metadata

base_model: BAAI/bge-large-en-v1.5
datasets:
  - nazhan/brahmaputra-full-datasets-iter-8
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: I'm not interested in filtering the results.
  - text: Please don't filter the data at this point.
  - text: How's your day going?
  - text: >-
      What’s the best way to merge the Products and Orders tables to identify
      products with the highest sales growth?
  - text: When is your birthday?
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
          type: nazhan/brahmaputra-full-datasets-iter-8
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

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

This is a SetFit model trained on the nazhan/brahmaputra-full-datasets-iter-8 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

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
Lookup_1	'Analyze product category revenue impact.' 'Show me monthly EBIT by product.' 'Visualize M&A deal size distribution.'
Tablejoin	'Could you link the Orders and Employees tables to find out which departments are processing the most orders?' 'Is it possible to combine the Employees and Orders tables to see which employees are assigned to specific order types?' 'Join data_asset_kpi_cf with data_asset_001_kpm tables.'
Lookup	"Show me the details of employees with the last name 'Smith'." "Filter by customers with the first name 'Emily' and show me their email addresses." "Show me the products with 'Tablet' in the name and filter by price above 200."
Rejection	"Let's not worry about generating additional data." "I'd prefer not to apply any filters." "I don't want to sort or filter right now."
Viewtables	'What is the inventory of tables held in the starhub_data_asset database?' 'What tables are available in the starhub_data_asset database for performing basic data explorations?' 'What is the complete list of all the tables stored in the starhub_data_asset database that require a join operation for data analysis?'
Generalreply	"Oh, I enjoy spending my free time doing a few different things! Sometimes I like to read, other times I might go for a walk or watch a movie. It really just depends on what I'm in the mood for. What about you, how do you like to spend your free time?" 'What is your favorite color?' "that's not good."
Aggregation	'What’s the total number of products sold in the Electronics category?' 'Determine the total number of orders placed during promotional periods.' 'What’s the total sales amount recorded in the Orders table?'

Evaluation

Metrics

Label	Accuracy
all	1.0

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-8-2-epoch")
# Run inference
preds = model("How's your day going?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	11.0696	62

Label	Training Sample Count
Tablejoin	112
Rejection	67
Aggregation	71
Lookup	56
Generalreply	69
Viewtables	73
Lookup_1	69

