---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Federal Office for Radiation Protection established a new monitoring network
around nuclear waste storage sites.
- text: how could we imagine these mechanisms to be implemented within a nato-based
missile defence system.
- text: president putin said that the precondition for a ceasefire is that ukraine
should give up even more land, to give up all the four provinces that russia has
annexed without controlling.
- text: and helped protect and defend turkey’s territory and citizens against missile
attacks.
- text: let me first of all say that we take nuclear issues extremely seriously.
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: 0.9105329220295713
name: F1_Macro
- type: f1_binary
value: 0.8988235294117647
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
### 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 | - 'actually, we have decided in nato at the last summit that we should explore the possibilities to integrate russian missile defence systems in our missile defence system, which i think has become even more easy after the u.s. has presented new missile defence plans.'
- 'and we see, of course, the risk of proliferation of nuclear weapons.'
- 'there are of course opportunities and we need to engage with china on issues like climate change, arms control.'
|
| negative | - 'We welcome the successful achievement of a draft Chemical Weapons Convention.'
- 'in practice, this means that, in addition to reinforcing cooperation with our current partners, we should look to enhance our relations with countries such as australia , new zealand , japan and south korea.'
- 'this is not about militarizing space.'
|
## Evaluation
### Metrics
| Label | F1_Macro | F1_Binary |
|:--------|:---------|:----------|
| **all** | 0.9105 | 0.8988 |
## 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/nuclear_trained_f2llm")
# Run inference
preds = model("let me first of all say that we take nuclear issues extremely seriously.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 23.7926 | 132 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 1096 |
| positive | 857 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (1e-07, 1e-07)
- head_learning_rate: 0.0001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.35
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0016 | 1 | 0.3497 | - |
| 0.0327 | 20 | 0.3957 | - |
| 0.0655 | 40 | 0.3689 | - |
| 0.0982 | 60 | 0.3778 | - |
| 0.1309 | 80 | 0.3446 | - |
| 0.1637 | 100 | 0.3345 | - |
| 0.1964 | 120 | 0.3217 | - |
| 0.2291 | 140 | 0.3038 | - |
| 0.2619 | 160 | 0.2764 | - |
| 0.2946 | 180 | 0.27 | - |
| 0.3273 | 200 | 0.2596 | - |
| 0.3601 | 220 | 0.2575 | - |
| 0.3928 | 240 | 0.2528 | - |
| 0.4255 | 260 | 0.2507 | - |
