metadata

base_model: BAAI/bge-large-en-v1.5
datasets:
  - nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Can you filter by the 'Fashion' category and show me the products
      available?
  - text: Get forecast by service type.
  - text: How many orders were placed in each quarter?
  - text: What are the details of customers with no phone number listed?
  - text: I don't want to filter the database currently.
inference: true
model-index:
  - name: SetFit with BAAI/bge-large-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
          type: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
          split: test
        metrics:
          - type: accuracy
            value: 0.9739130434782609
            name: Accuracy

SetFit with BAAI/bge-large-en-v1.5

This is a SetFit model trained on the nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed dataset that can be used for Text Classification. This SetFit model uses BAAI/bge-large-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-large-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 7 classes
Training Dataset: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed

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
Aggregation	'How many unique customers made purchases last year?' 'Determine the minimum order amount for each customer.' 'Get me sum of total_revenue.'
Tablejoin	'Show me a join of cash flow and variance.' 'Join data_asset_001_forecast with data_asset_kpi_bs tables.' 'Join data_asset_kpi_ma_product with data_asset_001_variance.'
Lookup_1	'Show me asset impairment by year.' 'Get me data_asset_001_pcc group by category.' 'Show me data_asset_001_variance group by category.'
Viewtables	'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?' 'How can I access the table directory for starhub_data_asset database to view all the available tables?' 'Please show me the tables that contain data related to customer transactions present in the starhub_data_asset database.'
Generalreply	"Oh my favorite food? That's a tough one. I love so many different kinds of food, but if I had to choose one it would probably be pizza. What about you? What's your favorite food?" "Hmm, let me think... I'm actually pretty good at playing guitar! I've been playing for a few years now and it's always been one of my favorite hobbies. How about you, do you play any instruments or have any interesting hobbies?" 'What is your favorite color?'
Lookup	"Get me all the customers who haven't placed any orders." 'Get me the list of customers who have a phone number listed.' 'Can you filter by customers who registered without an email address?'