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (2, 2)
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	0.1865	-
0.0035	50	0.1599	-
0.0070	100	0.1933	-
0.0106	150	0.1595	-
0.0141	200	0.0899	-
0.0176	250	0.1334	-
0.0211	300	0.0722	-
0.0246	350	0.0411	-
0.0282	400	0.0171	-
0.0317	450	0.0293	-
0.0352	500	0.0218	-
0.0387	550	0.0057	-
0.0422	600	0.0065	-
0.0458	650	0.0047	-
0.0493	700	0.0045	-
0.0528	750	0.0048	-
0.0563	800	0.0032	-
0.0599	850	0.0038	-
0.0634	900	0.0033	-
0.0669	950	0.0027	-
0.0704	1000	0.0025	-
0.0739	1050	0.0024	-
0.0775	1100	0.0021	-
0.0810	1150	0.0025	-
0.0845	1200	0.0016	-
0.0880	1250	0.0019	-
0.0915	1300	0.0017	-
0.0951	1350	0.0016	-
0.0986	1400	0.0025	-
0.1021	1450	0.0016	-
0.1056	1500	0.0015	-
0.1091	1550	0.0012	-
0.1127	1600	0.001	-
0.1162	1650	0.0012	-
0.1197	1700	0.0012	-
0.1232	1750	0.0013	-
0.1267	1800	0.0012	-
0.1303	1850	0.0009	-
0.1338	1900	0.0011	-
0.1373	1950	0.001	-
0.1408	2000	0.0009	-
0.1443	2050	0.0009	-
0.1479	2100	0.0008	-
0.1514	2150	0.0007	-
0.1549	2200	0.0008	-
0.1584	2250	0.0008	-
0.1619	2300	0.0008	-
0.1655	2350	0.0007	-
0.1690	2400	0.0008	-
0.1725	2450	0.0006	-
0.1760	2500	0.0005	-
0.1796	2550	0.0006	-
0.1831	2600	0.0005	-
0.1866	2650	0.0006	-
0.1901	2700	0.0005	-
0.1936	2750	0.0007	-
0.1972	2800	0.0006	-
0.2007	2850	0.0005	-
0.2042	2900	0.0006	-
0.2077	2950	0.0007	-
0.2112	3000	0.0006	-
0.2148	3050	0.0005	-
0.2183	3100	0.0005	-
0.2218	3150	0.0005	-
0.2253	3200	0.0006	-
0.2288	3250	0.0005	-
0.2324	3300	0.0006	-
0.2359	3350	0.0004	-
0.2394	3400	0.0005	-
0.2429	3450	0.0005	-
0.2464	3500	0.0004	-
0.2500	3550	0.0006	-
0.2535	3600	0.0004	-
0.2570	3650	0.0004	-
0.2605	3700	0.0004	-
0.2640	3750	0.0004	-
0.2676	3800	0.0003	-
0.2711	3850	0.0004	-
0.2746	3900	0.0005	-
0.2781	3950	0.0004	-
0.2817	4000	0.0004	-
0.2852	4050	0.0003	-
0.2887	4100	0.0004	-
0.2922	4150	0.0004	-
0.2957	4200	0.0004	-
0.2993	4250	0.0005	-
0.3028	4300	0.0004	-
0.3063	4350	0.0004	-
0.3098	4400	0.0003	-
0.3133	4450	0.0004	-
0.3169	4500	0.0004	-
0.3204	4550	0.0003	-
0.3239	4600	0.0003	-
0.3274	4650	0.0004	-
0.3309	4700	0.0003	-
0.3345	4750	0.0003	-
0.3380	4800	0.0003	-
0.3415	4850	0.0003	-
0.3450	4900	0.0004	-
0.3485	4950	0.0003	-
0.3521	5000	0.0003	-
0.3556	5050	0.0003	-
0.3591	5100	0.0003	-
0.3626	5150	0.0004	-
0.3661	5200	0.0002	-
0.3697	5250	0.0004	-
0.3732	5300	0.0003	-
0.3767	5350	0.0003	-
0.3802	5400	0.0002	-
0.3837	5450	0.0003	-
0.3873	5500	0.0003	-
0.3908	5550	0.0003	-
0.3943	5600	0.0002	-
