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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: poopchute
- text: Made
- text: prox
- text: What happens, uncle, everything in order?
- text: I need Maritima Avenue to reduce congestion
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
---
# English Input Classifier
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 4 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 |
|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| request | <ul><li>'necessary lingerie'</li><li>'necessary material for today'</li><li>'I finished the room 234'</li></ul> |
| conversation | <ul><li>"What's up, uncle, all good?"</li><li>'Good, how is the thing going?!'</li><li>'Hello how are you'</li></ul> |
| help | <ul><li>'Please help'</li><li>"Help I don't know what I can do"</li><li>'Hello, what can I do'</li></ul> |
| censorship | <ul><li>'You are a useless complete, you are useless'</li><li>'Always saying stupidities, better shut up'</li><li>'Your single existence is a shame'</li></ul> |
## 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("monentiadev/en-input-classifier")
# Run inference
preds = model("Hello")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 5.1483 | 40 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| censorship | 576 |
| conversation | 123 |
| help | 204 |
| request | 520 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1 | 0.3104 | - |
| 0.1124 | 50 | 0.3267 | - |
| 0.2247 | 100 | 0.2008 | - |
| 0.3371 | 150 | 0.0842 | - |
| 0.4494 | 200 | 0.0218 | - |
| 0.5618 | 250 | 0.0103 | - |
| 0.6742 | 300 | 0.0052 | - |
| 0.7865 | 350 | 0.0034 | - |
| 0.8989 | 400 | 0.0025 | - |
| 1.0112 | 450 | 0.0019 | - |
| 1.1236 | 500 | 0.0019 | - |
| 1.2360 | 550 | 0.0017 | - |
| 1.3483 | 600 | 0.001 | - |
| 1.4607 | 650 | 0.001 | - |
| 1.5730 | 700 | 0.0011 | - |
| 1.6854 | 750 | 0.0009 | - |
| 1.7978 | 800 | 0.001 | - |
| 1.9101 | 850 | 0.0007 | - |
| 2.0225 | 900 | 0.0008 | - |
| 2.1348 | 950 | 0.0007 | - |
| 2.2472 | 1000 | 0.0007 | - |
| 2.3596 | 1050 | 0.0006 | - |
| 2.4719 | 1100 | 0.0006 | - |
| 2.5843 | 1150 | 0.0006 | - |
| 2.6966 | 1200 | 0.0006 | - |
| 2.8090 | 1250 | 0.0006 | - |
| 2.9213 | 1300 | 0.0006 | - |
### Framework Versions
- Python: 3.10.0
- SetFit: 1.1.2
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.7.1+cu126
- Datasets: 2.19.2
- Tokenizers: 0.21.2
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