 'Johnson'." "Can you filter by employees who have the last name 'Brown'?"
Aggregation	'Get me max Accumulated Amortisation and Impairment.' 'Can I have mode of Revenue' 'Show me count company_name'
Tablejoin	'Could you merge the Orders and Employees tables to identify which employees have processed high-value orders?' 'Could you connect the Products and Orders tables to analyze sales data by product category?' 'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'
Viewtables	'What are the tables in the starhub_data_asset database that a user can join to perform a sales analysis?' 'What tables can be found in the asset-tracking section of the starhub_data_asset database?' 'What tables exist in the starhub_data_asset database?'
Rejection	"Let's avoid creating any new data sets." "I'd prefer to avoid generating data fields." "I'm not interested in filtering this collection."

Evaluation

Metrics

Label	Accuracy
all	0.9915

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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10")
# Run inference
preds = model("Can I have avg Cost_Efficiency")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	8.6563	62

Label	Training Sample Count
Tablejoin	129
Rejection	77
Aggregation	282
Lookup	60
Generalreply	63
Viewtables	74
Lookup_1	150

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
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
0.0000	1	0.2038	-
0.0014	50	0.2019	-
0.0029	100	0.1983	-
0.0043	150	0.206	-
0.0057	200	0.2268	-
0.0071	250	0.2025	-
0.0086	300	0.2041	-
0.0100	350	0.1426	-
0.0114	400	0.1513	-
0.0129	450	0.1215	-
0.0143	500	0.1426	-
0.0157	550	0.0859	-
0.0172	600	0.0486	-
0.0186	650	0.0378	-
0.0200	700	0.0519	-
0.0214	750	0.0717	-
0.0229	800	0.1161	-
0.0243	850	0.0771	-
0.0257	900	0.074	-
0.0272	950	0.0567	-
0.0286	1000	0.0223	-
0.0300	1050	0.0266	-
0.0315	1100	0.0261	-
0.0329	1150	0.0333	-
0.0343	1200	0.0107	-
0.0357	1250	0.0123	-
0.0372	1300	0.0193	-
0.0386	1350	0.0039	-
0.0400	1400	0.0079	-
0.0415	1450	0.0035	-
0.0429	1500	0.003	-
0.0443	1550	0.0041	-
0.0457	1600	0.0038	-
0.0472	1650	0.002	-
0.0486	1700	0.0028	-
0.0500	1750	0.0056	-
0.0515	1800	0.0035	-
0.0529	1850	0.0027	-
0.0543	1900	0.0028	-
0.0558	1950	0.0028	-
0.0572	2000	0.0019	-
0.0586	2050	0.0046	-
0.0600	2100	0.0017	-
0.0615	2150	0.0016	-
0.0629	2200	0.0022	-
0.0643	2250	0.002	-
0.0658	2300	0.0029	-
0.0672	2350	0.0032	-