Rejection	"I'm not keen on producing any new data sets." "Please don't generate any new data." "I don't want to create any new data outputs."

Evaluation

Metrics

Label	Accuracy
all	0.9739

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-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch")
# Run inference
preds = model("Get forecast by service type.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	8.8252	62

Label	Training Sample Count
Tablejoin	129
Rejection	74
Aggregation	210
Lookup	60
Generalreply	59
Viewtables	75
Lookup_1	217

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	0.1706	-
0.0014	50	0.1976	-
0.0029	100	0.2045	-
0.0043	150	0.1846	-
0.0058	200	0.1608	-
0.0072	250	0.105	-
0.0087	300	0.1618	-
0.0101	350	0.1282	-
0.0116	400	0.0382	-
0.0130	450	0.0328	-
0.0145	500	0.0483	-
0.0159	550	0.0245	-
0.0174	600	0.0093	-
0.0188	650	0.0084	-
0.0203	700	0.0042	-
0.0217	750	0.0044	-
0.0231	800	0.0035	-
0.0246	850	0.0065	-
0.0260	900	0.0036	-
0.0275	950	0.0039	-
0.0289	1000	0.0037	-
0.0304	1050	0.005	-
0.0318	1100	0.0024	-
0.0333	1150	0.0023	-
0.0347	1200	0.0023	-
0.0362	1250	0.0019	-
0.0376	1300	0.0015	-
0.0391	1350	0.0023	-
0.0405	1400	0.0011	-
0.0420	1450	0.0017	-
0.0434	1500	0.0015	-
0.0448	1550	0.0014	-
0.0463	1600	0.0014	-
0.0477	1650	0.0013	-
0.0492	1700	0.0013	-
0.0506	1750	0.001	-
0.0521	1800	0.0013	-
0.0535	1850	0.0013	-
0.0550	1900	0.0011	-
0.0564	1950	0.0012	-
0.0579	2000	0.001	-
0.0593	2050	0.0012	-
0.0608	2100	0.0008	-
0.0622	2150	0.0008	-
0.0637	2200	0.001	-
0.0651	2250	0.0007	-
0.0665	2300	0.0006	-
0.0680	2350	0.0007	-
0.0694	2400	0.0008	-
0.0709	2450	0.0008	-
0.0723	2500	0.0006	-
0.0738	2550	0.0006	-
0.0752	2600	0.0007	-
0.0767	2650	0.0008	-
0.0781	2700	0.0005	-
0.0796	2750	0.0008	-
0.0810	2800	0.0006	-
0.0825	2850	0.0007	-
0.0839	2900	0.0007	-
0.0854	2950	0.0005	-
0.0868	3000	0.0007	-
0.0882	3050	0.0005	-
0.0897	3100	0.0005	-
0.0911	3150	0.0007	-
0.0926	3200	0.0005	-
0.0940	3250	0.0005	-
0.0955	3300	0.0007	-
0.0969	3350	0.0004	-
0.0984	3400	0.0005	-
0.0998	3450	0.0004	-
0.1013	3500	0.0007	-
0.1027	3550	0.0004	-
0.1042	3600	0.0004	-
0.1056	3650	0.0006	-
0.1071	3700	0.0005	-
0.1085	3750	0.0004	-
0.1100	3800	0.0005	-
0.1114	3850	0.0004	-
0.1128	3900	0.0004	-
0.1143	3950	0.0003	-
0.1157	4000	0.0004	-
0.1172	4050	0.0004	-
0.1186	4100	0.0004	-
0.1201	4150	0.0004	-
0.1215	4200	0.0004	-
0.1230	4250	0.0004	-
0.1244	4300	0.0003	-
0.1259	4350	0.0004	-
0.1273	4400	0.0003	-
0.1288	4450	0.0003	-
0.1302	4500	0.0003	-
0.1317	4550	0.0002	-
0.1331	4600	0.0003	-
0.1345	4650	0.0004	-
0.1360	4700	0.0003	-
0.1374	4750	0.0003	-
0.1389	4800	0.0002	-