| 0.4583 | 280 | 0.2427 | - |
| 0.4910 | 300 | 0.2347 | - |
| 0.5237 | 320 | 0.2226 | - |
| 0.5565 | 340 | 0.2094 | - |
| 0.5892 | 360 | 0.1975 | - |
| 0.6219 | 380 | 0.1837 | - |
| 0.6547 | 400 | 0.1729 | - |
| 0.6874 | 420 | 0.1646 | - |
| 0.7201 | 440 | 0.1516 | - |
| 0.7529 | 460 | 0.1392 | - |
| 0.7856 | 480 | 0.1303 | - |
| 0.8183 | 500 | 0.1265 | - |
| 0.8511 | 520 | 0.1156 | - |
| 0.8838 | 540 | 0.1149 | - |
| 0.9165 | 560 | 0.112 | - |
| 0.9493 | 580 | 0.1041 | - |
| 0.9820 | 600 | 0.1007 | - |
| 1.0 | 611 | - | 0.1073 |
| 1.0147 | 620 | 0.0926 | - |
| 1.0475 | 640 | 0.076 | - |
| 1.0802 | 660 | 0.0862 | - |
| 1.1129 | 680 | 0.078 | - |
| 1.1457 | 700 | 0.0745 | - |
| 1.1784 | 720 | 0.0676 | - |
| 1.2111 | 740 | 0.0524 | - |
| 1.2439 | 760 | 0.0585 | - |
| 1.2766 | 780 | 0.0506 | - |
| 1.3093 | 800 | 0.0419 | - |
| 1.3421 | 820 | 0.0446 | - |
| 1.3748 | 840 | 0.04 | - |
| 1.4075 | 860 | 0.0349 | - |
| 1.4403 | 880 | 0.0353 | - |
| 1.4730 | 900 | 0.0259 | - |
| 1.5057 | 920 | 0.0273 | - |
| 1.5385 | 940 | 0.0252 | - |
| 1.5712 | 960 | 0.0247 | - |
| 1.6039 | 980 | 0.0157 | - |
| 1.6367 | 1000 | 0.0172 | - |
| 1.6694 | 1020 | 0.0142 | - |
| 1.7021 | 1040 | 0.0136 | - |
| 1.7349 | 1060 | 0.0144 | - |
| 1.7676 | 1080 | 0.0111 | - |
| 1.8003 | 1100 | 0.0074 | - |
| 1.8331 | 1120 | 0.0103 | - |
| 1.8658 | 1140 | 0.0118 | - |
| 1.8985 | 1160 | 0.0098 | - |
| 1.9313 | 1180 | 0.0071 | - |
| 1.9640 | 1200 | 0.0082 | - |
| 1.9967 | 1220 | 0.0092 | - |
| 2.0 | 1222 | - | 0.1244 |
| 2.0295 | 1240 | 0.0049 | - |
| 2.0622 | 1260 | 0.0063 | - |
| 2.0949 | 1280 | 0.0047 | - |
| 2.1277 | 1300 | 0.0062 | - |
| 2.1604 | 1320 | 0.0053 | - |
| 2.1931 | 1340 | 0.0048 | - |
| 2.2259 | 1360 | 0.0042 | - |
| 2.2586 | 1380 | 0.0046 | - |
| 2.2913 | 1400 | 0.0053 | - |
| 2.3241 | 1420 | 0.007 | - |
| 2.3568 | 1440 | 0.0063 | - |
| 2.3895 | 1460 | 0.0044 | - |
| 2.4223 | 1480 | 0.0047 | - |
| 2.4550 | 1500 | 0.0033 | - |
| 2.4877 | 1520 | 0.0039 | - |
| 2.5205 | 1540 | 0.0069 | - |
| 2.5532 | 1560 | 0.004 | - |
| 2.5859 | 1580 | 0.0038 | - |
| 2.6187 | 1600 | 0.0031 | - |
| 2.6514 | 1620 | 0.005 | - |
| 2.6841 | 1640 | 0.0028 | - |
| 2.7169 | 1660 | 0.0056 | - |
| 2.7496 | 1680 | 0.0056 | - |
| 2.7823 | 1700 | 0.005 | - |
| 2.8151 | 1720 | 0.0045 | - |
| 2.8478 | 1740 | 0.0038 | - |
| 2.8805 | 1760 | 0.0049 | - |
| 2.9133 | 1780 | 0.0051 | - |
| 2.9460 | 1800 | 0.0031 | - |
| 2.9787 | 1820 | 0.0021 | - |
| 3.0 | 1833 | - | 0.1320 |
| 3.0115 | 1840 | 0.0027 | - |
| 3.0442 | 1860 | 0.005 | - |
| 3.0769 | 1880 | 0.004 | - |
| 3.1097 | 1900 | 0.0031 | - |
| 3.1424 | 1920 | 0.0032 | - |
| 3.1751 | 1940 | 0.0038 | - |
| 3.2079 | 1960 | 0.0045 | - |
| 3.2406 | 1980 | 0.0031 | - |
| 3.2733 | 2000 | 0.0044 | - |
| 3.3061 | 2020 | 0.0047 | - |