0.0686	2400	0.0018	-
0.0701	2450	0.0015	-
0.0715	2500	0.0015	-
0.0729	2550	0.0016	-
0.0743	2600	0.0012	-
0.0758	2650	0.0014	-
0.0772	2700	0.0015	-
0.0786	2750	0.0018	-
0.0801	2800	0.0012	-
0.0815	2850	0.0009	-
0.0829	2900	0.001	-
0.0843	2950	0.0011	-
0.0858	3000	0.0011	-
0.0872	3050	0.001	-
0.0886	3100	0.0012	-
0.0901	3150	0.0006	-
0.0915	3200	0.0013	-
0.0929	3250	0.0007	-
0.0944	3300	0.0007	-
0.0958	3350	0.0009	-
0.0972	3400	0.0008	-
0.0986	3450	0.0005	-
0.1001	3500	0.001	-
0.1015	3550	0.001	-
0.1029	3600	0.0008	-
0.1044	3650	0.0007	-
0.1058	3700	0.0006	-
0.1072	3750	0.0009	-
0.1086	3800	0.0012	-
0.1101	3850	0.0007	-
0.1115	3900	0.0008	-
0.1129	3950	0.0009	-
0.1144	4000	0.0007	-
0.1158	4050	0.0007	-
0.1172	4100	0.0007	-
0.1187	4150	0.0006	-
0.1201	4200	0.0006	-
0.1215	4250	0.0011	-
0.1229	4300	0.0012	-
0.1244	4350	0.0007	-
0.1258	4400	0.0007	-
0.1272	4450	0.0006	-
0.1287	4500	0.0005	-
0.1301	4550	0.0008	-
0.1315	4600	0.0006	-
0.1330	4650	0.0007	-
0.1344	4700	0.0006	-
0.1358	4750	0.0005	-
0.1372	4800	0.0006	-
0.1387	4850	0.0008	-
0.1401	4900	0.0008	-
0.1415	4950	0.0004	-
0.1430	5000	0.0005	-
0.1444	5050	0.0005	-
0.1458	5100	0.0007	-
0.1472	5150	0.0006	-
0.1487	5200	0.0006	-
0.1501	5250	0.0004	-
0.1515	5300	0.0005	-
0.1530	5350	0.0007	-
0.1544	5400	0.0007	-
0.1558	5450	0.0005	-
0.1573	5500	0.0007	-
0.1587	5550	0.0004	-
0.1601	5600	0.0004	-
0.1615	5650	0.0006	-
0.1630	5700	0.0005	-
0.1644	5750	0.0006	-
0.1658	5800	0.0004	-
0.1673	5850	0.0005	-
0.1687	5900	0.0007	-
0.1701	5950	0.0005	-
0.1716	6000	0.0005	-
0.1730	6050	0.0003	-
0.1744	6100	0.0003	-
0.1758	6150	0.0005	-
0.1773	6200	0.0007	-
0.1787	6250	0.0004	-
0.1801	6300	0.0006	-
0.1816	6350	0.0004	-
0.1830	6400	0.0003	-
0.1844	6450	0.0005	-
0.1858	6500	0.0004	-
0.1873	6550	0.0006	-
0.1887	6600	0.0005	-
0.1901	6650	0.0005	-
0.1916	6700	0.0003	-
0.1930	6750	0.0004	-
0.1944	6800	0.0004	-
0.1959	6850	0.0004	-
0.1973	6900	0.0003	-
0.1987	6950	0.0004	-
0.2001	7000	0.0004	-
0.2016	7050	0.0003	-
0.2030	7100	0.0003	-
0.2044	7150	0.0005	-
0.2059	7200	0.0004	-
0.2073	7250	0.0003	-
0.2087	7300	0.0002	-
0.2102	7350	0.0003	-
0.2116	7400	0.0004	-
0.2130	7450	0.0006	-
0.2144	7500	0.0003	-
0.2159	7550	0.0002	-
0.2173	7600	0.0004	-
0.2187	7650	0.0003	-
0.2202	7700	0.0005	-
0.2216	7750	0.0004	-
0.2230	7800	0.0004	-
0.2244	7850	0.0004	-
0.2259	7900	0.0003	-
0.2273	7950	0.0005	-
0.2287	8000	0.0003	-
0.2302	8050	0.0003	-