0.1403	4850	0.0003	-
0.1418	4900	0.0003	-
0.1432	4950	0.0003	-
0.1447	5000	0.0002	-
0.1461	5050	0.0002	-
0.1476	5100	0.0003	-
0.1490	5150	0.0002	-
0.1505	5200	0.0004	-
0.1519	5250	0.0003	-
0.1534	5300	0.0003	-
0.1548	5350	0.0002	-
0.1562	5400	0.0003	-
0.1577	5450	0.0002	-
0.1591	5500	0.0002	-
0.1606	5550	0.0002	-
0.1620	5600	0.0002	-
0.1635	5650	0.0002	-
0.1649	5700	0.0003	-
0.1664	5750	0.0002	-
0.1678	5800	0.0003	-
0.1693	5850	0.0003	-
0.1707	5900	0.0002	-
0.1722	5950	0.0007	-
0.1736	6000	0.0003	-
0.1751	6050	0.0002	-
0.1765	6100	0.0002	-
0.1779	6150	0.0003	-
0.1794	6200	0.0002	-
0.1808	6250	0.0002	-
0.1823	6300	0.0002	-
0.1837	6350	0.0003	-
0.1852	6400	0.0002	-
0.1866	6450	0.0003	-
0.1881	6500	0.0002	-
0.1895	6550	0.0003	-
0.1910	6600	0.0002	-
0.1924	6650	0.0003	-
0.1939	6700	0.0002	-
0.1953	6750	0.0002	-
0.1968	6800	0.0002	-
0.1982	6850	0.0003	-
0.1996	6900	0.0003	-
0.2011	6950	0.0002	-
0.2025	7000	0.0002	-
0.2040	7050	0.0001	-
0.2054	7100	0.0002	-
0.2069	7150	0.0002	-
0.2083	7200	0.0002	-
0.2098	7250	0.0002	-
0.2112	7300	0.0002	-
0.2127	7350	0.0002	-
0.2141	7400	0.0002	-
0.2156	7450	0.0004	-
0.2170	7500	0.0002	-
0.2185	7550	0.0002	-
0.2199	7600	0.0003	-
0.2213	7650	0.0002	-
0.2228	7700	0.0003	-
0.2242	7750	0.0002	-
0.2257	7800	0.0001	-
0.2271	7850	0.0001	-
0.2286	7900	0.0002	-
0.2300	7950	0.0002	-
0.2315	8000	0.0001	-
0.2329	8050	0.0002	-
0.2344	8100	0.0002	-
0.2358	8150	0.0002	-
0.2373	8200	0.0002	-
0.2387	8250	0.0002	-
0.2402	8300	0.0001	-
0.2416	8350	0.0005	-
0.2430	8400	0.002	-
0.2445	8450	0.0037	-
0.2459	8500	0.0516	-
0.2474	8550	0.0028	-
0.2488	8600	0.0013	-
0.2503	8650	0.0017	-
0.2517	8700	0.0012	-
0.2532	8750	0.0513	-
0.2546	8800	0.001	-
0.2561	8850	0.035	-
0.2575	8900	0.0005	-
0.2590	8950	0.0076	-
0.2604	9000	0.0113	-
0.2619	9050	0.0006	-
0.2633	9100	0.0006	-
0.2647	9150	0.0018	-
0.2662	9200	0.0025	-
0.2676	9250	0.0011	-
0.2691	9300	0.001	-
0.2705	9350	0.0011	-
0.2720	9400	0.0004	-
0.2734	9450	0.0012	-
0.2749	9500	0.0011	-
0.2763	9550	0.0009	-
0.2778	9600	0.0003	-
0.2792	9650	0.0005	-
0.2807	9700	0.0006	-
0.2821	9750	0.0004	-
0.2836	9800	0.0004	-
0.2850	9850	0.0009	-
0.2865	9900	0.0014	-
0.2879	9950	0.0007	-
0.2893	10000	0.0014	-
0.2908	10050	0.0007	-
0.2922	10100	0.0003	-
0.2937	10150	0.0015	-
0.2951	10200	0.0003	-
0.2966	10250	0.0006	-
0.2980	10300	0.0003	-
0.2995	10350	0.0003	-
0.3009	10400	0.0004	-
0.3024	10450	0.0003	-
0.3038	10500	0.0008	-
0.3053	10550	0.0002	-
0.3067	10600	0.0005	-
0.3082	10650	0.0004	-
0.3096	10700	0.0006	-
0.3110	10750	0.0002	-
0.3125	10800	0.0008	-
0.3139	10850	0.0005	-
0.3154	10900	0.0004	-
0.3168	10950	0.0002	-
0.3183	11000	0.0002	-