| 3.3388 | 2040 | 0.0022 | - |
| 3.3715 | 2060 | 0.0023 | - |
| 3.4043 | 2080 | 0.0027 | - |
| 3.4370 | 2100 | 0.0038 | - |
| 3.4697 | 2120 | 0.0011 | - |
| 3.5025 | 2140 | 0.0042 | - |
| 3.5352 | 2160 | 0.0027 | - |
| 3.5679 | 2180 | 0.0033 | - |
| 3.6007 | 2200 | 0.0042 | - |
| 3.6334 | 2220 | 0.0036 | - |
| 3.6661 | 2240 | 0.0046 | - |
| 3.6989 | 2260 | 0.0029 | - |
| 3.7316 | 2280 | 0.0041 | - |
| 3.7643 | 2300 | 0.003 | - |
| 3.7971 | 2320 | 0.0033 | - |
| 3.8298 | 2340 | 0.0033 | - |
| 3.8625 | 2360 | 0.0047 | - |
| 3.8953 | 2380 | 0.0041 | - |
| 3.9280 | 2400 | 0.0036 | - |
| 3.9607 | 2420 | 0.0037 | - |
| 3.9935 | 2440 | 0.0044 | - |
| 4.0 | 2444 | - | 0.1335 |
| 4.0262 | 2460 | 0.0034 | - |
| 4.0589 | 2480 | 0.0036 | - |
| 4.0917 | 2500 | 0.0041 | - |
| 4.1244 | 2520 | 0.0021 | - |
| 4.1571 | 2540 | 0.0032 | - |
| 4.1899 | 2560 | 0.002 | - |
| 4.2226 | 2580 | 0.0039 | - |
| 4.2553 | 2600 | 0.0035 | - |
| 4.2881 | 2620 | 0.0032 | - |
| 4.3208 | 2640 | 0.0032 | - |
| 4.3535 | 2660 | 0.0025 | - |
| 4.3863 | 2680 | 0.0024 | - |
| 4.4190 | 2700 | 0.0054 | - |
| 4.4517 | 2720 | 0.0035 | - |
| 4.4845 | 2740 | 0.0028 | - |
| 4.5172 | 2760 | 0.0042 | - |
| 4.5499 | 2780 | 0.0025 | - |
| 4.5827 | 2800 | 0.0027 | - |
| 4.6154 | 2820 | 0.0039 | - |
| 4.6481 | 2840 | 0.0046 | - |
| 4.6809 | 2860 | 0.0036 | - |
| 4.7136 | 2880 | 0.004 | - |
| 4.7463 | 2900 | 0.0031 | - |
| 4.7791 | 2920 | 0.0024 | - |
| 4.8118 | 2940 | 0.0036 | - |
| 4.8445 | 2960 | 0.0046 | - |
| 4.8773 | 2980 | 0.0025 | - |
| 4.9100 | 3000 | 0.0056 | - |
| 4.9427 | 3020 | 0.0031 | - |
| 4.9755 | 3040 | 0.0024 | - |
| 5.0 | 3055 | - | 0.1357 |
| 0.0016 | 1 | 0.008 | - |
| 0.0327 | 20 | 0.004 | - |
| 0.0655 | 40 | 0.0028 | - |
| 0.0982 | 60 | 0.0042 | - |
| 0.1309 | 80 | 0.0024 | - |
| 0.1637 | 100 | 0.0039 | - |
| 0.1964 | 120 | 0.0028 | - |
| 0.2291 | 140 | 0.0053 | - |
| 0.2619 | 160 | 0.0027 | - |
| 0.2946 | 180 | 0.0046 | - |
| 0.3273 | 200 | 0.0031 | - |
| 0.3601 | 220 | 0.0031 | - |
| 0.3928 | 240 | 0.0045 | - |
| 0.4255 | 260 | 0.0028 | - |
| 0.4583 | 280 | 0.0045 | - |
| 0.4910 | 300 | 0.0034 | - |
| 0.5237 | 320 | 0.0019 | - |
| 0.5565 | 340 | 0.0008 | - |
| 0.5892 | 360 | 0.0035 | - |
| 0.6219 | 380 | 0.0033 | - |
| 0.6547 | 400 | 0.0026 | - |
| 0.6874 | 420 | 0.0027 | - |
| 0.7201 | 440 | 0.0034 | - |
| 0.7529 | 460 | 0.0033 | - |
| 0.7856 | 480 | 0.0019 | - |
| 0.8183 | 500 | 0.0036 | - |
| 0.8511 | 520 | 0.0023 | - |
| 0.8838 | 540 | 0.0026 | - |
| 0.9165 | 560 | 0.0033 | - |
| 0.9493 | 580 | 0.0028 | - |
| 0.9820 | 600 | 0.004 | - |
| 1.0 | 611 | - | 0.1421 |
| 1.0147 | 620 | 0.003 | - |
| 1.0475 | 640 | 0.0023 | - |
| 1.0802 | 660 | 0.0019 | - |
| 1.1129 | 680 | 0.0026 | - |
| 1.1457 | 700 | 0.0018 | - |
| 1.1784 | 720 | 0.0018 | - |