0.2316	8100	0.0003	-
0.2330	8150	0.0002	-
0.2345	8200	0.0002	-
0.2359	8250	0.0004	-
0.2373	8300	0.0004	-
0.2387	8350	0.0004	-
0.2402	8400	0.0003	-
0.2416	8450	0.0002	-
0.2430	8500	0.0002	-
0.2445	8550	0.0003	-
0.2459	8600	0.0004	-
0.2473	8650	0.0004	-
0.2487	8700	0.0003	-
0.2502	8750	0.0002	-
0.2516	8800	0.0003	-
0.2530	8850	0.0003	-
0.2545	8900	0.0004	-
0.2559	8950	0.0003	-
0.2573	9000	0.0002	-
0.2588	9050	0.0003	-
0.2602	9100	0.0003	-
0.2616	9150	0.0003	-
0.2630	9200	0.0003	-
0.2645	9250	0.0002	-
0.2659	9300	0.0002	-
0.2673	9350	0.0003	-
0.2688	9400	0.0552	-
0.2702	9450	0.0003	-
0.2716	9500	0.0003	-
0.2731	9550	0.0004	-
0.2745	9600	0.0004	-
0.2759	9650	0.0005	-
0.2773	9700	0.0003	-
0.2788	9750	0.0003	-
0.2802	9800	0.0003	-
0.2816	9850	0.0003	-
0.2831	9900	0.0004	-
0.2845	9950	0.0003	-
0.2859	10000	0.0003	-
0.2873	10050	0.0004	-
0.2888	10100	0.0005	-
0.2902	10150	0.0003	-
0.2916	10200	0.0004	-
0.2931	10250	0.0002	-
0.2945	10300	0.0005	-
0.2959	10350	0.0003	-
0.2974	10400	0.0003	-
0.2988	10450	0.0002	-
0.3002	10500	0.0003	-
0.3016	10550	0.0004	-
0.3031	10600	0.0003	-
0.3045	10650	0.0003	-
0.3059	10700	0.0004	-
0.3074	10750	0.0003	-
0.3088	10800	0.0003	-
0.3102	10850	0.0003	-
0.3117	10900	0.0002	-
0.3131	10950	0.0005	-
0.3145	11000	0.0003	-
0.3159	11050	0.0002	-
0.3174	11100	0.0003	-
0.3188	11150	0.0004	-
0.3202	11200	0.0004	-
0.3217	11250	0.0002	-
0.3231	11300	0.0003	-
0.3245	11350	0.0003	-
0.3259	11400	0.0003	-
0.3274	11450	0.0004	-
0.3288	11500	0.0004	-
0.3302	11550	0.0003	-
0.3317	11600	0.0003	-
0.3331	11650	0.0002	-
0.3345	11700	0.0004	-
0.3360	11750	0.0002	-
0.3374	11800	0.0003	-
0.3388	11850	0.0002	-
0.3402	11900	0.0003	-
0.3417	11950	0.0002	-
0.3431	12000	0.0004	-
0.3445	12050	0.0003	-
0.3460	12100	0.0004	-
0.3474	12150	0.0005	-
0.3488	12200	0.0004	-
0.3503	12250	0.0004	-
0.3517	12300	0.0002	-
0.3531	12350	0.0002	-
0.3545	12400	0.0004	-
0.3560	12450	0.0002	-
0.3574	12500	0.0002	-
0.3588	12550	0.0003	-
0.3603	12600	0.0005	-
0.3617	12650	0.0003	-
0.3631	12700	0.0003	-
0.3645	12750	0.0002	-
0.3660	12800	0.0003	-
0.3674	12850	0.0002	-
0.3688	12900	0.0002	-
0.3703	12950	0.0001	-
0.3717	13000	0.0002	-
0.3731	13050	0.0003	-
0.3746	13100	0.0003	-
0.3760	13150	0.0002	-
0.3774	13200	0.0004	-
0.3788	13250	0.0003	-
0.3803	13300	0.0002	-
0.3817	13350	0.0003	-
0.3831	13400	0.0003	-
0.3846	13450	0.0003	-
0.3860	13500	0.0002	-
0.3874	13550	0.0002	-
0.3888	13600	0.0003	-
