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skylord/
setfit-bge-small-v1.5-sst2-8-shot-talk2loop

Name: setfit-bge-small-v1.5-sst2-8-shot-talk2loop
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

BAAI/bge-small-en-v1.5

finetune

model-index

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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 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-small-en-v1.5
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

sensitive

'Im Amie Taylaran from Pan-ay Clarin from Solo Parent Organization grateful and excited to receive the help you are giving.'
'I want to volunteer'
'There is now a growing popular street Pennsylvania street in the annex Phase 3 of Greenland Executive Village for bikers walkers joggers every morning when the weather is fair. I presume they are groups of retirees matrons sports enthusiasts an even dance exercisers. They all wear face masks for health protection against COVID-19 infection. My concern is this: face masks are just thrown away after use when these fitness buffs are done with their morning binges. Face masks thrown on the pavement of the street the sidewalks and the grass field. Health fitness aficionados they all are but careless with the proper disposal of their face masks.'

other

'There is a man here forced us the girls in the house to have sex with him. He took videos of us and now he is asking for money. Can someone help us?'
'In this community alcohol abuse is rampant. The men go out drinking and come home and beat their wives. They are getting seriously injured.'
"I find myself in a very challenging situation - I've experienced sexual abuse at work. If anyone has gone through something similar, I would appreciate your guidance and support. It's tough, but we're stronger together."

Evaluation

Metrics

Label	Accuracy
all	0.9828

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("skylord/setfit-bge-small-v1.5-sst2-8-shot-talk2loop")
# Run inference
preds = model("Drenage problem here in lilanda")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	38.0	171

Label	Training Sample Count
sensitive	8
other	8

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (10, 10)
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
0.2	1	0.1988	-
10.0	50	0.019	-

Framework Versions

Python: 3.10.11
SetFit: 1.0.3
Sentence Transformers: 2.3.1
Transformers: 4.37.2
PyTorch: 2.2.0+cu121
Datasets: 2.16.1
Tokenizers: 0.15.1

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}
}