---
language:
- en
license: apache-2.0
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- sst2
metrics:
- precision
- recall
- f1
widget:
- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
    they are magnificent . '
- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
    plain bored . '
- text: 'the band ''s courage in the face of official repression is inspiring , especially
    for aging hippies ( this one included ) . '
- text: 'a fast , funny , highly enjoyable movie . '
- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
    ... ( quills ) did by showing them . '
pipeline_tag: text-classification
co2_eq_emissions:
  emissions: 2.768308759172054
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.072
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: sst2
      type: sst2
      split: test
    metrics:
    - type: accuracy
      value: 0.7512953367875648
      name: Accuracy
---

# SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset 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:** 2 classes
- **Training Dataset:** [sst2](https://huggingface.co/datasets/sst2)
- **Language:** en
- **License:** apache-2.0

### 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                                                                                                                                                                               |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'a tough pill to swallow and '</li><li>'indignation '</li><li>'that the typical hollywood disregard for historical truth and realism is at work here '</li></ul>               |
| positive | <ul><li>"a moving experience for people who have n't read the book "</li><li>'in the best possible senses of both those words '</li><li>'to serve the work especially well '</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7513   |

## 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 🤗 Hub
model = SetFitModel.from_pretrained("tomaarsen/setfit-all-MiniLM-L6-v2-sst2-8-shot")
# Run inference
preds = model("a fast , funny , highly enjoyable movie . ")
```
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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 10.2812 | 36  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 32                    |
| positive | 32                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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
- load_best_model_at_end: True

### Training Results
| Epoch      | Step   | Training Loss | Validation Loss |
|:----------:|:------:|:-------------:|:---------------:|
| 0.0076     | 1      | 0.3787        | -               |
| 0.0758     | 10     | 0.2855        | -               |
| 0.1515     | 20     | 0.3458        | 0.29            |
| 0.2273     | 30     | 0.2496        | -               |
| 0.3030     | 40     | 0.2398        | 0.2482          |
| 0.3788     | 50     | 0.2068        | -               |
| 0.4545     | 60     | 0.2471        | 0.244           |
| 0.5303     | 70     | 0.2053        | -               |
| **0.6061** | **80** | **0.1802**    | **0.2361**      |
| 0.6818     | 90     | 0.0767        | -               |
| 0.7576     | 100    | 0.0279        | 0.2365          |
| 0.8333     | 110    | 0.0192        | -               |
| 0.9091     | 120    | 0.0095        | 0.2527          |
| 0.9848     | 130    | 0.0076        | -               |
| 1.0606     | 140    | 0.0082        | 0.2651          |
| 1.1364     | 150    | 0.0068        | -               |
| 1.2121     | 160    | 0.0052        | 0.2722          |
| 1.2879     | 170    | 0.0029        | -               |
| 1.3636     | 180    | 0.0042        | 0.273           |
| 1.4394     | 190    | 0.0026        | -               |
| 1.5152     | 200    | 0.0036        | 0.2761          |
| 1.5909     | 210    | 0.0044        | -               |
| 1.6667     | 220    | 0.0027        | 0.2796          |
| 1.7424     | 230    | 0.0025        | -               |
| 1.8182     | 240    | 0.0025        | 0.2817          |
| 1.8939     | 250    | 0.003         | -               |
| 1.9697     | 260    | 0.0026        | 0.2817          |
| 2.0455     | 270    | 0.0035        | -               |
| 2.1212     | 280    | 0.002         | 0.2816          |
| 2.1970     | 290    | 0.0023        | -               |
| 2.2727     | 300    | 0.0016        | 0.2821          |
| 2.3485     | 310    | 0.0023        | -               |
| 2.4242     | 320    | 0.0015        | 0.2838          |
| 2.5        | 330    | 0.0014        | -               |
| 2.5758     | 340    | 0.002         | 0.2842          |
| 2.6515     | 350    | 0.002         | -               |
| 2.7273     | 360    | 0.0013        | 0.2847          |
| 2.8030     | 370    | 0.0009        | -               |
| 2.8788     | 380    | 0.0018        | 0.2857          |
| 2.9545     | 390    | 0.0016        | -               |

* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.003 kg of CO2
- **Hours Used**: 0.072 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.0.dev0
- Sentence Transformers: 2.2.2
- Transformers: 4.29.0
- PyTorch: 1.13.1+cu117
- Datasets: 2.15.0
- Tokenizers: 0.13.3

## 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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