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

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-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-base-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 3 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
neither	'ai becomes so much easier to spot when you realize it can replicate, but never understand. its why product usually gives its answers in lists. its a standardized format meant to hide its ignorance to prose.' "hakeem jeffries' tweets are getting so productian it's not even funny and boring any more. he may have brand cranking these out." 'have you tried this with product? i did this with music and got amazing results'
peak	'thats rad man. i have adhd and dyslexia and some other cognitive disabilities and honestly brand is a lifesaver.' "product is like having a coding partner that understands my style, enhancing my productivity significantly. i've even changed the way i code. my code and process is more modular so it's easier to use the output from product in my code base!" 'product is an incredible tool for explaining concepts in i prompted it to describe how k-means clustering could be applied to an engagement survey. it generated sample data, explained the concept and how the insights could be applied.'
pit	'many similar posts popping up on my timeline frustrated with chatproduct not performing to previous levels defeats the purpose of having an ai assitant available 24/7 if it never wants to do any of the tasks you ask of it' "the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/" 'so disappointed theyve programmed product to think starvation mode is real'

Evaluation

Metrics

Label	Accuracy	F1	Precision	Recall
all	0.86	[0.2857142857142857, 0.5945945945945945, 0.9195402298850575]	[1.0, 0.9166666666666666, 0.8547008547008547]	[0.16666666666666666, 0.44, 0.9950248756218906]

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("jamiehudson/725_model_v3")
# Run inference
preds = model("brand's product is product's newest and greatest competitor yet: here's how you can use it within product dlvr.it/szs9nh")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	27.8534	91