0.3197	11050	0.0002	-
0.3212	11100	0.0006	-
0.3226	11150	0.0003	-
0.3241	11200	0.0002	-
0.3255	11250	0.0002	-
0.3270	11300	0.0003	-
0.3284	11350	0.0001	-
0.3299	11400	0.0002	-
0.3313	11450	0.0004	-
0.3327	11500	0.0006	-
0.3342	11550	0.0003	-
0.3356	11600	0.0003	-
0.3371	11650	0.0002	-
0.3385	11700	0.0002	-
0.3400	11750	0.0005	-
0.3414	11800	0.0003	-
0.3429	11850	0.0004	-
0.3443	11900	0.0004	-
0.3458	11950	0.0002	-
0.3472	12000	0.0004	-
0.3487	12050	0.0002	-
0.3501	12100	0.0002	-
0.3516	12150	0.0002	-
0.3530	12200	0.0002	-
0.3544	12250	0.0002	-
0.3559	12300	0.0002	-
0.3573	12350	0.0003	-
0.3588	12400	0.0002	-
0.3602	12450	0.0002	-
0.3617	12500	0.0002	-
0.3631	12550	0.0005	-
0.3646	12600	0.0003	-
0.3660	12650	0.0003	-
0.3675	12700	0.0002	-
0.3689	12750	0.0004	-
0.3704	12800	0.0003	-
0.3718	12850	0.0003	-
0.3733	12900	0.0001	-
0.3747	12950	0.0002	-
0.3761	13000	0.0001	-
0.3776	13050	0.0002	-
0.3790	13100	0.0001	-
0.3805	13150	0.0001	-
0.3819	13200	0.0002	-
0.3834	13250	0.0003	-
0.3848	13300	0.0001	-
0.3863	13350	0.0003	-
0.3877	13400	0.0002	-
0.3892	13450	0.0001	-
0.3906	13500	0.0003	-
0.3921	13550	0.0002	-
0.3935	13600	0.0002	-
0.3950	13650	0.0001	-
0.3964	13700	0.0004	-
0.3978	13750	0.0002	-
0.3993	13800	0.0002	-
0.4007	13850	0.0003	-
0.4022	13900	0.0002	-
0.4036	13950	0.0003	-
0.4051	14000	0.0003	-
0.4065	14050	0.0003	-
0.4080	14100	0.0002	-
0.4094	14150	0.0002	-
0.4109	14200	0.0002	-
0.4123	14250	0.0002	-
0.4138	14300	0.0002	-
0.4152	14350	0.0002	-
0.4167	14400	0.0002	-
0.4181	14450	0.0003	-
0.4195	14500	0.0002	-
0.4210	14550	0.0002	-
0.4224	14600	0.0001	-
0.4239	14650	0.0003	-
0.4253	14700	0.0002	-
0.4268	14750	0.0002	-
0.4282	14800	0.0002	-
0.4297	14850	0.0002	-
0.4311	14900	0.0002	-
0.4326	14950	0.0003	-
0.4340	15000	0.0002	-
0.4355	15050	0.0002	-
0.4369	15100	0.0002	-
0.4384	15150	0.0002	-
0.4398	15200	0.0002	-
0.4412	15250	0.0001	-
0.4427	15300	0.0002	-
0.4441	15350	0.0003	-
0.4456	15400	0.0003	-
0.4470	15450	0.0003	-
0.4485	15500	0.0002	-
0.4499	15550	0.0001	-
0.4514	15600	0.0001	-
0.4528	15650	0.0001	-
0.4543	15700	0.0001	-
0.4557	15750	0.0002	-
0.4572	15800	0.0001	-
0.4586	15850	0.0002	-
0.4601	15900	0.0003	-
0.4615	15950	0.0002	-
0.4629	16000	0.0002	-
0.4644	16050	0.0002	-
0.4658	16100	0.0001	-
0.4673	16150	0.0001	-
0.4687	16200	0.0001	-
0.4702	16250	0.0002	-
0.4716	16300	0.0003	-
0.4731	16350	0.0001	-
0.4745	16400	0.0001	-
0.4760	16450	0.0001	-
0.4774	16500	0.0002	-
0.4789	16550	0.0006	-
0.4803	16600	0.0002	-
0.4818	16650	0.0001	-
0.4832	16700	0.0002	-
0.4847	16750	0.0001	-
0.4861	16800	0.0003	-
0.4875	16850	0.0001	-
0.4890	16900	0.0002	-
0.4904	16950	0.0002	-
