---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Everyone could play a part, he said, adding that if industries and private
    individuals could reduce gas consumption by 10%, it would help avoid an emergency.
- text: Can we really believe that the nuclear weapon will never be used again?
- text: In Washington, President George Bush said that Mr Gorbachev's decision showed
    statesmanship and strengthened efforts to build 'enduring relationships based
    on co-operation.'
- text: A similar paralysis of adequate American response would follow a Soviet threat
    to Europe.
- text: You know, I’ve always found about the British, they don’t need much help.
metrics:
- f1_macro
- f1_binary
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: codefuse-ai/F2LLM-v2-80M
model-index:
- name: SetFit with codefuse-ai/F2LLM-v2-80M
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1_macro
      value: 0.7436245252306022
      name: F1_Macro
    - type: f1_binary
      value: 0.631578947368421
      name: F1_Binary
---

# SetFit with codefuse-ai/F2LLM-v2-80M

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M) 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:** [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 40960 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'He and other thinkers anguishing over the inadequacy of global mechanisms advocate a new set of principles which would allow for intervention in a nation in crisis.'</li><li>'Plus, there are escalating concerns over cyberthreats, territorial disputes in the South China Sea and the spread of Ebola virus.'</li><li>'Now, says Urban, Russia, China and India have such strong conventional forces, and America has cut its forces so much, that in the event of a conflict "the US would be left with the choice of nuclear escalation or backing down".'</li></ul> |
| negative | <ul><li>'Positioning nuclear missiles in the exclave would give the Kremlin the ability to strike most central European capitals.'</li><li>'Discard the Trident missile programme.'</li><li>"We're really going to choose between what I consider to be an old way of governing ourselves, of high levels of spending, high taxes, an ever more intrusive bureaucracy, or a new course, a new era if you will, and Governor Bush and I want to offer that new course of action."</li></ul>                                                                                          |

## Evaluation

### Metrics
| Label   | F1_Macro | F1_Binary |
|:--------|:---------|:----------|
| **all** | 0.7436   | 0.6316    |

## 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("fefofico/crisis_trained_allnat")
# Run inference
preds = model("Can we really believe that the nuclear weapon will never be used again?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 24.5399 | 67  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 361                   |
| positive | 178                   |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (1e-06, 1e-06)
- head_learning_rate: 0.003
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0233 | 1    | 0.4268        | -               |
| 0.4651 | 20   | 0.4109        | -               |
| 0.0118 | 1    | 0.3813        | -               |
| 0.2353 | 20   | 0.3829        | -               |
| 0.4706 | 40   | 0.3552        | -               |
| 0.7059 | 60   | 0.3192        | -               |
| 0.9412 | 80   | 0.2901        | -               |
| 1.0    | 85   | -             | 0.2554          |
| 1.1765 | 100  | 0.2728        | -               |
| 1.4118 | 120  | 0.2668        | -               |
| 1.6471 | 140  | 0.2596        | -               |
| 1.8824 | 160  | 0.2556        | -               |
| 2.0    | 170  | -             | 0.2423          |
| 2.1176 | 180  | 0.2489        | -               |
| 2.3529 | 200  | 0.2442        | -               |
| 2.5882 | 220  | 0.239         | -               |
| 2.8235 | 240  | 0.2354        | -               |
| 3.0    | 255  | -             | 0.2356          |
| 3.0588 | 260  | 0.2281        | -               |
| 3.2941 | 280  | 0.2244        | -               |
| 3.5294 | 300  | 0.2227        | -               |
| 3.7647 | 320  | 0.2175        | -               |
| 4.0    | 340  | 0.2114        | 0.2313          |
| 4.2353 | 360  | 0.2103        | -               |
| 4.4706 | 380  | 0.2055        | -               |
| 4.7059 | 400  | 0.2048        | -               |
| 4.9412 | 420  | 0.2029        | -               |
| 5.0    | 425  | -             | 0.2297          |

### Framework Versions
- Python: 3.12.13
- SetFit: 1.1.3
- Sentence Transformers: 3.4.1
- Transformers: 4.57.6
- PyTorch: 2.11.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2

## 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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