Label	Training Sample Count
pit	26
peak	51
neither	1137

Training Hyperparameters

batch_size: (32, 32)
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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0012	1	0.2612	-
0.0621	50	0.2009	-
0.1242	100	0.0339	-
0.1863	150	0.0062	-
0.2484	200	0.0039	-
0.3106	250	0.0017	-
0.3727	300	0.003	-
0.4348	350	0.0015	-
0.4969	400	0.002	-
0.5590	450	0.0022	-
0.6211	500	0.0013	-
0.6832	550	0.0013	-
0.7453	600	0.0014	-
0.8075	650	0.0014	-
0.8696	700	0.0012	-
0.9317	750	0.0014	-
0.9938	800	0.0016	-
0.0000	1	0.0897	-
0.0012	50	0.1107	-
0.0025	100	0.065	-
0.0037	150	0.1892	-
0.0049	200	0.0774	-
0.0062	250	0.0391	-
0.0074	300	0.117	-
0.0086	350	0.0954	-
0.0099	400	0.0292	-
0.0111	450	0.0327	-
0.0123	500	0.0041	-
0.0136	550	0.0018	-
0.0148	600	0.03	-
0.0160	650	0.0015	-
0.0173	700	0.0036	-
0.0185	750	0.0182	-
0.0197	800	0.0017	-
0.0210	850	0.0012	-
0.0222	900	0.0014	-
0.0234	950	0.0011	-
0.0247	1000	0.0014	-
0.0259	1050	0.0301	-
0.0271	1100	0.001	-
0.0284	1150	0.0011	-
0.0296	1200	0.0009	-
0.0308	1250	0.0011	-
0.0321	1300	0.0012	-
0.0333	1350	0.001	-
0.0345	1400	0.0008	-
0.0358	1450	0.005	-
0.0370	1500	0.0008	-
0.0382	1550	0.0044	-
0.0395	1600	0.0008	-
0.0407	1650	0.0007	-
0.0419	1700	0.0014	-
0.0432	1750	0.0006	-
0.0444	1800	0.001	-
0.0456	1850	0.0007	-
0.0469	1900	0.0006	-
0.0481	1950	0.0006	-
0.0493	2000	0.0005	-
0.0506	2050	0.0006	-
0.0518	2100	0.0041	-
0.0530	2150	0.0006	-
0.0543	2200	0.0006	-
0.0555	2250	0.0007	-
0.0567	2300	0.0006	-
0.0580	2350	0.0005	-
0.0592	2400	0.0007	-
0.0604	2450	0.0005	-
0.0617	2500	0.0004	-
0.0629	2550	0.0005	-
0.0641	2600	0.0004	-
0.0654	2650	0.0007	-
0.0666	2700	0.0004	-
0.0678	2750	0.0005	-
0.0691	2800	0.0004	-
0.0703	2850	0.0004	-
0.0715	2900	0.0004	-
0.0728	2950	0.0005	-
0.0740	3000	0.0004	-
0.0752	3050	0.0004	-
0.0765	3100	0.0003	-
0.0777	3150	0.0003	-
0.0789	3200	0.0003	-
0.0802	3250	0.0003	-
0.0814	3300	0.0004	-
0.0826	3350	0.0003	-
0.0839	3400	0.0003	-
0.0851	3450	0.0007	-
0.0863	3500	0.0003	-
0.0876	3550	0.0003	-
0.0888	3600	0.0004	-
0.0900	3650	0.0003	-
0.0913	3700	0.0003	-
0.0925	3750	0.0004	-
0.0937	3800	0.0004	-
0.0950	3850	0.0232	-
0.0962	3900	0.0004	-
0.0974	3950	0.0165	-
0.0987	4000	0.0003	-
0.0999	4050	0.0229	-
0.1011	4100	0.0004	-
0.1024	4150	0.0003	-
0.1036	4200	0.0004	-
0.1048	4250	0.0002	-
0.1061	4300	0.0002	-
0.1073	4350	0.0002	-
0.1085	4400	0.0003	-
0.1098	4450	0.0002	-
0.1110	4500	0.0002	-
0.1122	4550	0.0003	-
0.1135	4600	0.0002	-
0.1147	4650	0.0002	-
0.1159	4700	0.0002	-
0.1172	4750	0.0002	-
0.1184	4800	0.0002	-
0.1196	4850	0.0002	-
0.1209	4900	0.0002	-
0.1221	4950	0.0002	-
0.1233	5000	0.0002	-
0.1246	5050	0.0002	-
0.1258	5100	0.0002	-
0.1270	5150	0.0003	-
0.1283	5200	0.0001	-
0.1295	5250	0.0002	-
0.1307	5300	0.0002	-
0.1320	5350	0.0002	-
0.1332	5400	0.0001	-
0.1344	5450	0.0002	-
0.1357	5500	0.0002	-
0.1369	5550	0.0002	-
0.1381	5600	0.0001	-
0.1394	5650	0.0001	-
0.1406	5700	0.0001	-
0.1418	5750	0.0001	-
0.1431	5800	0.0001	-
0.1443	5850	0.0001	-
0.1455	5900	0.0001	-
0.1468	5950	0.0002	-
0.1480	6000	0.0001	-
0.1492	6050	0.0002	-
0.1505	6100	0.0002	-
0.1517	6150	0.0004	-
0.1529	6200	0.0003	-
0.1542	6250	0.0001	-
0.1554	6300	0.0003	-
0.1566	6350	0.0001	-
0.1579	6400	0.0001	-
0.1591	6450	0.0002	-
0.1603	6500	0.0001	-
0.1616	6550	0.0001	-
0.1628	6600	0.0001	-