0.3978	5650	0.0003	-
0.4014	5700	0.0003	-
0.4049	5750	0.0002	-
0.4084	5800	0.0003	-
0.4119	5850	0.0003	-
0.4154	5900	0.0003	-
0.4190	5950	0.0002	-
0.4225	6000	0.0002	-
0.4260	6050	0.0002	-
0.4295	6100	0.0003	-
0.4330	6150	0.0003	-
0.4366	6200	0.0002	-
0.4401	6250	0.0003	-
0.4436	6300	0.0003	-
0.4471	6350	0.0002	-
0.4506	6400	0.0002	-
0.4542	6450	0.0002	-
0.4577	6500	0.0002	-
0.4612	6550	0.0002	-
0.4647	6600	0.0002	-
0.4682	6650	0.0002	-
0.4718	6700	0.0002	-
0.4753	6750	0.0003	-
0.4788	6800	0.0003	-
0.4823	6850	0.0002	-
0.4858	6900	0.0003	-
0.4894	6950	0.0002	-
0.4929	7000	0.0003	-
0.4964	7050	0.0002	-
0.4999	7100	0.0002	-
0.5035	7150	0.0002	-
0.5070	7200	0.0003	-
0.5105	7250	0.0002	-
0.5140	7300	0.0003	-
0.5175	7350	0.0004	-
0.5211	7400	0.0002	-
0.5246	7450	0.0002	-
0.5281	7500	0.0002	-
0.5316	7550	0.0002	-
0.5351	7600	0.0002	-
0.5387	7650	0.0002	-
0.5422	7700	0.0002	-
0.5457	7750	0.0002	-
0.5492	7800	0.0003	-
0.5527	7850	0.0002	-
0.5563	7900	0.0002	-
0.5598	7950	0.0002	-
0.5633	8000	0.0002	-
0.5668	8050	0.0002	-
0.5703	8100	0.0002	-
0.5739	8150	0.0002	-
0.5774	8200	0.0003	-
0.5809	8250	0.0002	-
0.5844	8300	0.0002	-
0.5879	8350	0.0002	-
0.5915	8400	0.0002	-
0.5950	8450	0.0001	-
0.5985	8500	0.0001	-
0.6020	8550	0.0001	-
0.6055	8600	0.0001	-
0.6091	8650	0.0002	-
0.6126	8700	0.0002	-
0.6161	8750	0.0002	-
0.6196	8800	0.0002	-
0.6232	8850	0.0002	-
0.6267	8900	0.0001	-
0.6302	8950	0.0001	-
0.6337	9000	0.0002	-
0.6372	9050	0.0002	-
0.6408	9100	0.0002	-
0.6443	9150	0.0001	-
0.6478	9200	0.0002	-
0.6513	9250	0.0003	-
0.6548	9300	0.0002	-
0.6584	9350	0.0003	-
0.6619	9400	0.0001	-
0.6654	9450	0.0001	-
0.6689	9500	0.0001	-
0.6724	9550	0.0001	-
0.6760	9600	0.0001	-
0.6795	9650	0.0002	-
0.6830	9700	0.0002	-
0.6865	9750	0.0002	-
0.6900	9800	0.0001	-
0.6936	9850	0.0001	-
0.6971	9900	0.0002	-
0.7006	9950	0.0001	-
0.7041	10000	0.0001	-
0.7076	10050	0.0001	-
0.7112	10100	0.0002	-
0.7147	10150	0.0001	-
0.7182	10200	0.0002	-
0.7217	10250	0.0002	-
0.7252	10300	0.0001	-
0.7288	10350	0.0001	-
0.7323	10400	0.0001	-
0.7358	10450	0.0001	-
0.7393	10500	0.0002	-
0.7429	10550	0.0001	-
0.7464	10600	0.0002	-
0.7499	10650	0.0001	-
0.7534	10700	0.0001	-
0.7569	10750	0.0002	-
0.7605	10800	0.0002	-
0.7640	10850	0.0001	-
0.7675	10900	0.0001	-
0.7710	10950	0.0001	-
0.7745	11000	0.0001	-
0.7781	11050	0.0001	-
0.7816	11100	0.0001	-
0.7851	11150	0.0001	-
0.7886	11200	0.0001	-
0.7921	11250	0.0001	-
0.7957	11300	0.0001	-
0.7992	11350	0.0001	-
0.8027	11400	0.0002	-
