metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      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?
  - text: >-
      I work in textile factory. My boss, he not nice. He do things I don't
      like. He make job not good, and my friends stop talk to me. I need help
  - text: >-
      In Bahay Toro QC  even though they did not wear face masks  there was not
      much news of COVID  there were those who felt symptoms but
      self-quarantined and there were also those who died who were told that
      they were COVID even though they were not. Only a few were reported dead
      in the area due to COVID  less than five. During the pandemic  the
      Barangay had curfew  social distancing  facemasks and alcohol. We received
      SAP  canned rice  alcohol and facemasks and money. If the number of COVID
      cases increases  the barangay is not ready and when it increases  that is
      just the time that they will be stricter. All of us in our family were
      able to be vaccinated and had booster shots apart from my younger brother.
      When it tightens again and there is a pandemic  unemployment and source of
      income will be a test. Focus more on providing immediate assistance in the
      midst of a pandemic
  - text: >-
      There is a child here who will be married soon. Please send help urgently.
      She is only 13. It is not the first time he has done this.
  - text: Drenage problem here in lilanda
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9827586206896551
            name: Accuracy

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."

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.

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

@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}
}