| 1.2111 | 740 | 0.0007 | - |
| 1.2439 | 760 | 0.0025 | - |
| 1.2766 | 780 | 0.0021 | - |
| 1.3093 | 800 | 0.0011 | - |
| 1.3421 | 820 | 0.0029 | - |
| 1.3748 | 840 | 0.0023 | - |
| 1.4075 | 860 | 0.0019 | - |
| 1.4403 | 880 | 0.0016 | - |
| 1.4730 | 900 | 0.0022 | - |
| 1.5057 | 920 | 0.0015 | - |
| 1.5385 | 940 | 0.0012 | - |
| 1.5712 | 960 | 0.0014 | - |
| 1.6039 | 980 | 0.0012 | - |
| 1.6367 | 1000 | 0.0019 | - |
| 1.6694 | 1020 | 0.0015 | - |
| 1.7021 | 1040 | 0.0015 | - |
| 1.7349 | 1060 | 0.0008 | - |
| 1.7676 | 1080 | 0.0004 | - |
| 1.8003 | 1100 | 0.0014 | - |
| 1.8331 | 1120 | 0.0015 | - |
| 1.8658 | 1140 | 0.0011 | - |
| 1.8985 | 1160 | 0.0008 | - |
| 1.9313 | 1180 | 0.0021 | - |
| 1.9640 | 1200 | 0.0011 | - |
| 1.9967 | 1220 | 0.0023 | - |
| 2.0 | 1222 | - | 0.1374 |
| 0.0016 | 1 | 0.0009 | - |
| 0.0327 | 20 | 0.0022 | - |
| 0.0655 | 40 | 0.0015 | - |
| 0.0982 | 60 | 0.0022 | - |
| 0.1309 | 80 | 0.0007 | - |
| 0.1637 | 100 | 0.0015 | - |
| 0.1964 | 120 | 0.0011 | - |
| 0.2291 | 140 | 0.0019 | - |
| 0.2619 | 160 | 0.0011 | - |
| 0.2946 | 180 | 0.0014 | - |
| 0.3273 | 200 | 0.0011 | - |
| 0.3601 | 220 | 0.0007 | - |
| 0.3928 | 240 | 0.0014 | - |
| 0.4255 | 260 | 0.0011 | - |
| 0.4583 | 280 | 0.0024 | - |
| 0.4910 | 300 | 0.0011 | - |
| 0.5237 | 320 | 0.0006 | - |
| 0.5565 | 340 | 0.0003 | - |
| 0.5892 | 360 | 0.0003 | - |
| 0.6219 | 380 | 0.001 | - |
| 0.6547 | 400 | 0.0006 | - |
| 0.6874 | 420 | 0.0014 | - |
| 0.7201 | 440 | 0.001 | - |
| 0.7529 | 460 | 0.001 | - |
| 0.7856 | 480 | 0.0006 | - |
| 0.8183 | 500 | 0.0016 | - |
| 0.8511 | 520 | 0.0007 | - |
| 0.8838 | 540 | 0.0011 | - |
| 0.9165 | 560 | 0.0005 | - |
| 0.9493 | 580 | 0.0008 | - |
| 0.9820 | 600 | 0.0004 | - |
| 1.0 | 611 | - | 0.1352 |
| 1.0147 | 620 | 0.0003 | - |
| 1.0475 | 640 | 0.0003 | - |
| 1.0802 | 660 | 0.0003 | - |
| 1.1129 | 680 | 0.0003 | - |
| 1.1457 | 700 | 0.0003 | - |
| 1.1784 | 720 | 0.0003 | - |
| 1.2111 | 740 | 0.0002 | - |
| 1.2439 | 760 | 0.0003 | - |
| 1.2766 | 780 | 0.0003 | - |
| 1.3093 | 800 | 0.0002 | - |
| 1.3421 | 820 | 0.0003 | - |
| 1.3748 | 840 | 0.0003 | - |
| 1.4075 | 860 | 0.0003 | - |
| 1.4403 | 880 | 0.0003 | - |
| 1.4730 | 900 | 0.0003 | - |
| 1.5057 | 920 | 0.0003 | - |
| 1.5385 | 940 | 0.0003 | - |
| 1.5712 | 960 | 0.0003 | - |
| 1.6039 | 980 | 0.0003 | - |
| 1.6367 | 1000 | 0.0003 | - |
| 1.6694 | 1020 | 0.0002 | - |
| 1.7021 | 1040 | 0.0002 | - |
| 1.7349 | 1060 | 0.0002 | - |
| 1.7676 | 1080 | 0.0002 | - |
| 1.8003 | 1100 | 0.0002 | - |
| 1.8331 | 1120 | 0.0002 | - |
| 1.8658 | 1140 | 0.0002 | - |
| 1.8985 | 1160 | 0.0003 | - |
| 1.9313 | 1180 | 0.0003 | - |
| 1.9640 | 1200 | 0.0002 | - |
| 1.9967 | 1220 | 0.0003 | - |
| 2.0 | 1222 | - | 0.1392 |
### 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}
}
```