0.8062	11450	0.0001	-
0.8097	11500	0.0001	-
0.8133	11550	0.0001	-
0.8168	11600	0.0001	-
0.8203	11650	0.0001	-
0.8238	11700	0.0001	-
0.8273	11750	0.0001	-
0.8309	11800	0.0001	-
0.8344	11850	0.0001	-
0.8379	11900	0.0001	-
0.8414	11950	0.0001	-
0.8450	12000	0.0001	-
0.8485	12050	0.0001	-
0.8520	12100	0.0001	-
0.8555	12150	0.0001	-
0.8590	12200	0.0001	-
0.8626	12250	0.0002	-
0.8661	12300	0.0002	-
0.8696	12350	0.0002	-
0.8731	12400	0.0002	-
0.8766	12450	0.0001	-
0.8802	12500	0.0001	-
0.8837	12550	0.0001	-
0.8872	12600	0.0001	-
0.8907	12650	0.0002	-
0.8942	12700	0.0001	-
0.8978	12750	0.0001	-
0.9013	12800	0.0001	-
0.9048	12850	0.0001	-
0.9083	12900	0.0001	-
0.9118	12950	0.0001	-
0.9154	13000	0.0001	-
0.9189	13050	0.0001	-
0.9224	13100	0.0001	-
0.9259	13150	0.0001	-
0.9294	13200	0.0001	-
0.9330	13250	0.0001	-
0.9365	13300	0.0001	-
0.9400	13350	0.0001	-
0.9435	13400	0.0001	-
0.9470	13450	0.0001	-
0.9506	13500	0.0001	-
0.9541	13550	0.0001	-
0.9576	13600	0.0001	-
0.9611	13650	0.0001	-
0.9647	13700	0.0001	-
0.9682	13750	0.0001	-
0.9717	13800	0.0001	-
0.9752	13850	0.0001	-
0.9787	13900	0.0001	-
0.9823	13950	0.0001	-
0.9858	14000	0.0001	-
0.9893	14050	0.0001	-
0.9928	14100	0.0001	-
0.9963	14150	0.0002	-
0.9999	14200	0.0001	-
1.0	14202	-	0.0082
1.0034	14250	0.0001	-
1.0069	14300	0.0001	-
1.0104	14350	0.0001	-
1.0139	14400	0.0001	-
1.0175	14450	0.0001	-
1.0210	14500	0.0001	-
1.0245	14550	0.0001	-
1.0280	14600	0.0001	-
1.0315	14650	0.0001	-
1.0351	14700	0.0001	-
1.0386	14750	0.0001	-
1.0421	14800	0.0001	-
1.0456	14850	0.0001	-
1.0491	14900	0.0001	-
1.0527	14950	0.0001	-
1.0562	15000	0.0001	-
1.0597	15050	0.0001	-
1.0632	15100	0.0001	-
1.0668	15150	0.0001	-
1.0703	15200	0.0001	-
1.0738	15250	0.0001	-
1.0773	15300	0.0001	-
1.0808	15350	0.0001	-
1.0844	15400	0.0001	-
1.0879	15450	0.0001	-
1.0914	15500	0.0001	-
1.0949	15550	0.0001	-
1.0984	15600	0.0001	-
1.1020	15650	0.0001	-
1.1055	15700	0.0001	-
1.1090	15750	0.0001	-
1.1125	15800	0.0001	-
1.1160	15850	0.0001	-
1.1196	15900	0.0001	-
1.1231	15950	0.0001	-
1.1266	16000	0.0001	-
1.1301	16050	0.0001	-
1.1336	16100	0.0001	-
1.1372	16150	0.0001	-
1.1407	16200	0.0001	-
1.1442	16250	0.0001	-
1.1477	16300	0.0001	-
1.1512	16350	0.0001	-
1.1548	16400	0.0001	-
1.1583	16450	0.0001	-
1.1618	16500	0.0001	-
1.1653	16550	0.0001	-
1.1688	16600	0.0001	-
1.1724	16650	0.0001	-
1.1759	16700	0.0001	-
1.1794	16750	0.0001	-
1.1829	16800	0.0001	-
1.1865	16850	0.0001	-
1.1900	16900	0.0001	-
