README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: There is talk of five years of austerity.
- text: Vadym Boychenko, mayor of Mariupol, said that Russian forces have killed twice
    as many of the city's residents in the two months of the war as Nazi Germany did
    in its two years of occupation.
- text: But by allowing Kosovo to separate relatively peacefully from Serbia, it caused
    little lasting damage.
- text: Dubbed Satan 2 by Western analysts, the Sarmat missile is formidable, purportedly
    designed to deploy numerous nuclear warheads or other weapons from its main 100-tonne
    missile at hypersonic speed.
- text: Hagel said that the "military prowess" of the Islamic State, coupled with
    its deep sources of financing, poses an unprecedented threat to the United States.
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: 0.8302631578947368
      name: F1_Macro
    - type: f1_binary
      value: 0.8105263157894737
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'His first comment was simply a reflex allusion to the fact that French policy is to stay apart from the common military arrangements of the rest of Western Europe.'</li><li>"The President and Chancellor agreed during their private meeting on the importance of modernizing the West's nuclear defenses and on the threat of growing Soviet nuclear power, French officials said."</li><li>'Senate ratification hearings are scheduled to start on January 25.'</li></ul> |
| positive | <ul><li>'And you have an escalation in an enormously tense relationship between two countries that have fought three wars, which means you have an enormous amount of destructive capability on a fairly short trigger.'</li><li>"It was August 1991, the days and nights of the failed coup d'etat which was meant to restore Stalinism but which brought Boris Yeltsin to power."</li><li>'A crisis of international security emerges.'</li></ul>                                    |

## Evaluation

### Metrics
| Label   | F1_Macro | F1_Binary |
|:--------|:---------|:----------|
| **all** | 0.8303   | 0.8105    |

## 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_f2llm_selection")
# Run inference
preds = model("There is talk of five years of austerity.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 22.4121 | 74  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 499                   |
| positive | 360                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0074 | 1    | 0.4596        | -               |
| 0.1481 | 20   | 0.4139        | -               |
| 0.2963 | 40   | 0.3995        | -               |
| 0.4444 | 60   | 0.369         | -               |
| 0.5926 | 80   | 0.3209        | -               |
| 0.7407 | 100  | 0.2825        | -               |
| 0.8889 | 120  | 0.2615        | -               |
| 1.0    | 135  | -             | 0.2649          |
| 1.0370 | 140  | 0.2548        | -               |
| 1.1852 | 160  | 0.2496        | -               |
| 1.3333 | 180  | 0.245         | -               |
| 1.4815 | 200  | 0.2373        | -               |
| 1.6296 | 220  | 0.2326        | -               |
| 1.7778 | 240  | 0.228         | -               |
| 1.9259 | 260  | 0.2179        | -               |
| 2.0    | 270  | -             | 0.2277          |
| 2.0741 | 280  | 0.2057        | -               |
| 2.2222 | 300  | 0.1982        | -               |
| 2.3704 | 320  | 0.1884        | -               |
| 2.5185 | 340  | 0.1752        | -               |
| 2.6667 | 360  | 0.1639        | -               |
| 2.8148 | 380  | 0.1526        | -               |
| 2.9630 | 400  | 0.1425        | -               |
| 3.0    | 405  | -             | 0.1906          |
| 3.1111 | 420  | 0.1334        | -               |
| 3.2593 | 440  | 0.1157        | -               |
| 3.4074 | 460  | 0.1075        | -               |
| 3.5556 | 480  | 0.0966        | -               |
| 3.7037 | 500  | 0.0866        | -               |
| 3.8519 | 520  | 0.0746        | -               |
| 4.0    | 540  | 0.0704        | 0.1889          |
| 4.1481 | 560  | 0.0666        | -               |
| 4.2963 | 580  | 0.0603        | -               |
| 4.4444 | 600  | 0.0533        | -               |
| 4.5926 | 620  | 0.0514        | -               |
| 4.7407 | 640  | 0.0519        | -               |
| 4.8889 | 660  | 0.0506        | -               |
| 5.0    | 675  | -             | 0.1930          |

### 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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