0.3903	13650	0.0003	-
0.3917	13700	0.0002	-
0.3931	13750	0.0002	-
0.3946	13800	0.0002	-
0.3960	13850	0.0004	-
0.3974	13900	0.0003	-
0.3989	13950	0.0002	-
0.4003	14000	0.0003	-
0.4017	14050	0.0001	-
0.4031	14100	0.0002	-
0.4046	14150	0.0001	-
0.4060	14200	0.0002	-
0.4074	14250	0.0002	-
0.4089	14300	0.0002	-
0.4103	14350	0.0003	-
0.4117	14400	0.0003	-
0.4132	14450	0.0002	-
0.4146	14500	0.0003	-
0.4160	14550	0.0003	-
0.4174	14600	0.0002	-
0.4189	14650	0.0002	-
0.4203	14700	0.0003	-
0.4217	14750	0.0003	-
0.4232	14800	0.0002	-
0.4246	14850	0.0003	-
0.4260	14900	0.0003	-
0.4274	14950	0.0003	-
0.4289	15000	0.0002	-
0.4303	15050	0.0002	-
0.4317	15100	0.0002	-
0.4332	15150	0.0004	-
0.4346	15200	0.0003	-
0.4360	15250	0.0001	-
0.4375	15300	0.0002	-
0.4389	15350	0.0001	-
0.4403	15400	0.0002	-
0.4417	15450	0.0001	-
0.4432	15500	0.0002	-
0.4446	15550	0.0002	-
0.4460	15600	0.0002	-
0.4475	15650	0.0002	-
0.4489	15700	0.0003	-
0.4503	15750	0.0002	-
0.4518	15800	0.0002	-
0.4532	15850	0.0003	-
0.4546	15900	0.0003	-
0.4560	15950	0.0002	-
0.4575	16000	0.0002	-
0.4589	16050	0.0002	-
0.4603	16100	0.0003	-
0.4618	16150	0.0002	-
0.4632	16200	0.0003	-
0.4646	16250	0.0002	-
0.4660	16300	0.0002	-
0.4675	16350	0.0002	-
0.4689	16400	0.0002	-
0.4703	16450	0.0002	-
0.4718	16500	0.0002	-
0.4732	16550	0.0002	-
0.4746	16600	0.0003	-
0.4761	16650	0.0002	-
0.4775	16700	0.0002	-
0.4789	16750	0.0002	-
0.4803	16800	0.0002	-
0.4818	16850	0.0001	-
0.4832	16900	0.0003	-
0.4846	16950	0.0002	-
0.4861	17000	0.0002	-
0.4875	17050	0.0002	-
0.4889	17100	0.0002	-
0.4904	17150	0.0002	-
0.4918	17200	0.0002	-
0.4932	17250	0.0002	-
0.4946	17300	0.0002	-
0.4961	17350	0.0002	-
0.4975	17400	0.0002	-
0.4989	17450	0.0001	-
0.5004	17500	0.0001	-
0.5018	17550	0.0002	-
0.5032	17600	0.0002	-
0.5046	17650	0.0002	-
0.5061	17700	0.0002	-
0.5075	17750	0.0002	-
0.5089	17800	0.0002	-
0.5104	17850	0.0002	-
0.5118	17900	0.0002	-
0.5132	17950	0.0003	-
0.5147	18000	0.0002	-
0.5161	18050	0.0002	-
0.5175	18100	0.0002	-
0.5189	18150	0.0002	-
0.5204	18200	0.0002	-
0.5218	18250	0.0002	-
0.5232	18300	0.0002	-
0.5247	18350	0.0002	-
0.5261	18400	0.0002	-
0.5275	18450	0.0003	-
0.5289	18500	0.0001	-
0.5304	18550	0.0002	-
0.5318	18600	0.0001	-
0.5332	18650	0.0002	-
0.5347	18700	0.0002	-
0.5361	18750	0.0002	-
0.5375	18800	0.0002	-
0.5390	18850	0.0001	-
0.5404	18900	0.0001	-
0.5418	18950	0.0001	-
0.5432	19000	0.0002	-
