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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: poopchute |
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- text: Made |
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- text: prox |
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- text: What happens, uncle, everything in order? |
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- text: I need Maritima Avenue to reduce congestion |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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--- |
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# English Input Classifier |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 256 tokens |
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- **Number of Classes:** 4 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| request | <ul><li>'necessary lingerie'</li><li>'necessary material for today'</li><li>'I finished the room 234'</li></ul> | |
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| 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> | |
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| 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> | |
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| 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> | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("monentiadev/en-input-classifier") |
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# Run inference |
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preds = model("Hello") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 1 | 5.1483 | 40 | |
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| Label | Training Sample Count | |
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|:-------------|:----------------------| |
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| censorship | 576 | |
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| conversation | 123 | |
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| help | 204 | |
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| request | 520 | |
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### Training Hyperparameters |
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- batch_size: (128, 128) |
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- num_epochs: (3, 3) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0022 | 1 | 0.3104 | - | |
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| 0.1124 | 50 | 0.3267 | - | |
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| 0.2247 | 100 | 0.2008 | - | |
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| 0.3371 | 150 | 0.0842 | - | |
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| 0.4494 | 200 | 0.0218 | - | |
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| 0.5618 | 250 | 0.0103 | - | |
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| 0.6742 | 300 | 0.0052 | - | |
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| 0.7865 | 350 | 0.0034 | - | |
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| 0.8989 | 400 | 0.0025 | - | |
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| 1.0112 | 450 | 0.0019 | - | |
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| 1.1236 | 500 | 0.0019 | - | |
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| 1.2360 | 550 | 0.0017 | - | |
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| 1.3483 | 600 | 0.001 | - | |
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| 1.4607 | 650 | 0.001 | - | |
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| 1.5730 | 700 | 0.0011 | - | |
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| 1.6854 | 750 | 0.0009 | - | |
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| 1.7978 | 800 | 0.001 | - | |
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| 1.9101 | 850 | 0.0007 | - | |
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| 2.0225 | 900 | 0.0008 | - | |
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| 2.1348 | 950 | 0.0007 | - | |
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| 2.2472 | 1000 | 0.0007 | - | |
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| 2.3596 | 1050 | 0.0006 | - | |
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| 2.4719 | 1100 | 0.0006 | - | |
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| 2.5843 | 1150 | 0.0006 | - | |
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| 2.6966 | 1200 | 0.0006 | - | |
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| 2.8090 | 1250 | 0.0006 | - | |
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| 2.9213 | 1300 | 0.0006 | - | |
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### Framework Versions |
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- Python: 3.10.0 |
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- SetFit: 1.1.2 |
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- Sentence Transformers: 5.0.0 |
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- Transformers: 4.53.1 |
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- PyTorch: 2.7.1+cu126 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.21.2 |
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