0.4919	17000	0.0001	-
0.4933	17050	0.0002	-
0.4948	17100	0.0001	-
0.4962	17150	0.0002	-
0.4977	17200	0.0002	-
0.4991	17250	0.0001	-
0.5006	17300	0.0002	-
0.5020	17350	0.0002	-
0.5035	17400	0.0001	-
0.5049	17450	0.0002	-
0.5064	17500	0.0003	-
0.5078	17550	0.0001	-
0.5092	17600	0.0002	-
0.5107	17650	0.0001	-
0.5121	17700	0.0002	-
0.5136	17750	0.0002	-
0.5150	17800	0.0003	-
0.5165	17850	0.0002	-
0.5179	17900	0.0002	-
0.5194	17950	0.0001	-
0.5208	18000	0.0002	-
0.5223	18050	0.0001	-
0.5237	18100	0.0001	-
0.5252	18150	0.0001	-
0.5266	18200	0.0003	-
0.5281	18250	0.0001	-
0.5295	18300	0.0001	-
0.5309	18350	0.0001	-
0.5324	18400	0.0001	-
0.5338	18450	0.0002	-
0.5353	18500	0.0008	-
0.5367	18550	0.0002	-
0.5382	18600	0.0001	-
0.5396	18650	0.0002	-
0.5411	18700	0.0002	-
0.5425	18750	0.0001	-
0.5440	18800	0.0001	-
0.5454	18850	0.0001	-
0.5469	18900	0.0002	-
0.5483	18950	0.0001	-
0.5498	19000	0.0001	-
0.5512	19050	0.0001	-
0.5526	19100	0.0002	-
0.5541	19150	0.0001	-
0.5555	19200	0.0001	-
0.5570	19250	0.0002	-
0.5584	19300	0.0001	-
0.5599	19350	0.0002	-
0.5613	19400	0.0001	-
0.5628	19450	0.0002	-
0.5642	19500	0.0001	-
0.5657	19550	0.0002	-
0.5671	19600	0.0002	-
0.5686	19650	0.0002	-
0.5700	19700	0.0001	-
0.5715	19750	0.0001	-
0.5729	19800	0.0003	-
0.5743	19850	0.0001	-
0.5758	19900	0.0001	-
0.5772	19950	0.0001	-
0.5787	20000	0.0001	-
0.5801	20050	0.0001	-
0.5816	20100	0.0001	-
0.5830	20150	0.0001	-
0.5845	20200	0.0001	-
0.5859	20250	0.0001	-
0.5874	20300	0.0002	-
0.5888	20350	0.0002	-
0.5903	20400	0.0001	-
0.5917	20450	0.0002	-
0.5932	20500	0.0001	-
0.5946	20550	0.0001	-
0.5960	20600	0.0001	-
0.5975	20650	0.0002	-
0.5989	20700	0.0002	-
0.6004	20750	0.0001	-
0.6018	20800	0.0001	-
0.6033	20850	0.0002	-
0.6047	20900	0.0001	-
0.6062	20950	0.0002	-
0.6076	21000	0.0001	-
0.6091	21050	0.0001	-
0.6105	21100	0.0001	-
0.6120	21150	0.0002	-
0.6134	21200	0.0001	-
0.6149	21250	0.0001	-
0.6163	21300	0.0001	-
0.6177	21350	0.0001	-
0.6192	21400	0.0002	-
0.6206	21450	0.0001	-
0.6221	21500	0.0002	-
0.6235	21550	0.0003	-
0.6250	21600	0.0001	-
0.6264	21650	0.0001	-
0.6279	21700	0.0001	-
0.6293	21750	0.0001	-
0.6308	21800	0.0002	-
0.6322	21850	0.0001	-
0.6337	21900	0.0001	-
0.6351	21950	0.0001	-
0.6366	22000	0.0002	-
0.6380	22050	0.0001	-
0.6394	22100	0.0001	-
0.6409	22150	0.0002	-
0.6423	22200	0.0002	-
0.6438	22250	0.0003	-
0.6452	22300	0.0001	-
0.6467	22350	0.0001	-
0.6481	22400	0.0001	-
0.6496	22450	0.0002	-
0.6510	22500	0.0001	-
0.6525	22550	0.0001	-
0.6539	22600	0.0001	-
0.6554	22650	0.0001	-
0.6568	22700	0.0002	-
0.6583	22750	0.0001	-
0.6597	22800	0.0001	-
0.6611	22850	0.0001	-
0.6626	22900	0.0001	-