0.5447	19050	0.0002	-
0.5461	19100	0.0002	-
0.5475	19150	0.0002	-
0.5490	19200	0.0002	-
0.5504	19250	0.0002	-
0.5518	19300	0.0001	-
0.5533	19350	0.0002	-
0.5547	19400	0.0002	-
0.5561	19450	0.0004	-
0.5575	19500	0.0002	-
0.5590	19550	0.0002	-
0.5604	19600	0.0003	-
0.5618	19650	0.0003	-
0.5633	19700	0.0002	-
0.5647	19750	0.0002	-
0.5661	19800	0.0001	-
0.5675	19850	0.0003	-
0.5690	19900	0.0002	-
0.5704	19950	0.0002	-
0.5718	20000	0.0001	-
0.5733	20050	0.0003	-
0.5747	20100	0.0001	-
0.5761	20150	0.0002	-
0.5776	20200	0.0003	-
0.5790	20250	0.0003	-
0.5804	20300	0.0002	-
0.5818	20350	0.0003	-
0.5833	20400	0.0002	-
0.5847	20450	0.0002	-
0.5861	20500	0.0002	-
0.5876	20550	0.0001	-
0.5890	20600	0.0002	-
0.5904	20650	0.0002	-
0.5919	20700	0.0002	-
0.5933	20750	0.0002	-
0.5947	20800	0.0001	-
0.5961	20850	0.0001	-
0.5976	20900	0.0001	-
0.5990	20950	0.0001	-
0.6004	21000	0.0002	-
0.6019	21050	0.0001	-
0.6033	21100	0.0002	-
0.6047	21150	0.0001	-
0.6061	21200	0.0002	-
0.6076	21250	0.0002	-
0.6090	21300	0.0001	-
0.6104	21350	0.0002	-
0.6119	21400	0.0001	-
0.6133	21450	0.0002	-
0.6147	21500	0.0001	-
0.6162	21550	0.0002	-
0.6176	21600	0.0001	-
0.6190	21650	0.0001	-
0.6204	21700	0.0001	-
0.6219	21750	0.0002	-
0.6233	21800	0.0001	-
0.6247	21850	0.0001	-
0.6262	21900	0.0001	-
0.6276	21950	0.0002	-
0.6290	22000	0.0002	-
0.6305	22050	0.0001	-
0.6319	22100	0.0002	-
0.6333	22150	0.0001	-
0.6347	22200	0.0001	-
0.6362	22250	0.0001	-
0.6376	22300	0.0002	-
0.6390	22350	0.0001	-
0.6405	22400	0.0003	-
0.6419	22450	0.0002	-
0.6433	22500	0.0002	-
0.6447	22550	0.0001	-
0.6462	22600	0.0002	-
0.6476	22650	0.0002	-
0.6490	22700	0.0002	-
0.6505	22750	0.0002	-
0.6519	22800	0.0001	-
0.6533	22850	0.0002	-
0.6548	22900	0.0002	-
0.6562	22950	0.0002	-
0.6576	23000	0.0002	-
0.6590	23050	0.0002	-
0.6605	23100	0.0002	-
0.6619	23150	0.0002	-
0.6633	23200	0.0002	-
0.6648	23250	0.0002	-
0.6662	23300	0.0002	-
0.6676	23350	0.0001	-
0.6690	23400	0.0002	-
0.6705	23450	0.0002	-
0.6719	23500	0.0001	-
0.6733	23550	0.0002	-
0.6748	23600	0.0001	-
0.6762	23650	0.0002	-
0.6776	23700	0.0002	-
0.6791	23750	0.0002	-
0.6805	23800	0.0001	-
0.6819	23850	0.0002	-
0.6833	23900	0.0003	-
0.6848	23950	0.0002	-
0.6862	24000	0.0002	-
0.6876	24050	0.0001	-
0.6891	24100	0.0002	-
0.6905	24150	0.0001	-
0.6919	24200	0.0003	-
0.6934	24250	0.0002	-
0.6948	24300	0.0001	-
0.6962	24350	0.0001	-
0.6976	24400	0.0001	-
0.6991	24450	0.0001	-
