SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression 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
0	', metal unless it was 70s oldskool' "do whichever you think would be best then: if you rename the account, just let me know, and i'll go over there to recreate it; but if you'd prefer to rename the account, recreate it, and send me the password which i can then change, that's fine with me." '" no, it was a far-too-much-of-an-in-joke on the fact that principle→principal is usually one of the first things the fa regulars jump on (along with the dreaded spaced em dash)\xa0–\xa0scent "'
1	"i'm going to kill you zink dawg your a scrap and a fag and you need to die. i'm going to kill you if someone else doesn't. you better keep protecting your user space because i will keep vandalizing it." "hope your head gets cut off and someone wipes there ass with it and then stabs you in your heart!!! no one can keep me from here you dumb piece of shit, not yamla's ass ass or you!!! now hurry up and sit in some traffic so i can see your head roll down the street" 'unblock me now you piece of shit! or i will find you and kill you, slowly and painfully!'

Label

Examples

', metal unless it was 70s oldskool'
"do whichever you think would be best then: if you rename the account, just let me know, and i'll go over there to recreate it; but if you'd prefer to rename the account, recreate it, and send me the password which i can then change, that's fine with me."
'" no, it was a far-too-much-of-an-in-joke on the fact that principle→principal is usually one of the first things the fa regulars jump on (along with the dreaded spaced em dash)\xa0–\xa0scent "'

"i'm going to kill you zink dawg your a scrap and a fag and you need to die. i'm going to kill you if someone else doesn't. you better keep protecting your user space because i will keep vandalizing it."
"hope your head gets cut off and someone wipes there ass with it and then stabs you in your heart!!! no one can keep me from here you dumb piece of shit, not yamla's ass ass or you!!! now hurry up and sit in some traffic so i can see your head roll down the street"
'unblock me now you piece of shit! or i will find you and kill you, slowly and painfully!'

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("waterabbit114/my-setfit-classifier_threat")
# Run inference
preds = model("\"   link   thanks for fixing that disambiguation link on usher's album ) flash; \"")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	50.65	426