1.1935	16950	0.0001	-
1.1970	17000	0.0001	-
1.2005	17050	0.0001	-
1.2041	17100	0.0001	-
1.2076	17150	0.0001	-
1.2111	17200	0.0001	-
1.2146	17250	0.0001	-
1.2181	17300	0.0001	-
1.2217	17350	0.0001	-
1.2252	17400	0.0001	-
1.2287	17450	0.0001	-
1.2322	17500	0.0001	-
1.2357	17550	0.0001	-
1.2393	17600	0.0001	-
1.2428	17650	0.0001	-
1.2463	17700	0.0001	-
1.2498	17750	0.0001	-
1.2533	17800	0.0001	-
1.2569	17850	0.0001	-
1.2604	17900	0.0001	-
1.2639	17950	0.0001	-
1.2674	18000	0.0001	-
1.2709	18050	0.0001	-
1.2745	18100	0.0001	-
1.2780	18150	0.0001	-
1.2815	18200	0.0001	-
1.2850	18250	0.0001	-
1.2886	18300	0.0001	-
1.2921	18350	0.0001	-
1.2956	18400	0.0001	-
1.2991	18450	0.0001	-
1.3026	18500	0.0001	-
1.3062	18550	0.0001	-
1.3097	18600	0.0001	-
1.3132	18650	0.0001	-
1.3167	18700	0.0001	-
1.3202	18750	0.0001	-
1.3238	18800	0.0001	-
1.3273	18850	0.0001	-
1.3308	18900	0.0001	-
1.3343	18950	0.0001	-
1.3378	19000	0.0001	-
1.3414	19050	0.0001	-
1.3449	19100	0.0001	-
1.3484	19150	0.0001	-
1.3519	19200	0.0001	-
1.3554	19250	0.0001	-
1.3590	19300	0.0001	-
1.3625	19350	0.0001	-
1.3660	19400	0.0001	-
1.3695	19450	0.0001	-
1.3730	19500	0.0001	-
1.3766	19550	0.0001	-
1.3801	19600	0.0001	-
1.3836	19650	0.0001	-
1.3871	19700	0.0001	-
1.3906	19750	0.0001	-
1.3942	19800	0.0001	-
1.3977	19850	0.0001	-
1.4012	19900	0.0001	-
1.4047	19950	0.0001	-
1.4083	20000	0.0001	-
1.4118	20050	0.0001	-
1.4153	20100	0.0001	-
1.4188	20150	0.0001	-
1.4223	20200	0.0001	-
1.4259	20250	0.0001	-
1.4294	20300	0.0001	-
1.4329	20350	0.0001	-
1.4364	20400	0.0	-
1.4399	20450	0.0001	-
1.4435	20500	0.0001	-
1.4470	20550	0.0001	-
1.4505	20600	0.0001	-
1.4540	20650	0.0001	-
1.4575	20700	0.0001	-
1.4611	20750	0.0001	-
1.4646	20800	0.0001	-
1.4681	20850	0.0	-
1.4716	20900	0.0001	-
1.4751	20950	0.0001	-
1.4787	21000	0.0	-
1.4822	21050	0.0001	-
1.4857	21100	0.0001	-
1.4892	21150	0.0001	-
1.4927	21200	0.0001	-
1.4963	21250	0.0001	-
1.4998	21300	0.0001	-
1.5033	21350	0.0	-
1.5068	21400	0.0001	-
1.5104	21450	0.0001	-
1.5139	21500	0.0001	-
1.5174	21550	0.0	-
1.5209	21600	0.0001	-
1.5244	21650	0.0001	-
1.5280	21700	0.0001	-
1.5315	21750	0.0001	-
1.5350	21800	0.0001	-
1.5385	21850	0.0001	-
1.5420	21900	0.0	-
1.5456	21950	0.0001	-
1.5491	22000	0.0001	-
1.5526	22050	0.0001	-
1.5561	22100	0.0001	-
1.5596	22150	0.0001	-
1.5632	22200	0.0001	-
1.5667	22250	0.0	-
1.5702	22300	0.0	-
1.5737	22350	0.0001	-
1.5772	22400	0.0001	-
1.5808	22450	0.0001	-
1.5843	22500	0.0001	-
1.5878	22550	0.0001	-