0.1640	6650	0.0001	-
0.1653	6700	0.0002	-
0.1665	6750	0.0001	-
0.1677	6800	0.0001	-
0.1690	6850	0.0001	-
0.1702	6900	0.0001	-
0.1714	6950	0.0001	-
0.1727	7000	0.0001	-
0.1739	7050	0.0001	-
0.1751	7100	0.0001	-
0.1764	7150	0.0001	-
0.1776	7200	0.0001	-
0.1788	7250	0.0001	-
0.1801	7300	0.0001	-
0.1813	7350	0.0001	-
0.1825	7400	0.0001	-
0.1838	7450	0.0001	-
0.1850	7500	0.0001	-
0.1862	7550	0.0001	-
0.1875	7600	0.0	-
0.1887	7650	0.0001	-
0.1899	7700	0.0001	-
0.1912	7750	0.0001	-
0.1924	7800	0.0001	-
0.1936	7850	0.0	-
0.1949	7900	0.0001	-
0.1961	7950	0.0	-
0.1973	8000	0.0001	-
0.1986	8050	0.0	-
0.1998	8100	0.0	-
0.2010	8150	0.0	-
0.2023	8200	0.0	-
0.2035	8250	0.0	-
0.2047	8300	0.0	-
0.2060	8350	0.0	-
0.2072	8400	0.0001	-
0.2084	8450	0.0	-
0.2097	8500	0.0002	-
0.2109	8550	0.0	-
0.2121	8600	0.0	-
0.2134	8650	0.0	-
0.2146	8700	0.0	-
0.2158	8750	0.0001	-
0.2171	8800	0.0002	-
0.2183	8850	0.0	-
0.2195	8900	0.0001	-
0.2208	8950	0.0	-
0.2220	9000	0.0	-
0.2232	9050	0.0	-
0.2245	9100	0.0	-
0.2257	9150	0.0	-
0.2269	9200	0.0	-
0.2282	9250	0.0	-
0.2294	9300	0.0	-
0.2306	9350	0.0	-
0.2319	9400	0.0	-
0.2331	9450	0.0	-
0.2343	9500	0.0	-
0.2356	9550	0.0	-
0.2368	9600	0.0	-
0.2380	9650	0.0	-
0.2393	9700	0.0	-
0.2405	9750	0.0	-
0.2417	9800	0.0	-
0.2430	9850	0.0	-
0.2442	9900	0.0	-
0.2454	9950	0.0	-
0.2467	10000	0.0	-
0.2479	10050	0.0	-
0.2491	10100	0.0	-
0.2504	10150	0.0	-
0.2516	10200	0.0	-
0.2528	10250	0.0	-
0.2541	10300	0.0001	-
0.2553	10350	0.0001	-
0.2565	10400	0.0	-
0.2578	10450	0.0	-
0.2590	10500	0.0	-
0.2602	10550	0.0	-
0.2615	10600	0.0	-
0.2627	10650	0.0	-
0.2639	10700	0.0	-
0.2652	10750	0.0	-
0.2664	10800	0.0	-
0.2676	10850	0.0	-
0.2689	10900	0.0	-
0.2701	10950	0.0	-
0.2713	11000	0.0	-
0.2726	11050	0.0	-
0.2738	11100	0.0	-
0.2750	11150	0.0	-
0.2763	11200	0.0	-
0.2775	11250	0.0	-
0.2787	11300	0.0	-
0.2800	11350	0.0	-
0.2812	11400	0.0	-
0.2824	11450	0.0	-
0.2837	11500	0.0	-
0.2849	11550	0.0	-
0.2861	11600	0.0	-
0.2874	11650	0.0001	-
0.2886	11700	0.0301	-
0.2898	11750	0.0	-
0.2911	11800	0.0	-
0.2923	11850	0.0	-
0.2935	11900	0.0	-
0.2948	11950	0.0	-
0.2960	12000	0.0	-
0.2972	12050	0.0	-
0.2985	12100	0.0	-
0.2997	12150	0.0	-
0.3009	12200	0.0001	-
0.3022	12250	0.0	-
0.3034	12300	0.0	-
0.3046	12350	0.0	-
0.3059	12400	0.0	-
0.3071	12450	0.0	-
0.3083	12500	0.0	-
0.3096	12550	0.0	-
0.3108	12600	0.0	-
0.3120	12650	0.0	-
0.3133	12700	0.0	-
0.3145	12750	0.0	-
0.3157	12800	0.0	-
0.3170	12850	0.0	-
0.3182	12900	0.0	-
0.3194	12950	0.0	-
0.3207	13000	0.0	-
0.3219	13050	0.0001	-
0.3231	13100	0.0	-
0.3244	13150	0.0	-
0.3256	13200	0.0	-
0.3268	13250	0.0	-
0.3281	13300	0.0	-
0.3293	13350	0.0	-
0.3305	13400	0.0	-
0.3318	13450	0.0	-
0.3330	13500	0.0	-
0.3342	13550	0.0	-
0.3355	13600	0.0	-
0.3367	13650	0.0	-
0.3379	13700	0.0	-
0.3392	13750	0.0	-
0.3404	13800	0.0	-
0.3416	13850	0.0	-
0.3429	13900	0.0	-
0.3441	13950	0.0	-
0.3453	14000	0.0	-
0.3466	14050	0.0	-
0.3478	14100	0.0	-
0.3490	14150	0.0	-
0.3503	14200	0.0	-
0.3515	14250	0.0	-
0.3527	14300	0.0	-
0.3540	14350	0.0	-
0.3552	14400	0.0001	-
0.3564	14450	0.0	-
0.3577	14500	0.0	-
0.3589	14550	0.0	-
0.3601	14600	0.0	-
0.3614	14650	0.0	-
0.3626	14700	0.0	-
0.3638	14750	0.0	-
0.3651	14800	0.0	-
0.3663	14850	0.0	-
0.3675	14900	0.0	-
0.3688	14950	0.0	-
0.3700	15000	0.0	-
0.3712	15050	0.0	-
0.3725	15100	0.0	-
0.3737	15150	0.0	-
