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---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| sensitive | <ul><li>'Im Amie Taylaran from Pan-ay Clarin from Solo Parent Organization grateful and excited to receive the help you are giving.'</li><li>'I want to volunteer'</li><li>'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.'</li></ul> |
| other | <ul><li>'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?'</li><li>'In this community alcohol abuse is rampant. The men go out drinking and come home and beat their wives. They are getting seriously injured.'</li><li>"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."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9828 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
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")
```
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## 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}
}
```
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