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larshubacher/
setfitdeepset_gelectra-base

Name: setfitdeepset_gelectra-base
Availability: InStock

setfit

safetensors

electra

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

deepset/gelectra-base

finetune

model-index

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SetFit with deepset/gelectra-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses deepset/gelectra-base 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: deepset/gelectra-base
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	0.0237

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("larshubacher/setfitdeepset_gelectra-base")
# Run inference
preds = model("Ein sehr schöner heller Shop, eine sehr freundliche Bedienung")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	50.5234	331

Training Hyperparameters

batch_size: (16, 16)
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
0.0031	1	0.4768	-
0.1562	50	0.244	-
0.3125	100	0.1326	-
0.4688	150	0.1522	-
0.625	200	0.0991	-
0.7812	250	0.1375	-
0.9375	300	0.1216	-

Framework Versions

Python: 3.11.11
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.40.2
PyTorch: 2.5.1+cu121
Datasets: 2.21.0
Tokenizers: 0.19.1

Citation

BibTeX

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}
}