0.6640	22950	0.0001	-
0.6655	23000	0.0001	-
0.6669	23050	0.0002	-
0.6684	23100	0.0001	-
0.6698	23150	0.0001	-
0.6713	23200	0.0001	-
0.6727	23250	0.0001	-
0.6742	23300	0.0002	-
0.6756	23350	0.0002	-
0.6771	23400	0.0001	-
0.6785	23450	0.0001	-
0.6800	23500	0.0001	-
0.6814	23550	0.0001	-
0.6829	23600	0.0002	-
0.6843	23650	0.0001	-
0.6857	23700	0.0001	-
0.6872	23750	0.0001	-
0.6886	23800	0.0001	-
0.6901	23850	0.0002	-
0.6915	23900	0.0001	-
0.6930	23950	0.0001	-
0.6944	24000	0.0002	-
0.6959	24050	0.0001	-
0.6973	24100	0.0001	-
0.6988	24150	0.0001	-
0.7002	24200	0.0001	-
0.7017	24250	0.0001	-
0.7031	24300	0.0001	-
0.7046	24350	0.0001	-
0.7060	24400	0.0001	-
0.7074	24450	0.0002	-
0.7089	24500	0.0001	-
0.7103	24550	0.0002	-
0.7118	24600	0.0001	-
0.7132	24650	0.0001	-
0.7147	24700	0.0001	-
0.7161	24750	0.0001	-
0.7176	24800	0.0001	-
0.7190	24850	0.0001	-
0.7205	24900	0.0001	-
0.7219	24950	0.0001	-
0.7234	25000	0.0001	-
0.7248	25050	0.0002	-
0.7263	25100	0.0001	-
0.7277	25150	0.0001	-
0.7291	25200	0.0001	-
0.7306	25250	0.0001	-
0.7320	25300	0.0001	-
0.7335	25350	0.0001	-
0.7349	25400	0.0	-
0.7364	25450	0.0001	-
0.7378	25500	0.0001	-
0.7393	25550	0.0001	-
0.7407	25600	0.0001	-
0.7422	25650	0.0001	-
0.7436	25700	0.0001	-
0.7451	25750	0.0001	-
0.7465	25800	0.0	-
0.7480	25850	0.0001	-
0.7494	25900	0.0001	-
0.7508	25950	0.0001	-
0.7523	26000	0.0001	-
0.7537	26050	0.0001	-
0.7552	26100	0.0001	-
0.7566	26150	0.0001	-
0.7581	26200	0.0001	-
0.7595	26250	0.0001	-
0.7610	26300	0.0001	-
0.7624	26350	0.0001	-
0.7639	26400	0.0002	-
0.7653	26450	0.0001	-
0.7668	26500	0.0001	-
0.7682	26550	0.0001	-
0.7697	26600	0.0001	-
0.7711	26650	0.0002	-
0.7725	26700	0.0001	-
0.7740	26750	0.0001	-
0.7754	26800	0.0001	-
0.7769	26850	0.0001	-
0.7783	26900	0.0001	-
0.7798	26950	0.0001	-
0.7812	27000	0.0001	-
0.7827	27050	0.0001	-
0.7841	27100	0.0001	-
0.7856	27150	0.0001	-
0.7870	27200	0.0001	-
0.7885	27250	0.0001	-
0.7899	27300	0.0001	-
0.7914	27350	0.0001	-
0.7928	27400	0.0001	-
0.7942	27450	0.0001	-
0.7957	27500	0.0001	-
0.7971	27550	0.0001	-
0.7986	27600	0.0001	-
0.8000	27650	0.0001	-
0.8015	27700	0.0001	-
0.8029	27750	0.0001	-
0.8044	27800	0.0	-
0.8058	27850	0.0001	-
0.8073	27900	0.0001	-
0.8087	27950	0.0001	-
0.8102	28000	0.0001	-
0.8116	28050	0.0	-
0.8131	28100	0.0	-
0.8145	28150	0.0001	-
0.8159	28200	0.0001	-
0.8174	28250	0.0001	-
0.8188	28300	0.0001	-
0.8203	28350	0.0001	-
0.8217	28400	0.0001	-
0.8232	28450	0.0001	-
0.8246	28500	0.0001	-
0.8261	28550	0.0001	-
0.8275	28600	0.0001	-
0.8290	28650	0.0001	-
0.8304	28700	0.0001	-
0.8319	28750	0.0001	-
0.8333	28800	0.0001	-
0.8348	28850	0.0002	-
0.8362	28900	0.0001	-
