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 SetFitHead 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 SetFitHead instance
Maximum Sequence Length: 512 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
NON_SARCASTIC	'so the newer devices have the ios screenshot i m still on ios but my ipad mini 1 st gen shows the ios screenshot . odd .' 'why do amazon need a test authorisation when i add a new payment card , as well as the authorisation they get when i actually use it ?' 'waterboarding sounds like a lot of fun until you find out what it is'
SARCASTIC	"have you been reading long ? you are not very good at it . it has nothing to do with who i like , especially since i am not a fan of corbyn anyway . it ' s that in one case someone was literally slapped in the face , and in the other someone wore a milkshake . battery > being annoying" 'wish one of the many people dressed as killers were actually one n killed me' 'is it even christmas if there isn t a fight with neighbours and a broken wrist ?'

Label

Examples

NON_SARCASTIC

'so the newer devices have the ios screenshot i m still on ios but my ipad mini 1 st gen shows the ios screenshot . odd .'
'why do amazon need a test authorisation when i add a new payment card , as well as the authorisation they get when i actually use it ?'
'waterboarding sounds like a lot of fun until you find out what it is'

SARCASTIC

"have you been reading long ? you are not very good at it . it has nothing to do with who i like , especially since i am not a fan of corbyn anyway . it ' s that in one case someone was literally slapped in the face , and in the other someone wore a milkshake . battery > being annoying"
'wish one of the many people dressed as killers were actually one n killed me'
'is it even christmas if there isn t a fight with neighbours and a broken wrist ?'

Evaluation

Metrics

Label	Accuracy	F1	Precision	Recall
all	0.6618	0.3952	0.2891	0.6242

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("w11wo/bge-small-en-v1.5-isarcasm")
# Run inference
preds = model("last day in my twenties")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	19.8489	63