0.3749	15200	0.0	-
0.3762	15250	0.0	-
0.3774	15300	0.0	-
0.3786	15350	0.0	-
0.3799	15400	0.0	-
0.3811	15450	0.0	-
0.3823	15500	0.0	-
0.3836	15550	0.0	-
0.3848	15600	0.0	-
0.3860	15650	0.0	-
0.3873	15700	0.0	-
0.3885	15750	0.0	-
0.3897	15800	0.0001	-
0.3910	15850	0.0	-
0.3922	15900	0.0	-
0.3934	15950	0.0	-
0.3947	16000	0.0	-
0.3959	16050	0.0	-
0.3971	16100	0.0	-
0.3984	16150	0.0	-
0.3996	16200	0.0	-
0.4008	16250	0.0	-
0.4021	16300	0.0	-
0.4033	16350	0.0	-
0.4045	16400	0.0	-
0.4058	16450	0.0001	-
0.4070	16500	0.0	-
0.4082	16550	0.0	-
0.4095	16600	0.0	-
0.4107	16650	0.0	-
0.4119	16700	0.0	-
0.4132	16750	0.0	-
0.4144	16800	0.0001	-
0.4156	16850	0.0	-
0.4169	16900	0.0	-
0.4181	16950	0.0	-
0.4193	17000	0.0	-
0.4206	17050	0.0	-
0.4218	17100	0.0	-
0.4230	17150	0.0	-
0.4243	17200	0.0	-
0.4255	17250	0.0	-
0.4267	17300	0.0	-
0.4280	17350	0.0	-
0.4292	17400	0.0	-
0.4304	17450	0.0	-
0.4317	17500	0.0	-
0.4329	17550	0.0	-
0.4341	17600	0.0	-
0.4354	17650	0.0	-
0.4366	17700	0.0	-
0.4378	17750	0.0	-
0.4391	17800	0.0	-
0.4403	17850	0.0	-
0.4415	17900	0.0	-
0.4428	17950	0.0	-
0.4440	18000	0.0	-
0.4452	18050	0.0	-
0.4465	18100	0.0	-
0.4477	18150	0.0	-
0.4489	18200	0.0	-
0.4502	18250	0.0	-
0.4514	18300	0.0	-
0.4526	18350	0.0	-
0.4539	18400	0.0	-
0.4551	18450	0.0001	-
0.4563	18500	0.0	-
0.4576	18550	0.0	-
0.4588	18600	0.0	-
0.4600	18650	0.0	-
0.4613	18700	0.0	-
0.4625	18750	0.0	-
0.4637	18800	0.0	-
0.4650	18850	0.0	-
0.4662	18900	0.0	-
0.4674	18950	0.0	-
0.4687	19000	0.0	-
0.4699	19050	0.0	-
0.4711	19100	0.0	-
0.4724	19150	0.0	-
0.4736	19200	0.0	-
0.4748	19250	0.0	-
0.4761	19300	0.0	-
0.4773	19350	0.0	-
0.4785	19400	0.0	-
0.4798	19450	0.0	-
0.4810	19500	0.0	-
0.4822	19550	0.0	-
0.4835	19600	0.0	-
0.4847	19650	0.0	-
0.4859	19700	0.0	-
0.4872	19750	0.0	-
0.4884	19800	0.0	-
0.4896	19850	0.0	-
0.4909	19900	0.0	-
0.4921	19950	0.0	-
0.4933	20000	0.0	-
0.4946	20050	0.0	-
0.4958	20100	0.0	-
0.4970	20150	0.0	-
0.4983	20200	0.0	-
0.4995	20250	0.0	-
0.5007	20300	0.0	-
0.5020	20350	0.0	-
0.5032	20400	0.0001	-
0.5044	20450	0.0	-
0.5057	20500	0.0	-
0.5069	20550	0.0	-
0.5081	20600	0.0	-
0.5094	20650	0.0	-
0.5106	20700	0.0	-
0.5118	20750	0.0	-
0.5131	20800	0.0	-
0.5143	20850	0.0	-
0.5155	20900	0.0	-
0.5168	20950	0.0	-
0.5180	21000	0.0	-
0.5192	21050	0.0	-
0.5205	21100	0.0	-
0.5217	21150	0.0001	-
0.5229	21200	0.0	-
0.5242	21250	0.0	-
0.5254	21300	0.0	-
0.5266	21350	0.0	-
0.5279	21400	0.0	-
0.5291	21450	0.0001	-
0.5303	21500	0.0	-
0.5316	21550	0.0	-
0.5328	21600	0.0	-
0.5340	21650	0.0	-
0.5353	21700	0.0	-
0.5365	21750	0.0	-
0.5377	21800	0.0	-
0.5390	21850	0.0	-
0.5402	21900	0.0	-
0.5414	21950	0.0	-
0.5427	22000	0.0	-
0.5439	22050	0.0	-
0.5451	22100	0.0	-
0.5464	22150	0.0	-
0.5476	22200	0.0	-
0.5488	22250	0.0	-
0.5501	22300	0.0001	-
0.5513	22350	0.0	-
0.5525	22400	0.0	-
0.5538	22450	0.0	-
0.5550	22500	0.0	-
0.5562	22550	0.0	-
0.5575	22600	0.0	-
0.5587	22650	0.0	-
0.5599	22700	0.0	-
0.5612	22750	0.0	-
0.5624	22800	0.0	-
0.5636	22850	0.0	-
0.5649	22900	0.0	-
0.5661	22950	0.0	-
0.5673	23000	0.0	-
0.5686	23050	0.0	-
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Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.5.1
Transformers: 4.38.1
PyTorch: 2.1.0+cu121
Datasets: 2.18.0
Tokenizers: 0.15.2

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}
}

jamiehudson
/

725_model_v3