0.8376	28950	0.0001	-
0.8391	29000	0.0001	-
0.8405	29050	0.0001	-
0.8420	29100	0.0001	-
0.8434	29150	0.0001	-
0.8449	29200	0.0	-
0.8463	29250	0.0001	-
0.8478	29300	0.0001	-
0.8492	29350	0.0001	-
0.8507	29400	0.0001	-
0.8521	29450	0.0001	-
0.8536	29500	0.0001	-
0.8550	29550	0.0001	-
0.8565	29600	0.0002	-
0.8579	29650	0.0	-
0.8594	29700	0.0001	-
0.8608	29750	0.0001	-
0.8622	29800	0.0001	-
0.8637	29850	0.0001	-
0.8651	29900	0.0	-
0.8666	29950	0.0001	-
0.8680	30000	0.0001	-
0.8695	30050	0.0001	-
0.8709	30100	0.0	-
0.8724	30150	0.0	-
0.8738	30200	0.0001	-
0.8753	30250	0.0001	-
0.8767	30300	0.0001	-
0.8782	30350	0.0001	-
0.8796	30400	0.0001	-
0.8811	30450	0.0001	-
0.8825	30500	0.0001	-
0.8839	30550	0.0001	-
0.8854	30600	0.0	-
0.8868	30650	0.0001	-
0.8883	30700	0.0001	-
0.8897	30750	0.0001	-
0.8912	30800	0.0001	-
0.8926	30850	0.0	-
0.8941	30900	0.0	-
0.8955	30950	0.0001	-
0.8970	31000	0.0001	-
0.8984	31050	0.0001	-
0.8999	31100	0.0001	-
0.9013	31150	0.0	-
0.9028	31200	0.0001	-
0.9042	31250	0.0001	-
0.9056	31300	0.0001	-
0.9071	31350	0.0001	-
0.9085	31400	0.0001	-
0.9100	31450	0.0002	-
0.9114	31500	0.0001	-
0.9129	31550	0.0001	-
0.9143	31600	0.0001	-
0.9158	31650	0.0001	-
0.9172	31700	0.0001	-
0.9187	31750	0.0001	-
0.9201	31800	0.0001	-
0.9216	31850	0.0001	-
0.9230	31900	0.0001	-
0.9245	31950	0.0001	-
0.9259	32000	0.0001	-
0.9273	32050	0.0	-
0.9288	32100	0.0002	-
0.9302	32150	0.0001	-
0.9317	32200	0.0001	-
0.9331	32250	0.0001	-
0.9346	32300	0.0002	-
0.9360	32350	0.0	-
0.9375	32400	0.0001	-
0.9389	32450	0.0001	-
0.9404	32500	0.0	-
0.9418	32550	0.0001	-
0.9433	32600	0.0001	-
0.9447	32650	0.0001	-
0.9462	32700	0.0001	-
0.9476	32750	0.0001	-
0.9490	32800	0.0001	-
0.9505	32850	0.0001	-
0.9519	32900	0.0	-
0.9534	32950	0.0001	-
0.9548	33000	0.0001	-
0.9563	33050	0.0001	-
0.9577	33100	0.0001	-
0.9592	33150	0.0001	-
0.9606	33200	0.0001	-
0.9621	33250	0.0001	-
0.9635	33300	0.0001	-
0.9650	33350	0.0	-
0.9664	33400	0.0001	-
0.9679	33450	0.0001	-
0.9693	33500	0.0	-
0.9707	33550	0.0001	-
0.9722	33600	0.0	-
0.9736	33650	0.0001	-
0.9751	33700	0.0001	-
0.9765	33750	0.0001	-
0.9780	33800	0.0	-
0.9794	33850	0.0001	-
0.9809	33900	0.0001	-
0.9823	33950	0.0001	-
0.9838	34000	0.0001	-
0.9852	34050	0.0	-
0.9867	34100	0.0001	-
0.9881	34150	0.0	-
0.9896	34200	0.0001	-
0.9910	34250	0.0	-
0.9924	34300	0.0001	-
0.9939	34350	0.0	-
0.9953	34400	0.0001	-
0.9968	34450	0.0	-
0.9982	34500	0.0	-
0.9997	34550	0.0001	-
1.0	34561	-	0.0036

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
SetFit: 1.1.0.dev0
Sentence Transformers: 3.0.1
Transformers: 4.44.2
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}
}