0.7005	24500	0.0001	-
0.7019	24550	0.0002	-
0.7034	24600	0.0001	-
0.7048	24650	0.0002	-
0.7062	24700	0.0001	-
0.7076	24750	0.0002	-
0.7091	24800	0.0002	-
0.7105	24850	0.0002	-
0.7119	24900	0.0002	-
0.7134	24950	0.0001	-
0.7148	25000	0.0002	-
0.7162	25050	0.0001	-
0.7177	25100	0.0002	-
0.7191	25150	0.0001	-
0.7205	25200	0.0001	-
0.7219	25250	0.0002	-
0.7234	25300	0.0002	-
0.7248	25350	0.0002	-
0.7262	25400	0.0001	-
0.7277	25450	0.0002	-
0.7291	25500	0.0002	-
0.7305	25550	0.0002	-
0.7320	25600	0.0001	-
0.7334	25650	0.0002	-
0.7348	25700	0.0002	-
0.7362	25750	0.0002	-
0.7377	25800	0.0002	-
0.7391	25850	0.0001	-
0.7405	25900	0.0002	-
0.7420	25950	0.0002	-
0.7434	26000	0.0001	-
0.7448	26050	0.0001	-
0.7462	26100	0.0001	-
0.7477	26150	0.0001	-
0.7491	26200	0.0001	-
0.7505	26250	0.0002	-
0.7520	26300	0.0001	-
0.7534	26350	0.0001	-
0.7548	26400	0.0001	-
0.7563	26450	0.0002	-
0.7577	26500	0.0001	-
0.7591	26550	0.0002	-
0.7605	26600	0.0003	-
0.7620	26650	0.0002	-
0.7634	26700	0.0002	-
0.7648	26750	0.0001	-
0.7663	26800	0.0001	-
0.7677	26850	0.0002	-
0.7691	26900	0.0002	-
0.7706	26950	0.0001	-
0.7720	27000	0.0001	-
0.7734	27050	0.0001	-
0.7748	27100	0.0001	-
0.7763	27150	0.0001	-
0.7777	27200	0.0002	-
0.7791	27250	0.0001	-
0.7806	27300	0.0001	-
0.7820	27350	0.0001	-
0.7834	27400	0.0002	-
0.7848	27450	0.0001	-
0.7863	27500	0.0001	-
0.7877	27550	0.0001	-
0.7891	27600	0.0001	-
0.7906	27650	0.0001	-
0.7920	27700	0.0001	-
0.7934	27750	0.0001	-
0.7949	27800	0.0001	-
0.7963	27850	0.0001	-
0.7977	27900	0.0001	-
0.7991	27950	0.0003	-
0.8006	28000	0.0001	-
0.8020	28050	0.0002	-
0.8034	28100	0.0001	-
0.8049	28150	0.0002	-
0.8063	28200	0.0	-
0.8077	28250	0.0001	-
0.8091	28300	0.0001	-
0.8106	28350	0.0001	-
0.8120	28400	0.0001	-
0.8134	28450	0.0002	-
0.8149	28500	0.0001	-
0.8163	28550	0.0001	-
0.8177	28600	0.0001	-
0.8192	28650	0.0001	-
0.8206	28700	0.0001	-
0.8220	28750	0.0002	-
0.8234	28800	0.0002	-
0.8249	28850	0.0002	-
0.8263	28900	0.0001	-
0.8277	28950	0.0002	-
0.8292	29000	0.0001	-
0.8306	29050	0.0002	-
0.8320	29100	0.0001	-
0.8335	29150	0.0001	-
0.8349	29200	0.0001	-
0.8363	29250	0.0001	-
0.8377	29300	0.0001	-
0.8392	29350	0.0001	-
0.8406	29400	0.0001	-
0.8420	29450	0.0002	-
0.8435	29500	0.0001	-
0.8449	29550	0.0001	-
0.8463	29600	0.0001	-
0.8477	29650	0.0001	-
0.8492	29700	0.0001	-
0.8506	29750	0.0002	-
0.8520	29800	0.0002	-
0.8535	29850	0.0001	-
0.8549	29900	0.0002	-