Label	Training Sample Count
0	10
1	10

Training Hyperparameters

batch_size: (1, 1)
num_epochs: (10, 10)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
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
0.0013	1	0.192	-
0.0625	50	0.0173	-
0.125	100	0.0013	-
0.1875	150	0.0024	-
0.25	200	0.0002	-
0.3125	250	0.0	-
0.375	300	0.0	-
0.4375	350	0.0006	-
0.5	400	0.0003	-
0.5625	450	0.0001	-
0.625	500	0.0001	-
0.6875	550	0.0002	-
0.75	600	0.0008	-
0.8125	650	0.0002	-
0.875	700	0.0001	-
0.9375	750	0.0009	-
1.0	800	0.0001	-
1.0625	850	0.0001	-
1.125	900	0.0001	-
1.1875	950	0.0	-
1.25	1000	0.0	-
1.3125	1050	0.0	-
1.375	1100	0.0001	-
1.4375	1150	0.0	-
1.5	1200	0.0	-
1.5625	1250	0.0	-
1.625	1300	0.0	-
1.6875	1350	0.0	-
1.75	1400	0.0003	-
1.8125	1450	0.0001	-
1.875	1500	0.0	-
1.9375	1550	0.0001	-
2.0	1600	0.0	-
2.0625	1650	0.0	-
2.125	1700	0.0001	-
2.1875	1750	0.0	-
2.25	1800	0.0	-
2.3125	1850	0.0	-
2.375	1900	0.0	-
2.4375	1950	0.0	-
2.5	2000	0.0	-
2.5625	2050	0.0	-
2.625	2100	0.0001	-
2.6875	2150	0.0	-
2.75	2200	0.0	-
2.8125	2250	0.0002	-
2.875	2300	0.0	-
2.9375	2350	0.0	-
3.0	2400	0.0002	-
3.0625	2450	0.0	-
3.125	2500	0.0001	-
3.1875	2550	0.0001	-
3.25	2600	0.0001	-
3.3125	2650	0.0	-
3.375	2700	0.0	-
3.4375	2750	0.0	-
3.5	2800	0.0	-
3.5625	2850	0.0	-
3.625	2900	0.0	-
3.6875	2950	0.0	-
3.75	3000	0.0	-
3.8125	3050	0.0	-
3.875	3100	0.0002	-
3.9375	3150	0.0	-
4.0	3200	0.0	-
4.0625	3250	0.0001	-
4.125	3300	0.0001	-
4.1875	3350	0.0	-
4.25	3400	0.0004	-
4.3125	3450	0.0001	-
4.375	3500	0.0001	-
4.4375	3550	0.0001	-
4.5	3600	0.0	-
4.5625	3650	0.0	-
4.625	3700	0.0	-
4.6875	3750	0.0	-
4.75	3800	0.0	-
4.8125	3850	0.0	-
4.875	3900	0.0001	-
4.9375	3950	0.0	-
5.0	4000	0.0	-
5.0625	4050	0.0	-
5.125	4100	0.0	-
5.1875	4150	0.0	-
5.25	4200	0.0	-
5.3125	4250	0.0002	-
5.375	4300	0.0	-
5.4375	4350	0.0	-
5.5	4400	0.0	-
5.5625	4450	0.0001	-
5.625	4500	0.0	-
5.6875	4550	0.0	-
5.75	4600	0.0002	-
5.8125	4650	0.0	-
5.875	4700	0.0	-
5.9375	4750	0.0	-
6.0	4800	0.0	-
6.0625	4850	0.0	-
6.125	4900	0.0	-
6.1875	4950	0.0	-
6.25	5000	0.0	-
6.3125	5050	0.0	-
6.375	5100	0.0001	-
6.4375	5150	0.0	-
6.5	5200	0.0	-
6.5625	5250	0.0	-
6.625	5300	0.0	-
6.6875	5350	0.0	-
6.75	5400	0.0	-
6.8125	5450	0.0	-
6.875	5500	0.0	-
6.9375	5550	0.0	-
7.0	5600	0.0	-
7.0625	5650	0.0	-
7.125	5700	0.0	-
7.1875	5750	0.0	-
7.25	5800	0.0001	-
7.3125	5850	0.0	-
7.375	5900	0.0	-
7.4375	5950	0.0	-
7.5	6000	0.0	-
7.5625	6050	0.0	-
7.625	6100	0.0	-
7.6875	6150	0.0	-
7.75	6200	0.0	-
7.8125	6250	0.0	-
7.875	6300	0.0	-
7.9375	6350	0.0	-
8.0	6400	0.0	-
8.0625	6450	0.0	-
8.125	6500	0.0	-
8.1875	6550	0.0	-
8.25	6600	0.0	-
8.3125	6650	0.0	-
8.375	6700	0.0	-
8.4375	6750	0.0	-
8.5	6800	0.0	-
8.5625	6850	0.0	-
8.625	6900	0.0	-
8.6875	6950	0.0	-
8.75	7000	0.0	-
8.8125	7050	0.0	-
8.875	7100	0.0	-
8.9375	7150	0.0	-
9.0	7200	0.0	-
9.0625	7250	0.0	-
9.125	7300	0.0	-
9.1875	7350	0.0	-
9.25	7400	0.0	-
9.3125	7450	0.0	-
9.375	7500	0.0	-
9.4375	7550	0.0	-
9.5	7600	0.0	-
9.5625	7650	0.0	-
9.625	7700	0.0	-
9.6875	7750	0.0	-
9.75	7800	0.0	-
9.8125	7850	0.0	-
9.875	7900	0.0	-
9.9375	7950	0.0	-
10.0	8000	0.0	-

Framework Versions

Python: 3.11.7
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.35.2
PyTorch: 2.1.1+cu121
Datasets: 2.14.5
Tokenizers: 0.15.1

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}
}

waterabbit114
/

my-setfit-classifier_threat