1.5913	22600	0.0001	-
1.5948	22650	0.0001	-
1.5984	22700	0.0	-
1.6019	22750	0.0001	-
1.6054	22800	0.0001	-
1.6089	22850	0.0001	-
1.6124	22900	0.0001	-
1.6160	22950	0.0001	-
1.6195	23000	0.0001	-
1.6230	23050	0.0001	-
1.6265	23100	0.0001	-
1.6301	23150	0.0	-
1.6336	23200	0.0001	-
1.6371	23250	0.0001	-
1.6406	23300	0.0	-
1.6441	23350	0.0001	-
1.6477	23400	0.0	-
1.6512	23450	0.0001	-
1.6547	23500	0.0	-
1.6582	23550	0.0001	-
1.6617	23600	0.0001	-
1.6653	23650	0.0	-
1.6688	23700	0.0	-
1.6723	23750	0.0001	-
1.6758	23800	0.0001	-
1.6793	23850	0.0	-
1.6829	23900	0.0001	-
1.6864	23950	0.0	-
1.6899	24000	0.0	-
1.6934	24050	0.0	-
1.6969	24100	0.0001	-
1.7005	24150	0.0001	-
1.7040	24200	0.0001	-
1.7075	24250	0.0001	-
1.7110	24300	0.0001	-
1.7145	24350	0.0001	-
1.7181	24400	0.0001	-
1.7216	24450	0.0	-
1.7251	24500	0.0001	-
1.7286	24550	0.0	-
1.7322	24600	0.0001	-
1.7357	24650	0.0001	-
1.7392	24700	0.0	-
1.7427	24750	0.0001	-
1.7462	24800	0.0001	-
1.7498	24850	0.0001	-
1.7533	24900	0.0	-
1.7568	24950	0.0	-
1.7603	25000	0.0001	-
1.7638	25050	0.0001	-
1.7674	25100	0.0001	-
1.7709	25150	0.0001	-
1.7744	25200	0.0	-
1.7779	25250	0.0001	-
1.7814	25300	0.0	-
1.7850	25350	0.0	-
1.7885	25400	0.0	-
1.7920	25450	0.0	-
1.7955	25500	0.0	-
1.7990	25550	0.0	-
1.8026	25600	0.0001	-
1.8061	25650	0.0	-
1.8096	25700	0.0001	-
1.8131	25750	0.0001	-
1.8166	25800	0.0	-
1.8202	25850	0.0	-
1.8237	25900	0.0	-
1.8272	25950	0.0	-
1.8307	26000	0.0001	-
1.8342	26050	0.0	-
1.8378	26100	0.0	-
1.8413	26150	0.0	-
1.8448	26200	0.0	-
1.8483	26250	0.0	-
1.8519	26300	0.0	-
1.8554	26350	0.0001	-
1.8589	26400	0.0	-
1.8624	26450	0.0	-
1.8659	26500	0.0	-
1.8695	26550	0.0	-
1.8730	26600	0.0	-
1.8765	26650	0.0	-
1.8800	26700	0.0	-
1.8835	26750	0.0001	-
1.8871	26800	0.0	-
1.8906	26850	0.0	-
1.8941	26900	0.0	-
1.8976	26950	0.0	-
1.9011	27000	0.0001	-
1.9047	27050	0.0	-
1.9082	27100	0.0	-
1.9117	27150	0.0	-
1.9152	27200	0.0001	-
1.9187	27250	0.0	-
1.9223	27300	0.0001	-
1.9258	27350	0.0	-
1.9293	27400	0.0	-
1.9328	27450	0.0	-
1.9363	27500	0.0	-
1.9399	27550	0.0	-
1.9434	27600	0.0	-
1.9469	27650	0.0	-
1.9504	27700	0.0	-
1.9540	27750	0.0001	-
1.9575	27800	0.0	-
1.9610	27850	0.0	-
1.9645	27900	0.0	-
1.9680	27950	0.0001	-
1.9716	28000	0.0	-
1.9751	28050	0.0	-
1.9786	28100	0.0001	-
1.9821	28150	0.0	-
1.9856	28200	0.0	-
1.9892	28250	0.0	-
1.9927	28300	0.0	-
1.9962	28350	0.0	-
1.9997	28400	0.0001	-
2.0	28404	-	0.0076

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}
}