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 OneVsRestClassifier 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 OneVsRestClassifier instance
Maximum Sequence Length: 512 tokens

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Evaluation

Metrics

Label	Accuracy
all	0.8041

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")
# 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	69.1481	898

Training Hyperparameters

batch_size: (1, 1)
num_epochs: (1, 1)
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
0.0005	1	0.2094	-
0.0231	50	0.033	-
0.0463	100	0.0439	-
0.0694	150	0.001	-
0.0926	200	0.0245	-
0.1157	250	0.0008	-
0.1389	300	0.0001	-
0.1620	350	0.0	-
0.1852	400	0.0012	-
0.2083	450	0.0	-
0.2315	500	0.0002	-
0.2546	550	0.0006	-
0.2778	600	0.002	-
0.3009	650	0.0044	-
0.3241	700	0.0015	-
0.3472	750	0.0007	-
0.3704	800	0.0001	-
0.3935	850	0.0001	-
0.4167	900	0.0001	-
0.4398	950	0.0004	-
0.4630	1000	0.0001	-
0.4861	1050	0.0001	-
0.5093	1100	0.0	-
0.5324	1150	0.0052	-
0.5556	1200	0.0002	-
0.5787	1250	0.0	-
0.6019	1300	0.0003	-
0.625	1350	0.0	-
0.6481	1400	0.0001	-
0.6713	1450	0.0	-
0.6944	1500	0.0	-
0.7176	1550	0.0	-
0.7407	1600	0.0002	-
0.7639	1650	0.0001	-
0.7870	1700	0.0011	-
0.8102	1750	0.0001	-
0.8333	1800	0.0	-
0.8565	1850	0.0001	-
0.8796	1900	0.0006	-
0.9028	1950	0.0002	-
0.9259	2000	0.0002	-
0.9491	2050	0.0	-
0.9722	2100	0.0001	-
0.9954	2150	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