Label	Training Sample Count
NON_SARCASTIC	609
SARCASTIC	609

Training Hyperparameters

batch_size: (256, 16)
num_epochs: (3, 8)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (2e-05, 5e-06)
head_learning_rate: 0.002
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: True
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.2571	-
0.0172	50	0.251	-
0.0344	100	0.2556	-
0.0517	150	0.2513	-
0.0689	200	0.2531	-
0.0861	250	0.2518	-
0.1033	300	0.2553	-
0.1206	350	0.2501	-
0.1378	400	0.2546	-
0.1550	450	0.2506	-
0.1722	500	0.2317	-
0.1895	550	0.093	-
0.2067	600	0.0139	-
0.2239	650	0.0166	-
0.2411	700	0.0053	-
0.2584	750	0.0013	-
0.2756	800	0.0121	-
0.2928	850	0.0096	-
0.3100	900	0.0043	-
0.3272	950	0.0014	-
0.3445	1000	0.0009	-
0.3617	1050	0.0117	-
0.3789	1100	0.0144	-
0.3961	1150	0.0084	-
0.4134	1200	0.0006	-
0.4306	1250	0.0005	-
0.4478	1300	0.0081	-
0.4650	1350	0.0144	-
0.4823	1400	0.0045	-
0.4995	1450	0.0042	-
0.5167	1500	0.0005	-
0.5339	1550	0.003	-
0.5512	1600	0.0004	-
0.5684	1650	0.0005	-
0.5856	1700	0.0004	-
0.6028	1750	0.0004	-
0.6200	1800	0.0026	-
0.6373	1850	0.0004	-
0.6545	1900	0.0004	-
0.6717	1950	0.0003	-
0.6889	2000	0.0014	-
0.7062	2050	0.0004	-
0.7234	2100	0.0003	-
0.7406	2150	0.0003	-
0.7578	2200	0.0004	-
0.7751	2250	0.0003	-
0.7923	2300	0.0003	-
0.8095	2350	0.0003	-
0.8267	2400	0.0003	-
0.8440	2450	0.0003	-
0.8612	2500	0.0003	-
0.8784	2550	0.0003	-
0.8956	2600	0.0003	-
0.9128	2650	0.0003	-
0.9301	2700	0.0003	-
0.9473	2750	0.0004	-
0.9645	2800	0.0003	-
0.9817	2850	0.0003	-
0.9990	2900	0.0036	-
1.0162	2950	0.0003	-
1.0334	3000	0.0003	-
1.0506	3050	0.0002	-
1.0679	3100	0.0002	-
1.0851	3150	0.0002	-
1.1023	3200	0.0002	-
1.1195	3250	0.0002	-
1.1368	3300	0.0003	-
1.1540	3350	0.0004	-
1.1712	3400	0.0002	-
1.1884	3450	0.0002	-
1.2056	3500	0.0002	-
1.2229	3550	0.0002	-
1.2401	3600	0.0002	-
1.2573	3650	0.0009	-
1.2745	3700	0.0002	-
1.2918	3750	0.0002	-
1.3090	3800	0.0002	-
1.3262	3850	0.0002	-
1.3434	3900	0.0002	-
1.3607	3950	0.0002	-
1.3779	4000	0.0002	-
1.3951	4050	0.0002	-
1.4123	4100	0.0002	-
1.4296	4150	0.0002	-
1.4468	4200	0.0003	-
1.4640	4250	0.0002	-
1.4812	4300	0.0002	-
1.4984	4350	0.0002	-
1.5157	4400	0.0002	-
1.5329	4450	0.0002	-
1.5501	4500	0.0002	-
1.5673	4550	0.0002	-
1.5846	4600	0.0002	-
1.6018	4650	0.0002	-
1.6190	4700	0.0002	-
1.6362	4750	0.0002	-
1.6535	4800	0.0002	-
1.6707	4850	0.0002	-
1.6879	4900	0.0002	-
1.7051	4950	0.0002	-
1.7224	5000	0.0003	-
1.7396	5050	0.0002	-
1.7568	5100	0.0002	-
1.7740	5150	0.0002	-
1.7913	5200	0.0002	-
1.8085	5250	0.0002	-
1.8257	5300	0.0038	-
1.8429	5350	0.0002	-
1.8601	5400	0.0002	-
1.8774	5450	0.0002	-
1.8946	5500	0.0002	-
1.9118	5550	0.0002	-
1.9290	5600	0.0005	-
1.9463	5650	0.0002	-
1.9635	5700	0.0002	-
1.9807	5750	0.0002	-
1.9979	5800	0.0002	-
2.0152	5850	0.0001	-
2.0324	5900	0.0002	-
2.0496	5950	0.0002	-
2.0668	6000	0.0002	-
2.0841	6050	0.0002	-
2.1013	6100	0.0002	-
2.1185	6150	0.0002	-
2.1357	6200	0.0001	-
2.1529	6250	0.0002	-
2.1702	6300	0.0002	-
2.1874	6350	0.0001	-
2.2046	6400	0.0001	-
2.2218	6450	0.0001	-
2.2391	6500	0.0001	-
2.2563	6550	0.0001	-
2.2735	6600	0.0001	-
2.2907	6650	0.0001	-
2.3080	6700	0.0001	-
2.3252	6750	0.0001	-
2.3424	6800	0.0001	-
2.3596	6850	0.0001	-
2.3769	6900	0.0001	-
2.3941	6950	0.0001	-
2.4113	7000	0.0001	-
2.4285	7050	0.0001	-
2.4457	7100	0.0001	-
2.4630	7150	0.0001	-
2.4802	7200	0.0001	-
2.4974	7250	0.0001	-
2.5146	7300	0.0001	-
2.5319	7350	0.0001	-
2.5491	7400	0.0001	-
2.5663	7450	0.0001	-
2.5835	7500	0.0001	-
2.6008	7550	0.0001	-
2.6180	7600	0.0001	-
2.6352	7650	0.0001	-
2.6524	7700	0.0001	-
2.6697	7750	0.0001	-
2.6869	7800	0.0001	-
2.7041	7850	0.0001	-
2.7213	7900	0.0001	-
2.7385	7950	0.0001	-
2.7558	8000	0.0001	-
2.7730	8050	0.0001	-
2.7902	8100	0.0001	-
2.8074	8150	0.0001	-
2.8247	8200	0.0001	-
2.8419	8250	0.0001	-
2.8591	8300	0.0001	-
2.8763	8350	0.0001	-
2.8936	8400	0.0001	-
2.9108	8450	0.0001	-
2.9280	8500	0.0001	-
2.9452	8550	0.0001	-
2.9625	8600	0.0001	-
2.9797	8650	0.0001	-
2.9969	8700	0.0001	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.1
Sentence Transformers: 2.2.2
Transformers: 4.32.0
PyTorch: 2.1.1+cu121
Datasets: 2.14.5
Tokenizers: 0.13.3

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}
}

w11wo
/

bge-small-en-v1.5-isarcasm