0.8563	29950	0.0002	-
0.8578	30000	0.0002	-
0.8592	30050	0.0001	-
0.8606	30100	0.0002	-
0.8620	30150	0.0002	-
0.8635	30200	0.0003	-
0.8649	30250	0.0001	-
0.8663	30300	0.0001	-
0.8678	30350	0.0001	-
0.8692	30400	0.0001	-
0.8706	30450	0.0002	-
0.8721	30500	0.0001	-
0.8735	30550	0.0001	-
0.8749	30600	0.0001	-
0.8763	30650	0.0002	-
0.8778	30700	0.0002	-
0.8792	30750	0.0001	-
0.8806	30800	0.0002	-
0.8821	30850	0.0002	-
0.8835	30900	0.0001	-
0.8849	30950	0.0002	-
0.8863	31000	0.0002	-
0.8878	31050	0.0002	-
0.8892	31100	0.0001	-
0.8906	31150	0.0001	-
0.8921	31200	0.0001	-
0.8935	31250	0.0001	-
0.8949	31300	0.0002	-
0.8964	31350	0.0002	-
0.8978	31400	0.0001	-
0.8992	31450	0.0001	-
0.9006	31500	0.0002	-
0.9021	31550	0.0002	-
0.9035	31600	0.0001	-
0.9049	31650	0.0002	-
0.9064	31700	0.0001	-
0.9078	31750	0.0001	-
0.9092	31800	0.0001	-
0.9107	31850	0.0002	-
0.9121	31900	0.0002	-
0.9135	31950	0.0001	-
0.9149	32000	0.0001	-
0.9164	32050	0.0001	-
0.9178	32100	0.0001	-
0.9192	32150	0.0001	-
0.9207	32200	0.0001	-
0.9221	32250	0.0001	-
0.9235	32300	0.0002	-
0.9249	32350	0.0001	-
0.9264	32400	0.0001	-
0.9278	32450	0.0002	-
0.9292	32500	0.0001	-
0.9307	32550	0.0001	-
0.9321	32600	0.0002	-
0.9335	32650	0.0001	-
0.9350	32700	0.0001	-
0.9364	32750	0.0001	-
0.9378	32800	0.0001	-
0.9392	32850	0.0001	-
0.9407	32900	0.0002	-
0.9421	32950	0.0002	-
0.9435	33000	0.0	-
0.9450	33050	0.0001	-
0.9464	33100	0.0001	-
0.9478	33150	0.0001	-
0.9492	33200	0.0001	-
0.9507	33250	0.0001	-
0.9521	33300	0.0001	-
0.9535	33350	0.0002	-
0.9550	33400	0.0001	-
0.9564	33450	0.0001	-
0.9578	33500	0.0002	-
0.9593	33550	0.0001	-
0.9607	33600	0.0001	-
0.9621	33650	0.0002	-
0.9635	33700	0.0002	-
0.9650	33750	0.0001	-
0.9664	33800	0.0001	-
0.9678	33850	0.0001	-
0.9693	33900	0.0001	-
0.9707	33950	0.0	-
0.9721	34000	0.0002	-
0.9736	34050	0.0001	-
0.9750	34100	0.0001	-
0.9764	34150	0.0001	-
0.9778	34200	0.0001	-
0.9793	34250	0.0002	-
0.9807	34300	0.0002	-
0.9821	34350	0.0001	-
0.9836	34400	0.0001	-
0.9850	34450	0.0001	-
0.9864	34500	0.0001	-
0.9878	34550	0.0001	-
0.9893	34600	0.0001	-
0.9907	34650	0.0001	-
0.9921	34700	0.0001	-
0.9936	34750	0.0001	-
0.9950	34800	0.0001	-
0.9964	34850	0.0001	-
0.9979	34900	0.0002	-
0.9993	34950	0.0002	-
1.0	34975	-	0.0221

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.42.4
PyTorch: 2.4.0+cu121
Datasets: 2.21.0
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}
}